| Title: | Tools to Manage a Parametric Function that Describes Phenology and More |
|---|---|
| Description: | Functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons. |
| Authors: | Marc Girondot [aut, cre]
|
| Maintainer: | Marc Girondot <[email protected]> |
| License: | GPL-2 |
| Version: | 10.1 |
| Built: | 2024-10-23 03:14:41 UTC |
| Source: | https://github.com/cran/phenology |
Functions used to fit and test the phenology of species based on counts.
Note that only the most significant changes are reported in the NEWS.
The latest version of this package can always been installed using:
install.packages("https://hebergement.universite-paris-saclay.fr/marcgirondot/CRAN/HelpersMG.tar.gz", repos=NULL, type="source")
install.packages("https://hebergement.universite-paris-saclay.fr/marcgirondot/CRAN/phenology.tar.gz", repos=NULL, type="source")
Fit a parametric function that describes phenology
| Package: | phenology |
| Type: | Package |
| Version: | 10.1 build 1603 |
| Date: | 2024-08-23 |
| License: | GPL (>= 2) |
| LazyLoad: | yes |
Marc Girondot [email protected]
Girondot, M. 2010. Estimating density of animals during migratory waves: application to marine turtles at nesting site. Endangered Species Research, 12, 85-105.
Girondot M. and Rizzo A. 2015. Bayesian framework to integrate traditional ecological knowledge into ecological modeling: A case study. Journal of Ethnobiology, 35, 339-355. doi:10.2993/etbi-35-02-337-353.1
Girondot, M. 2010. Editorial: The zero counts. Marine Turtle Newsletter, 129, 5-6.
Girondot, M., 2017. Optimizing sampling design to infer marine turtles seasonal nest number for low-and high-density nesting beach using convolution of negative binomial distribution. Ecological Indicators 81, 83–89.
Rivalan, P., Godfrey, M.H., Prévot-Julliard, A.-C., Girondot, M., 2005. Maximum likelihood estimates of tag loss in leatherback sea turtles. Journal of Wildlife Management 69, 540-548.
Girondot, M., Rivalan, P., Wongsopawiro, R., Briane, J.-P., Hulin, V., Caut, S., Guirlet, E. & Godfrey, M. H. 2006. Phenology of marine turtle nesting revealed by a statistical model of the nesting season. BMC Ecology, 6, 11.
Delcroix, E., Bédel, S., Santelli, G., Girondot, M., 2013. Monitoring design for quantification of marine turtle nesting with limited human effort: a test case in the Guadeloupe Archipelago. Oryx 48, 95-105.
Briane J-P, Rivalan P, Girondot M (2007) The inverse problem applied to the Observed Clutch Frequency of Leatherbacks from Yalimapo beach, French Guiana. Chelonian Conservation and Biology 6:63-69
Fossette S, Kelle L, Girondot M, Goverse E, Hilterman ML, Verhage B, Thoisy B, de, Georges J-Y (2008) The world's largest leatherback rookeries: A review of conservation-oriented research in French Guiana/Suriname and Gabon. Journal of Experimental Marine Biology and Ecology 356:69-82
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # How many times this package has been download library(cranlogs) phenology <- cran_downloads("phenology", from = "2012-10-06", to = Sys.Date() - 1) sum(phenology$count) plot(phenology$date, phenology$count, type="l", bty="n") ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # How many times this package has been download library(cranlogs) phenology <- cran_downloads("phenology", from = "2012-10-06", to = Sys.Date() - 1) sum(phenology$count) plot(phenology$date, phenology$count, type="l", bty="n") ## End(Not run)
The function "adapt_parameters" extracts the set of parameters to be used with a subset of data. All the uncessary parameters are removed. It can be used when a set of beaches are fitted first and after only one of these beaches is fitted again.
adapt_parameters( data = stop("Datasets is mandatory for this function"), parameters = stop("Set of parameters is mandatory for this function") )adapt_parameters( data = stop("Datasets is mandatory for this function"), parameters = stop("Set of parameters is mandatory for this function") )
data |
A dataset of counts |
parameters |
A set of parameters |
adapt_parameters get the fitted parameters from a result object.
Return the set of parameters
Marc Girondot [email protected]
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Add unnecessary parameters to parg parg <- c(parg, Max_dummybeach=2, Peak_dummybeach=123) # Extract the fitted parameters parg1<-adapt_parameters(data=data_Gratiot, parameters=parg)library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Add unnecessary parameters to parg parg <- c(parg, Max_dummybeach=2, Peak_dummybeach=123) # Extract the fitted parameters parg1<-adapt_parameters(data=data_Gratiot, parameters=parg)
To create a new dataset, the syntaxe is :
data <- add_phenology(add=newdata, name="Site", reference=as.Date('2001-12-31'),
format='\
To add a dataset to a previous one, the syntax is :
data <- add_phenology(previous=previousdata, add=newdata, name='Site',
reference=as.Date('2001-01-01'), format="\
The dataset to be added must include 2 or 3 columns.
The colname.Date included the dates in the format specified by
the parameter format. If the number of nests is known
for an exact date, then only one date must be indicated.
If the number of nests is known for a range of date, the
first and last dates must be separated by a sep.dates character.
For example: 1/2/2000-10/2/2000
Note that date in the colname.Date column can be already formated and in this case
the parameter format is ignored.
The colname.Number includes the number of nests observed for
this date or this range of dates.
The colname.Rookery is optional and includes the name of the rookeries.
If only two columns are indicated, the name can be indicated as
a parameter of the function with the parameter name. If no name is indicated,
the default name Site will be used, but take care, only one
rookery of this name can be used.
Several rookeries can be included in the same file but in this case
the rookery name is obligatory at the colname.Rookery column.
The model cannot be fitted if a timeseries has no observation because the trivial
solution is of course with max=0. The solution is to include a fake false observation at the closest
position of the peak, and then the estimated number of nests/tracks will be the estimated number - 1.
If include0 is TRUE, then the series with no observation are included and one observation is added
at the monitored date the closest of datepeakfor0.
The normal way to manage such a situation is as followed:
1- Format data with include0 being FALSE
2- Fit parameters using fdf <- fit_phenology()
3- Format data with include0 being TRUE and datepeakfor0=fdf$par["Peak"]
4- Fix previsouly fitted parameters using pfixed <- fdf$par
5- Generate new set of parameters with par_init(data, fixed.parameters=pfixed)
6- Run again fit_phenology()
Some problems that can occur:
If a name is defined as a third column of a data.frame and a name is
defined also with name parameter, the third column has priority.
Two different timeseries MUST have different name and characters _ and
space are forbiden in timeseries names. They are automatically changed if they are present.
add_phenology( add = stop("New data must be given."), name = "Site", reference = NULL, month_ref = NULL, sep.dates = "-", end.season.date = NULL, colname.Date = 1, colname.Number = 2, colname.Rookery = "Site", colname.CountTypes = NULL, CountTypes.default = "exact", colname.A = NULL, A.default = NA, colname.S = NULL, S.default = NA, colname.ZeroCounts = NULL, ZeroCounts.default = TRUE, format = "%d/%m/%Y", previous = NULL, include0 = FALSE, datepeakfor0 = NULL, expandRange0Observation = TRUE, check.overlapping.dates = TRUE, silent = FALSE )add_phenology( add = stop("New data must be given."), name = "Site", reference = NULL, month_ref = NULL, sep.dates = "-", end.season.date = NULL, colname.Date = 1, colname.Number = 2, colname.Rookery = "Site", colname.CountTypes = NULL, CountTypes.default = "exact", colname.A = NULL, A.default = NA, colname.S = NULL, S.default = NA, colname.ZeroCounts = NULL, ZeroCounts.default = TRUE, format = "%d/%m/%Y", previous = NULL, include0 = FALSE, datepeakfor0 = NULL, expandRange0Observation = TRUE, check.overlapping.dates = TRUE, silent = FALSE )
add |
The data to be added. It can be a set of several entities that uses the same reference and date format. |
name |
The name of the monitored site. |
reference |
as.Date('2001-12-31') The date used as day 0 in the ordinal date model. |
month_ref |
If no reference date is given, use this month as a reference. |
sep.dates |
Separator used to separate dates when incertitude is included. |
end.season.date |
The date corresponding to the end of nesting season. |
colname.Date |
Name or number of column with dates. |
colname.Number |
Name or number of column with numbers. |
colname.Rookery |
Name or number of column with rookery names. |
colname.CountTypes |
Model of count type. It can be "exact" (default), "minimum" or a number to indicate the maximum possible. |
CountTypes.default |
The default of CountTypes if colname.CountTypes is not provided. |
colname.A |
The A parameter of the detection model |
A.default |
Default A value. |
colname.S |
The S parameter of the detection model |
S.default |
Default S value. |
colname.ZeroCounts |
The name of the column to indicate whether zero counts are included (TRUE is default). |
ZeroCounts.default |
The default for ZeroCounts. |
format |
The format of the dates. |
previous |
Previous data formated with add_phenology or NULL [default] if no previous data exist. |
include0 |
Does timeseries with only 0 should be included? |
datepeakfor0 |
If series with no observation are included, where add a 1 value in ordinal date (see description) |
expandRange0Observation |
If TRUE, the range of date with 0 observations are expanded into individual dates |
check.overlapping.dates |
If TRUE, will check for date overlapping |
silent |
Does information about added timeseries is shown |
add_phenology creates a new dataset.
Return a list of formated data that can be used ith fit_phenology()
Marc Girondot [email protected]
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) ############################################# # Example of use of include0 and datepeakfor0 ############################################# # Let create a times series with only 0 data0 <- data.frame(Date=c("11/3/2015", "12/3/2015", "13/3/2015-18/3/2015", "25/3/2015"), Number=c(0, 0, 0, 0), Beach=rep("Site", 4), stringsAsFactors=FALSE) # Here I don't include beach with no observation: error message try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=FALSE) # Here I include timeseries with no observation try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=TRUE, datepeakfor0=100) try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=TRUE, datepeakfor0=73) try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=TRUE, datepeakfor0=70) # It can be done in two steps try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=TRUE) try2 <- add_phenology(previous=try1, include0=TRUE, datepeakfor0=100) # Here I include the series without observation try1 <- add_phenology(add=data0, format="%d/%m/%Y", month_ref=1, include0=TRUE, expandRange0Observation=TRUE) ############################################################################## # Example of A and S parameters to say that only half of a beach was monitored ############################################################################## refdate <- as.Date("2001-01-01") data_Gratiot <- add_phenology(Gratiot, name="Complete1", reference=refdate, format="%d/%m/%Y") S.default = 10 # Let Complete1 be of length 2 and Complete0.5 be of length 1 length.Complete1 <- 2 length.Complete0.5 <- 1 A.default = log(1/(length.Complete0.5/length.Complete1)-1)/(-S.default*4) # For day 0, the detection probability is 1/(1+exp(-(4*S.default)*(A.default-0))) data_Gratiot <- add_phenology(previous=data_Gratiot, add=Gratiot, name="Complete0.5", A.default = A.default, S.default = S.default, reference=refdate, format="%d/%m/%Y") parg <- c(par_init(data_Gratiot, fixed.parameters=c(Min=0, Flat=0)), PMin=0.1) result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=c(Flat=0)) result_Gratiot$par # it shows that Max_Complete1 is half of Max_Complete0.5; all is ok summary(result_Gratiot)$synthesis ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) ############################################# # Example of use of include0 and datepeakfor0 ############################################# # Let create a times series with only 0 data0 <- data.frame(Date=c("11/3/2015", "12/3/2015", "13/3/2015-18/3/2015", "25/3/2015"), Number=c(0, 0, 0, 0), Beach=rep("Site", 4), stringsAsFactors=FALSE) # Here I don't include beach with no observation: error message try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=FALSE) # Here I include timeseries with no observation try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=TRUE, datepeakfor0=100) try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=TRUE, datepeakfor0=73) try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=TRUE, datepeakfor0=70) # It can be done in two steps try1 <- add_phenology(data0, format="%d/%m/%Y", month_ref=1, include0=TRUE) try2 <- add_phenology(previous=try1, include0=TRUE, datepeakfor0=100) # Here I include the series without observation try1 <- add_phenology(add=data0, format="%d/%m/%Y", month_ref=1, include0=TRUE, expandRange0Observation=TRUE) ############################################################################## # Example of A and S parameters to say that only half of a beach was monitored ############################################################################## refdate <- as.Date("2001-01-01") data_Gratiot <- add_phenology(Gratiot, name="Complete1", reference=refdate, format="%d/%m/%Y") S.default = 10 # Let Complete1 be of length 2 and Complete0.5 be of length 1 length.Complete1 <- 2 length.Complete0.5 <- 1 A.default = log(1/(length.Complete0.5/length.Complete1)-1)/(-S.default*4) # For day 0, the detection probability is 1/(1+exp(-(4*S.default)*(A.default-0))) data_Gratiot <- add_phenology(previous=data_Gratiot, add=Gratiot, name="Complete0.5", A.default = A.default, S.default = S.default, reference=refdate, format="%d/%m/%Y") parg <- c(par_init(data_Gratiot, fixed.parameters=c(Min=0, Flat=0)), PMin=0.1) result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=c(Flat=0)) result_Gratiot$par # it shows that Max_Complete1 is half of Max_Complete0.5; all is ok summary(result_Gratiot)$synthesis ## End(Not run)
This function is used to add standard error for a fixed parameter.
add_SE(fixed.parameters = NULL, parameters = NULL, SE = NULL)add_SE(fixed.parameters = NULL, parameters = NULL, SE = NULL)
fixed.parameters |
Set of fixed parameters |
parameters |
Set of current parameters |
SE |
Standard error value to be added |
add_SE adds standard error for a fixed parameter.
The parameters set with the new SE value
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Generate a set of fixed parameter: Flat and Min pfixed<-c(Flat=0, Min=0) # Add SE for the Flat parameter pfixed<-add_SE(fixed.parameters=pfixed, parameters="Flat", SE=5)library(phenology) # Generate a set of fixed parameter: Flat and Min pfixed<-c(Flat=0, Min=0) # Add SE for the Flat parameter pfixed<-add_SE(fixed.parameters=pfixed, parameters="Flat", SE=5)
This function is used to test several combinations of fit at a time.
AutoFitPhenology( data = stop("A dataset must be provided"), progressbar = TRUE, ... )AutoFitPhenology( data = stop("A dataset must be provided"), progressbar = TRUE, ... )
data |
Dataset generated with add_phenology() |
progressbar |
If FALSE, do not show the progress bar |
... |
Parameters for fit_phenology() |
AutoFitPhenology runs fit for phenology and tests several combinations
A list with 12 elements corresponding to the 12 tested models
Marc Girondot
Other Phenology model:
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Run the optimisation result_Gratiot_Auto <- AutoFitPhenology(data=data_Gratiot) result_Gratiot_Auto <- AutoFitPhenology(data=data_Gratiot, control=list(trace=0, REPORT=100, maxit=500)) ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Run the optimisation result_Gratiot_Auto <- AutoFitPhenology(data=data_Gratiot) result_Gratiot_Auto <- AutoFitPhenology(data=data_Gratiot, control=list(trace=0, REPORT=100, maxit=500)) ## End(Not run)
Model of remigration interval
Bayesian.remigration( parameters = stop("Priors must be supplied"), data = stop("data must be supplied"), kl = NULL, n.iter = 1e+05, n.chains = 1, n.adapt = 10000, thin = 1, trace = 10, adaptive = TRUE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )Bayesian.remigration( parameters = stop("Priors must be supplied"), data = stop("data must be supplied"), kl = NULL, n.iter = 1e+05, n.chains = 1, n.adapt = 10000, thin = 1, trace = 10, adaptive = TRUE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )
parameters |
Priors for Bayesian MCMC |
data |
Data to be fitted |
kl |
Maximum number of years for remigration intervals. |
n.iter |
Number of iterations for MCMC |
n.chains |
Number of replicates |
n.adapt |
Number of iterations before to store outputs |
thin |
Number of iterations between each stored output |
trace |
Or FALSE or period to show progress |
adaptive |
Should an adaptive process for SDProp be used |
adaptive.lag |
Lag to analyze the SDProp value in an adaptive context |
adaptive.fun |
Function used to change the SDProp |
intermediate |
Period for saving intermediate result, NULL for no save |
filename |
If intermediate is not NULL, save intermediate result in this file |
previous |
Previous result to be continued. Can be the filename in which intermediate results are saved. |
Bayesian.remigration fits a remigration interval using Bayesian MCMC
Return a posterior remigration interval.
Marc Girondot [email protected]
Other Model of Remigration Interval:
LnRI_norm(),
RI(),
plot.Remigration()
## Not run: library(phenology) # Example # Each year a fraction of 0.9 is surviving s <- c(s=0.9) # Probability of tag retention; 0.8 t <- c(t=0.8) # Time-conditional return probability - This is the true remigration rate r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) # Capture probability p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # Number of observations for 400 tagged females after 1, 2, 3, 4, and 5 years OBS <- c(400, 10, 120, 40, 20, 10) kl_s <- length(s) kl_t <- length(t) kl_r <- length(r) kl_p <- length(p) pMCMC <- data.frame(Density=c("newdbeta", "newdbeta", rep("dunif", kl_r), rep("newdbeta", kl_p), "dunif"), Prior1=c(s, t, rep(0, kl_r), rep(0.2, kl_p), 0), Prior2=c(0.02, 0.02, rep(1, kl_r), rep(0.08, kl_p), 10), SDProp=c(0.05, 0.05, rep(0.05, kl_r), rep(0.05, kl_p), 0.05), Min=c(0, 0, rep(0, kl_r), rep(0, kl_p), 0), Max=c(1, 1, rep(1, kl_r), rep(1, kl_p), 10), Init=c(s, t, r, p, 1), stringsAsFactors = FALSE, row.names=c("s", "t", names(r), names(p), "sd") ) rMCMC <- Bayesian.remigration(parameters = pMCMC, n.iter = 1000000, n.adapt = 300000, trace=10000, data=OBS) plot(rMCMC) ## End(Not run)## Not run: library(phenology) # Example # Each year a fraction of 0.9 is surviving s <- c(s=0.9) # Probability of tag retention; 0.8 t <- c(t=0.8) # Time-conditional return probability - This is the true remigration rate r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) # Capture probability p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # Number of observations for 400 tagged females after 1, 2, 3, 4, and 5 years OBS <- c(400, 10, 120, 40, 20, 10) kl_s <- length(s) kl_t <- length(t) kl_r <- length(r) kl_p <- length(p) pMCMC <- data.frame(Density=c("newdbeta", "newdbeta", rep("dunif", kl_r), rep("newdbeta", kl_p), "dunif"), Prior1=c(s, t, rep(0, kl_r), rep(0.2, kl_p), 0), Prior2=c(0.02, 0.02, rep(1, kl_r), rep(0.08, kl_p), 10), SDProp=c(0.05, 0.05, rep(0.05, kl_r), rep(0.05, kl_p), 0.05), Min=c(0, 0, rep(0, kl_r), rep(0, kl_p), 0), Max=c(1, 1, rep(1, kl_r), rep(1, kl_p), 10), Init=c(s, t, r, p, 1), stringsAsFactors = FALSE, row.names=c("s", "t", names(r), names(p), "sd") ) rMCMC <- Bayesian.remigration(parameters = pMCMC, n.iter = 1000000, n.adapt = 300000, trace=10000, data=OBS) plot(rMCMC) ## End(Not run)
This function is used to transform a set of parameters that uses Begin, Peak and End to a set of parameters that uses LengthB, Peak and LengthE.
BE_to_LBLE(parameters = NULL, help = FALSE)BE_to_LBLE(parameters = NULL, help = FALSE)
parameters |
Set of current parameters |
help |
If TRUE, an help is displayed |
BE_to_LBLE transforms a set of parameters from Begin End format to LengthB LengthE.
Return the set of modified parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Change the parameters to Begin End format parg1<-LBLE_to_BE(parameters=parg) # And change back to LengthB LengthE. parg2<-BE_to_LBLE(parameters=parg1) ## End(Not run)## Not run: # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Change the parameters to Begin End format parg1<-LBLE_to_BE(parameters=parg) # And change back to LengthB LengthE. parg2<-BE_to_LBLE(parameters=parg1) ## End(Not run)
The data must be a data.frame with the first column being years
and two columns for each beach: the average and the se for the estimate.
The correspondence between mean, se and density for each rookery are given in the RMU.names data.frame.
This data.frame must have a column named mean, another named se and a third named density. If
no sd column exists, no sd will be considered for the series and is no density column exists, it
will be considered as being "dnorm".
In the result list, the mean proportions for each rookeries are in $proportions.
The names of beach columns must not begin by T_, SD_, a0_, a1_ or a2_ and cannot be r.
A RMU is the acronyme for Regional Managment Unit. See:
Wallace, B.P., DiMatteo, A.D., Hurley, B.J., Finkbeiner, E.M., Bolten, A.B.,
Chaloupka, M.Y., Hutchinson, B.J., Abreu-Grobois, F.A., Amorocho, D., Bjorndal, K.A.,
Bourjea, J., Bowen, B.W., Dueñas, R.B., Casale, P., Choudhury, B.C., Costa, A.,
Dutton, P.H., Fallabrino, A., Girard, A., Girondot, M., Godfrey, M.H., Hamann, M.,
López-Mendilaharsu, M., Marcovaldi, M.A., Mortimer, J.A., Musick, J.A., Nel, R.,
Seminoff, J.A., Troëng, S., Witherington, B., Mast, R.B., 2010. Regional
management units for marine turtles: a novel framework for prioritizing
conservation and research across multiple scales. PLoS One 5, e15465.
Variance for each value is additive based on both the observed SE (in the RMU.data
object) and a constant value dependent on the rookery when model.SD is equal to
"Rookery-constant". The value is a global constant when model.SD is "global-constant".
The value is proportional to the observed number of nests when model.SD is
"global-proportional" with aSD_*observed+SD_ with aSD_ and SD_ being fitted
values. This value is fixed to zero when model.SD is "Zero".
If replicate.CI is 0, no CI is estimated, and only point estimation is returned.
CI.RMU( result = stop("A result obtained from fitRMU is necessary"), resultMCMC = NULL, chain = 1, replicate.CI = 10000, regularThin = TRUE, probs = c(0.025, 0.5, 0.975), silent = FALSE )CI.RMU( result = stop("A result obtained from fitRMU is necessary"), resultMCMC = NULL, chain = 1, replicate.CI = 10000, regularThin = TRUE, probs = c(0.025, 0.5, 0.975), silent = FALSE )
result |
A result of fitRMu() |
resultMCMC |
A resuts of fitRMU_MHmcmc() |
chain |
Number of MCMC chain to be used |
replicate.CI |
Number of replicates |
regularThin |
If TRUE, use regular thin for MCMC |
probs |
The probabilities to return for quantiles |
silent |
If TRUE does not display anything |
CI.RMU calculates the confidence interval of the results of fitRMU()
Return a list with Total, Proportions, and Numbers
Marc Girondot
Other Fill gaps in RMU:
fitRMU(),
fitRMU_MHmcmc(),
fitRMU_MHmcmc_p(),
logLik.fitRMU(),
plot.fitRMU()
## Not run: library("phenology") RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_Irakumpapy.French.Guiana"), stringsAsFactors = FALSE) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), density_Yalimapo.French.Guiana=rep("dnorm", 11), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), density_Galibi.Suriname=rep("dnorm", 11), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), density_Irakumpapy.French.Guiana=rep("dnorm", 11), stringsAsFactors = FALSE ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") out.CI.Cst <- CI.RMU(result=cst) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", control=list(trace=1, REPORT=100, maxit=500, parscale = c(3000, -0.2, 0.6))) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", method=c("Nelder-Mead","BFGS"), control = list(trace = 0, REPORT = 100, maxit = 500, parscale = c(3000, -0.2, 0.6))) expo <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Exponential", model.SD="Zero", method=c("Nelder-Mead","BFGS"), control = list(trace = 0, REPORT = 100, maxit = 500, parscale = c(6000, -0.05, -0.25, 0.6))) YS <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), model.SD="Zero") YS1 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), model.SD="Zero", model.rookeries="First-order") YS1_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="First-order", parameters=YS1$par, method=c("Nelder-Mead","BFGS")) YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="Second-order", parameters=YS1$par, method=c("Nelder-Mead","BFGS")) YS2_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="Second-order", parameters=YS1_cst$par, method=c("Nelder-Mead","BFGS")) compare_AIC(Constant=cst, Exponential=expo, YearSpecific=YS) compare_AIC(YearSpecific_ProportionsFirstOrder_Zero=YS1, YearSpecific_ProportionsFirstOrder_Constant=YS1_cst) compare_AIC(YearSpecific_ProportionsConstant=YS, YearSpecific_ProportionsFirstOrder=YS1, YearSpecific_ProportionsSecondOrder=YS2) compare_AIC(YearSpecific_ProportionsFirstOrder=YS1_cst, YearSpecific_ProportionsSecondOrder=YS2_cst) plot(cst, main="Use of different beaches along the time", what="total") plot(expo, main="Use of different beaches along the time", what="total") plot(YS2_cst, main="Use of different beaches along the time", what="total") plot(YS1, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time", what="numbers") # Gamma distribution should be used for MCMC outputs RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), density_Yalimapo.French.Guiana=rep("dgamma", 11), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), density_Galibi.Suriname=rep("dgamma", 11), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), density_Irakumpapy.French.Guiana=rep("dgamma", 11) ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") ## End(Not run)## Not run: library("phenology") RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_Irakumpapy.French.Guiana"), stringsAsFactors = FALSE) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), density_Yalimapo.French.Guiana=rep("dnorm", 11), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), density_Galibi.Suriname=rep("dnorm", 11), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), density_Irakumpapy.French.Guiana=rep("dnorm", 11), stringsAsFactors = FALSE ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") out.CI.Cst <- CI.RMU(result=cst) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", control=list(trace=1, REPORT=100, maxit=500, parscale = c(3000, -0.2, 0.6))) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", method=c("Nelder-Mead","BFGS"), control = list(trace = 0, REPORT = 100, maxit = 500, parscale = c(3000, -0.2, 0.6))) expo <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Exponential", model.SD="Zero", method=c("Nelder-Mead","BFGS"), control = list(trace = 0, REPORT = 100, maxit = 500, parscale = c(6000, -0.05, -0.25, 0.6))) YS <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), model.SD="Zero") YS1 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), model.SD="Zero", model.rookeries="First-order") YS1_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="First-order", parameters=YS1$par, method=c("Nelder-Mead","BFGS")) YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="Second-order", parameters=YS1$par, method=c("Nelder-Mead","BFGS")) YS2_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="Second-order", parameters=YS1_cst$par, method=c("Nelder-Mead","BFGS")) compare_AIC(Constant=cst, Exponential=expo, YearSpecific=YS) compare_AIC(YearSpecific_ProportionsFirstOrder_Zero=YS1, YearSpecific_ProportionsFirstOrder_Constant=YS1_cst) compare_AIC(YearSpecific_ProportionsConstant=YS, YearSpecific_ProportionsFirstOrder=YS1, YearSpecific_ProportionsSecondOrder=YS2) compare_AIC(YearSpecific_ProportionsFirstOrder=YS1_cst, YearSpecific_ProportionsSecondOrder=YS2_cst) plot(cst, main="Use of different beaches along the time", what="total") plot(expo, main="Use of different beaches along the time", what="total") plot(YS2_cst, main="Use of different beaches along the time", what="total") plot(YS1, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time", what="numbers") # Gamma distribution should be used for MCMC outputs RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), density_Yalimapo.French.Guiana=rep("dgamma", 11), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), density_Galibi.Suriname=rep("dgamma", 11), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), density_Irakumpapy.French.Guiana=rep("dgamma", 11) ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") ## End(Not run)
This function calculates a table of probabilities of ECF and OCF.
If p is lower or higher than 1E-100 or 1-1E-100, it is changed to 1E-100 and 1-(1E-100) respectively.
Names for p vector elements should be p, or px (with x=1:categories), or px.period.
If mu_season and sd_season are equal to NA, the model is not temporalized.
If mu_season and sd_season are not NA, the model returns a 3D-table OCFECF.
ECFOCF_f( mu, sd = NA, p, MaxNests = 15, mu_season = NA, sd_season = NA, MeanDaysBetween2Nests = 9.8, length_season = floor(365/MeanDaysBetween2Nests) + 1, parallel = TRUE )ECFOCF_f( mu, sd = NA, p, MaxNests = 15, mu_season = NA, sd_season = NA, MeanDaysBetween2Nests = 9.8, length_season = floor(365/MeanDaysBetween2Nests) + 1, parallel = TRUE )
mu |
The average of lognormal for clutch frequency. |
sd |
The sd parameter of lognormal for clutch frequency. |
p |
The capture probability for an individual nesting event. As a probability. |
MaxNests |
Maximum number of nests by a female. |
mu_season |
The average of ordinal day for beginning of nesting season. |
sd_season |
The sd parameter of lognormal for ordinal day for beginning of nesting season. |
MeanDaysBetween2Nests |
Average number of days between two nests. |
length_season |
The total length of season based on groups of interclutch intervals. |
parallel |
If TRUE parallel computing is used. |
ECFOCF_f calculate a table of probabilities of ECF and OCF.
Return a matrix of class TableECFOCF.
Marc Girondot [email protected]
Other Model of Clutch Frequency:
ECFOCF_full(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
generateCF(),
lnLCF(),
logLik.ECFOCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library(phenology) # Example modelECFOCF <- ECFOCF_f(mu=5.58013243236187, sd=1.225581130238, p=invlogit(1.3578137414575), MaxNests=15) plot(modelECFOCF) modelECFOCF <- ECFOCF_f(mu=5.58013243236187, sd=1.225581130238, mu_season=12, sd_season=2, p=c(p1=invlogit(1.3578137414575)), MaxNests=15, MeanDaysBetween2Nests=9.8, length_season=floor(365/9.8)+1) plot(modelECFOCF, period=2) ## End(Not run)## Not run: library(phenology) # Example modelECFOCF <- ECFOCF_f(mu=5.58013243236187, sd=1.225581130238, p=invlogit(1.3578137414575), MaxNests=15) plot(modelECFOCF) modelECFOCF <- ECFOCF_f(mu=5.58013243236187, sd=1.225581130238, mu_season=12, sd_season=2, p=c(p1=invlogit(1.3578137414575)), MaxNests=15, MeanDaysBetween2Nests=9.8, length_season=floor(365/9.8)+1) plot(modelECFOCF, period=2) ## End(Not run)
This function calculates a table of probabilities of ECF and OCF.
If p is lower or higher than 1E-100 or 1-1E-100, it is changed to 1E-100 and 1-(1E-100) respectively.
Names for p vector elements should be p, or px (with x=1:categories), or px.period.
If mu_season and sd_season are equal to NA, the model is not temporalized.
If mu_season and sd_season are not NA, the model returns a 3D-table OCFECF.
ECFOCF_full( mu, sd = NA, p, a = NULL, MaxNests = 15, mu_season = NA, sd_season = NA, OTN = c(OTN1 = 1), MeanDaysBetween2Nests = 9.8, length_season = NA, parallel = TRUE )ECFOCF_full( mu, sd = NA, p, a = NULL, MaxNests = 15, mu_season = NA, sd_season = NA, OTN = c(OTN1 = 1), MeanDaysBetween2Nests = 9.8, length_season = NA, parallel = TRUE )
mu |
The average of lognormal for clutch frequency. |
sd |
The sd parameter of lognormal for clutch frequency. |
p |
The capture probability for an individual nesting event. As a logit |
a |
The common capture probability. As a probability |
MaxNests |
Maximum number of nests by a female. |
mu_season |
The average of ordinal day for beginning of nesting season. |
sd_season |
The sd parameter of lognormal for ordinal day for beginning of nesting season. |
OTN |
The relative probability of categories |
MeanDaysBetween2Nests |
Average number of days between two nests. |
length_season |
The total length of season based on groups of interclutch intervals. |
parallel |
If TRUE parallel computing is used. |
ECFOCF_full calculate a table of probabilities of ECF and OCF.
Return a matrix of class TableECFOCF.
Marc Girondot [email protected]
Other Model of Clutch Frequency:
ECFOCF_f(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
generateCF(),
lnLCF(),
logLik.ECFOCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library(phenology) # Example modelECFOCF <- ECFOCF_full(mu=c(mu1=5.58013243236187), sd=c(sd1=1.225581130238), mu_season=c(mu_season1=12), sd_season=c(sd_season1=2), p=c(p1=logit(0.7954041)), a=c(a1=1), MaxNests=15, MeanDaysBetween2Nests=9.8, length_season=floor(365/9.8)+1) plot(modelECFOCF, period=12, max.scale=0.02) modelECFOCF <- ECFOCF_full(mu=c(mu1=5.58013243236187), sd=c(sd1=1.225581130238), a=c(a1=1), p=c(p1=invlogit(1.3578137414575)), MaxNests=15) plot(modelECFOCF) ## End(Not run)## Not run: library(phenology) # Example modelECFOCF <- ECFOCF_full(mu=c(mu1=5.58013243236187), sd=c(sd1=1.225581130238), mu_season=c(mu_season1=12), sd_season=c(sd_season1=2), p=c(p1=logit(0.7954041)), a=c(a1=1), MaxNests=15, MeanDaysBetween2Nests=9.8, length_season=floor(365/9.8)+1) plot(modelECFOCF, period=12, max.scale=0.02) modelECFOCF <- ECFOCF_full(mu=c(mu1=5.58013243236187), sd=c(sd1=1.225581130238), a=c(a1=1), p=c(p1=invlogit(1.3578137414575)), MaxNests=15) plot(modelECFOCF) ## End(Not run)
The idea of this function is to fit a regression exponential model using MCMC
because regression model using glm can produce biased outputs.
prior.default.distribution can be "dnorm", "dunif", or "dgamma". Note that if you propose
dgamma prior, it will use uniform prior for r because r can be negative.
The SD model is asd*Nt+bsd. The asd parameter represents multiplicative model and the
bsd parameter represents additive model. Both can be used simultaneously.
density can be dnorm or dnbinom_new (from HelpersMG package). dnbinom_new() is a
negative binomial with mean and sd parametrization.
ExponentialRegression( data = stop("A data.frame with values"), colname.time = "time", colname.number = "numbers", weights = NULL, fitted.parameters = c(N0 = NA, r = NA, asd = NA), fixed.parameters = NULL, n.iter = c(1e+05, 1e+05), prior.default.distribution = "dnorm", density = dnorm )ExponentialRegression( data = stop("A data.frame with values"), colname.time = "time", colname.number = "numbers", weights = NULL, fitted.parameters = c(N0 = NA, r = NA, asd = NA), fixed.parameters = NULL, n.iter = c(1e+05, 1e+05), prior.default.distribution = "dnorm", density = dnorm )
data |
A data.frame with a column time and a column number. |
colname.time |
Name of the column to be used as time index. |
colname.number |
Name of the column to be used as number index. |
weights |
an optional vector of ‘prior weights’ to be used in the fitting process. Should be NULL or a numeric vector. |
fitted.parameters |
A named vector with the parameters to be fitted |
fixed.parameters |
A named vector with the parameters to be fixed |
n.iter |
The number of MCMC iterations. |
prior.default.distribution |
The default prior distribution; see description. |
density |
by default is dnorm but can be dnbinom_new |
ExponentialRegression is used to fit additive, multiplicative or mixte exponential regression
Return a list with the results of exponential regression
Marc Girondot [email protected]
## Not run: library("phenology") t <- 1:100 N0 <- 100 r <- 0.05 y <- N0*exp(t*r) # Multiplicative model Nt <- rnorm(100, mean=y, sd=0.2*y) df <- data.frame(time=t, numbers=Nt) g <- ExponentialRegression(data=df, fitted.parameters=c(N0=NA, r=NA, asd=NA)) plot(g, parameters="r") as.parameters(g, index="median") # Note that if you propose gamma prior, it will use uniform prior for r # because r can be negative g <- ExponentialRegression(data=df, fitted.parameters=c(N0=NA, r=NA, asd=NA), prior.default.distribution="dgamma") plot(g, parameters="r") as.parameters(g, index="median") # Additive model Nt <- rnorm(100, mean=y, sd=5) df <- data.frame(time=t, numbers=Nt) g <- ExponentialRegression(data=df, fitted.parameters=c(N0=NA, r=NA, bsd=NA)) plot(g, parameters="r") as.parameters(g, index="median") # Mixt model Nt <- rnorm(100, mean=y, sd=0.2*y+5) df <- data.frame(time=t, numbers=Nt) g <- ExponentialRegression(data=df, fitted.parameters=c(N0=NA, r=NA, asd=NA, bsd=NA)) plot(g, parameters="r") as.parameters(g, index="median") # Example with 3 common ways to perform the regression t <- 1:100 N0 <- 100 r <- 0.05 y <- N0*exp(t*r) out_glm <- NULL out_mcmc <- NULL out_nls <- NULL for (i in 1:500) { print(i) set.seed(i) Nt <- rnorm(100, mean=y, sd=0.2*y) df <- data.frame(time=t, numbers=Nt) g0 <- glm(log(numbers) ~ time, data = df) out_glm <- c(out_glm, c(exp(coef(g0)[1]), coef(g0)[2])) g1 <- ExponentialRegression(data=df, n.iter=c(10000, 20000)) out_mcmc <- c(out_mcmc, as.parameters(g1, index="median")[1:2]) g2 <- nls(numbers ~ N0*exp(r*time), start = list(N0 = 100, r = 0.05), data = df) out_nls <- c(out_nls, coef(g2)) } # In conclusion the method proposed here has no biais as compare to glm and nls fits out_glm <- matrix(out_glm, ncol=2, byrow=TRUE) out_mcmc <- matrix(out_mcmc, ncol=2, byrow=TRUE) out_nls <- matrix(out_nls, ncol=2, byrow=TRUE) mean(out_glm[, 1]); mean(out_mcmc[, 1]); mean(out_nls[, 1]) sd(out_glm[, 1])/sqrt(nrow(out_glm)); sd(out_mcmc[, 1])/sqrt(nrow(out_mcmc)); sd(out_nls[, 1])/sqrt(nrow(out_nls)) mean(out_glm[, 2]); mean(out_mcmc[, 2]); mean(out_nls[, 2]) sd(out_glm[, 2])/sqrt(nrow(out_glm)); sd(out_mcmc[, 2])/sqrt(nrow(out_mcmc)); sd(out_nls[, 2])/sqrt(nrow(out_nls)) ## End(Not run)## Not run: library("phenology") t <- 1:100 N0 <- 100 r <- 0.05 y <- N0*exp(t*r) # Multiplicative model Nt <- rnorm(100, mean=y, sd=0.2*y) df <- data.frame(time=t, numbers=Nt) g <- ExponentialRegression(data=df, fitted.parameters=c(N0=NA, r=NA, asd=NA)) plot(g, parameters="r") as.parameters(g, index="median") # Note that if you propose gamma prior, it will use uniform prior for r # because r can be negative g <- ExponentialRegression(data=df, fitted.parameters=c(N0=NA, r=NA, asd=NA), prior.default.distribution="dgamma") plot(g, parameters="r") as.parameters(g, index="median") # Additive model Nt <- rnorm(100, mean=y, sd=5) df <- data.frame(time=t, numbers=Nt) g <- ExponentialRegression(data=df, fitted.parameters=c(N0=NA, r=NA, bsd=NA)) plot(g, parameters="r") as.parameters(g, index="median") # Mixt model Nt <- rnorm(100, mean=y, sd=0.2*y+5) df <- data.frame(time=t, numbers=Nt) g <- ExponentialRegression(data=df, fitted.parameters=c(N0=NA, r=NA, asd=NA, bsd=NA)) plot(g, parameters="r") as.parameters(g, index="median") # Example with 3 common ways to perform the regression t <- 1:100 N0 <- 100 r <- 0.05 y <- N0*exp(t*r) out_glm <- NULL out_mcmc <- NULL out_nls <- NULL for (i in 1:500) { print(i) set.seed(i) Nt <- rnorm(100, mean=y, sd=0.2*y) df <- data.frame(time=t, numbers=Nt) g0 <- glm(log(numbers) ~ time, data = df) out_glm <- c(out_glm, c(exp(coef(g0)[1]), coef(g0)[2])) g1 <- ExponentialRegression(data=df, n.iter=c(10000, 20000)) out_mcmc <- c(out_mcmc, as.parameters(g1, index="median")[1:2]) g2 <- nls(numbers ~ N0*exp(r*time), start = list(N0 = 100, r = 0.05), data = df) out_nls <- c(out_nls, coef(g2)) } # In conclusion the method proposed here has no biais as compare to glm and nls fits out_glm <- matrix(out_glm, ncol=2, byrow=TRUE) out_mcmc <- matrix(out_mcmc, ncol=2, byrow=TRUE) out_nls <- matrix(out_nls, ncol=2, byrow=TRUE) mean(out_glm[, 1]); mean(out_mcmc[, 1]); mean(out_nls[, 1]) sd(out_glm[, 1])/sqrt(nrow(out_glm)); sd(out_mcmc[, 1])/sqrt(nrow(out_mcmc)); sd(out_nls[, 1])/sqrt(nrow(out_nls)) mean(out_glm[, 2]); mean(out_mcmc[, 2]); mean(out_nls[, 2]) sd(out_glm[, 2])/sqrt(nrow(out_glm)); sd(out_mcmc[, 2])/sqrt(nrow(out_mcmc)); sd(out_nls[, 2])/sqrt(nrow(out_nls)) ## End(Not run)
The function "extract_result" permits to extract the set of parameters from a result object obtained after fit_phenology.
extract_result(result = NULL)extract_result(result = NULL)
result |
A result file |
extract_result get the fitted parameters from a result object.
Return the set of fitted parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) ## Not run: # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation # result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) ## End(Not run)library(phenology) ## Not run: # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation # result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) ## End(Not run)
Function of the package phenology to fit parameters to timeseries.
To fit data, the syntax is :
Result <- fit_phenology(data=dataset, fitted.parameters=par, fixed.parameters=pfixed, trace=1, hessian=TRUE)
or if no parameter is fixed :
Result <- fit_phenology(data=dataset, fitted.parameters=par)
Add trace=1 [default] to have information on the fit progression or trace=0 to hide information on the fit progression.
hessian = FALSE does not estimate Hessian matrix and SE of parameters.
If the parameter Theta is fixed to +Inf, a Poissonian model of daily nest distribution is implemented.
Special section about cofactors:
cofactors must be a data.frame with a column Date and a column for each cofactor
add.cofactors are the names of the column of parameter cofactors to use as a cofactor;
The model is then: parameter[add.cofactors] * cofactor[, add.cofactors]
If the name of the parameter is paste0(add.cofactors, "multi"), then the model is:
parameter[paste0(add.cofactors, "multi")] * cofactor[, add.cofactors] *
(number of nests without cofactor)
About parallel computing:
Set options mc.cores and forking to tell what sort of parallel computing
Example:
options(mc.cores = detectCores())
options(forking = FALSE)
fit_phenology( data = file.choose(), fitted.parameters = NULL, fixed.parameters = NULL, model_before = NULL, store.intermediate = FALSE, file.intermediate = "Intermediate.rda", hessian = FALSE, silent = FALSE, cofactors = NULL, add.cofactors = NULL, zero = 1e-09, lower = 0, upper = Inf, stop.fit = FALSE, method_Snbinom = "saddlepoint", control = list(trace = 1, REPORT = 1, maxit = 1000), method = c("Nelder-Mead", "L-BFGS-B") )fit_phenology( data = file.choose(), fitted.parameters = NULL, fixed.parameters = NULL, model_before = NULL, store.intermediate = FALSE, file.intermediate = "Intermediate.rda", hessian = FALSE, silent = FALSE, cofactors = NULL, add.cofactors = NULL, zero = 1e-09, lower = 0, upper = Inf, stop.fit = FALSE, method_Snbinom = "saddlepoint", control = list(trace = 1, REPORT = 1, maxit = 1000), method = c("Nelder-Mead", "L-BFGS-B") )
data |
A dataset generated by add_format |
fitted.parameters |
Set of parameters to be fitted |
fixed.parameters |
Set of fixed parameters |
model_before |
The change of parameters before to estimate daily counts. |
store.intermediate |
TRUE or FALSE to save the intermediates |
file.intermediate |
Name of the file where to save the intermediates as a list |
hessian |
If FALSE does not estimate se of parameters |
silent |
If TRUE does not show any message |
cofactors |
data.frame with a column Date and a column for each cofactor |
add.cofactors |
Names of the column of parameter cofactors to use as a cofactor |
zero |
If the theoretical nest number is under this value, this value will be used |
lower |
Lower bound for each parameter |
upper |
Upper bound for each parameter |
stop.fit |
If TRUE, will stop search for parameters even if not ML |
method_Snbinom |
Can be Furman, exact, or saddlepoint. |
control |
List for control parameters for optim |
method |
Method used by optim. Several can be setup. |
fit_phenology fits parameters to timeseries.
Return a list of with data and result
Marc Girondot [email protected]
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # or output <- summary(result_Gratiot) # With one sinusoid parg <- c('LengthB' = 95.187648986054285, 'Peak' = 173.86111755419921, 'LengthE' = 63.183281230994481, 'Max_Complete' = 33.052091519419136, 'MinB_Complete' = 0.21716081973776738, 'MinE_Complete' = 0.42444245288475702, 'Theta' = 3.9554976911657187, 'Alpha' = 0, 'Beta' = 0.21019683898423902, 'Delta' = 3.6798422076201724, result_Gratiot1 <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot1) # With two sinusoids parg <- c('LengthB' = 95.821859173220659, 'Peak' = 174.89756503758881, 'LengthE' = 60.954167489825352, 'Max_Complete' = 33.497846416498774, 'MinB_Complete' = 0.21331670808871037, 'MinE_Complete' = 0.43730327416828613, 'Theta' = 4.2569203480842814, 'Alpha1' = 0.15305562092653918, 'Beta1' = 0, 'Delta1' = 9.435730952200263, 'Phi1' = 15.7944494669335, 'Alpha' = 0, 'Beta' = 0.28212926001402688, 'Delta' = 18.651087311957518, 'Phi' = 9.7549929595313056) result_Gratiot2 <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot2) compare_AICc(no=result_Gratiot, one=result_Gratiot1, two=result_Gratiot2) # With parametrization based on Girondot 2010 parg <- c('Peak' = 173.52272236775076, 'Flat' = 0, 'LengthB' = 94.433284359804205, 'LengthE' = 64.288485646867329, 'Max_Complete' = 32.841568389778033, 'PMin' = 1.0368261725650889, 'Theta' = 3.5534818927979592) result_Gratiot_par1 <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # With new parametrization based on Omeyer et al. (2022): # Omeyer, L. C. M., McKinley, T. J., Bréheret, N., Bal, G., Balchin, G. P., # Bitsindou, A., Chauvet, E., Collins, T., Curran, B. K., Formia, A., Girard, A., # Girondot, M., Godley, B. J., Mavoungou, J.-G., Poli, L., Tilley, D., # VanLeeuwe, H. & Metcalfe, K. 2022. Missing data in sea turtle population # monitoring: a Bayesian statistical framework accounting for incomplete # sampling Front. Mar. Sci. (IF 3.661), 9, 817014. parg <- result_Gratiot_par1$par parg <- c(tp=unname(parg["Peak"]), tf=unname(parg["Flat"]), s1=unname(parg["LengthB"])/4.8, s2=unname(parg["LengthE"])/4.8, alpha=unname(parg["Max_Complete"]), Theta=0.66) result_Gratiot_par2 <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) compare_AICc(GirondotModel=result_Gratiot_par1, OmeyerModel=result_Gratiot_par2) plot(result_Gratiot_par1) plot(result_Gratiot_par2) # Use fit with co-factor # First extract tide information for that place td <- tide.info(year=2001, latitude=4.9167, longitude=-52.3333) # I keep only High tide level td2 <- td[td$Phase=="High Tide", ] # I get the date td3 <- cbind(td2, Date=as.Date(td2$DateTime.local)) td5 <- aggregate(x=td3[, c("Date", "DateTime.local", "Tide.meter")], by=list(Date=td3[, "Date"]), FUN=max)[, 2:4] with(td5, plot(DateTime.local, Tide.meter, type="l")) td6 <- td5[, c("Date", "Tide.meter")] parg <- par_init(data_Gratiot, fixed.parameters=NULL, add.cofactors="Tide.meter") likelihood_phenology(data=data_Gratiot, fitted.parameters = parg, cofactors=td6, add.cofactors="Tide.meter") result_Gratiot_CF <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL, cofactors=td6, add.cofactors="Tide.meter") compare_AIC(WithoutCF=result_Gratiot, WithCF=result_Gratiot_CF) plot(result_Gratiot_CF) # Example with two series fitted with different peaks but same Length of season Gratiot2 <- Gratiot Gratiot2[, 2] <- floor(Gratiot2[, 2]*runif(n=nrow(Gratiot2))) data_Gratiot <- add_phenology(Gratiot, name="Complete1", reference=as.Date("2001-01-01"), format="%d/%m/%Y") data_Gratiot <- add_phenology(Gratiot2, name="Complete2", reference=as.Date("2001-01-01"), format="%d/%m/%Y", previous=data_Gratiot) pfixed=c(Min=0) p <- par_init(data_Gratiot, fixed.parameters = pfixed) p <- c(p, Peak_Complete1=175, Peak_Complete2=175) p <- p[-4] p <- c(p, Length=90) p <- p[-(3:4)] result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=p, fixed.parameters=pfixed) # An example with bimodality g <- Gratiot g[30:60, 2] <- sample(10:20, 31, replace = TRUE) data_g <- add_phenology(g, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") parg <- c('Max.1_Complete' = 5.6344636692341856, 'MinB.1_Complete' = 0.15488810581002324, 'MinE.1_Complete' = 0.2, 'LengthB.1' = 22.366647176407636, 'Peak.1' = 47.902473939250036, 'LengthE.1' = 17.828495918533015, 'Max.2_Complete' = 33.053364083447434, 'MinE.2_Complete' = 0.42438173496989717, 'LengthB.2' = 96.651564706802702, 'Peak.2' = 175.3451874571835, 'LengthE.2' = 62.481968743789835, 'Theta' = 3.6423908093342572) pfixed <- c('MinB.2_Complete' = 0, 'Flat.1' = 0, 'Flat.2' = 0) result_g <- fit_phenology(data=data_g, fitted.parameters=parg, fixed.parameters=pfixed) plot(result_g) # Exemple with some minimum counts nb <- Gratiot[, 2] nbs <- sample(0:1, length(nb), replace=TRUE) nb <- ifelse(nb != 0, ifelse(nbs == 1, 1, nb), 0) nbc <- ifelse(nb != 0, ifelse(nbs == 1, "minimum", "exact"), "exact") Gratiot_minimal <- cbind(Gratiot, CountTypes=nbc) Gratiot_minimal[, 2] <- nb data_Gratiot_minimal <- add_phenology(add=Gratiot_minimal, colname.CountTypes = "CountTypes", month_ref=1) parg <- par_init(data_Gratiot_minimal, fixed.parameters=NULL) result_Gratiot_minimal <- fit_phenology(data=data_Gratiot_minimal, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot_minimal) summary(result_Gratiot_minimal) # Exemple with all zero counts being not recorded Gratiot_NoZeroCounts <- cbind(Gratiot[Gratiot[, 2] != 0, ], ZeroCounts=FALSE) data_Gratiot_NoZeroCounts <- add_phenology(add=Gratiot_NoZeroCounts, colname.ZeroCounts = "ZeroCounts", ZeroCounts.defaul=FALSE, month_ref=1) # or data_Gratiot_NoZeroCounts <- add_phenology(add=Gratiot[Gratiot[, 2] != 0, ], ZeroCounts.default=FALSE, month_ref=1) parg <- par_init(data_Gratiot_NoZeroCounts, fixed.parameters=NULL) result_Gratiot_NoZeroCounts <- fit_phenology(data=data_Gratiot_NoZeroCounts, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot_NoZeroCounts) summary(result_Gratiot_NoZeroCounts) # Exemple with data in range of date Gratiot_rangedate <- Gratiot Gratiot_rangedate[, 1] <- as.character(Gratiot_rangedate[, 1]) Gratiot_rangedate[148, 1] <- paste0(Gratiot_rangedate[148, 1], "-", Gratiot_rangedate[157, 1]) Gratiot_rangedate[148, 2] <- sum(Gratiot_rangedate[148:157, 2]) Gratiot_rangedate <- Gratiot_rangedate[-(149:157), ] data_Gratiot_rangedate <- add_phenology(add=Gratiot_rangedate, month_ref=1) parg <- par_init(data_Gratiot_rangedate, fixed.parameters=NULL) likelihood_phenology(data=data_Gratiot_rangedate, fitted.parameters=parg) result_Gratiot_rangedate <- fit_phenology(data=data_Gratiot_rangedate, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot_rangedate) likelihood_phenology(result=result_Gratiot_rangedate) # Exemple with data in range of date and CountTypes being minimum Gratiot_rangedate <- Gratiot Gratiot_rangedate[, 1] <- as.character(Gratiot_rangedate[, 1]) Gratiot_rangedate[148, 1] <- paste0(Gratiot_rangedate[148, 1], "-", Gratiot_rangedate[157, 1]) Gratiot_rangedate[148, 2] <- sum(Gratiot_rangedate[148:157, 2]) Gratiot_rangedate <- Gratiot_rangedate[-(149:157), ] Gratiot_rangedate <- cbind(Gratiot_rangedate, CountTypes="exact") Gratiot_rangedate[148, 2] <- 100 Gratiot_rangedate[148, "CountTypes"] <- "minimum" Gratiot_rangedate[28, "CountTypes"] <- "minimum" data_Gratiot_rangedate <- add_phenology(add=Gratiot_rangedate, colname.CountTypes="CountTypes", month_ref=1) parg <- par_init(data_Gratiot_rangedate, fixed.parameters=NULL) result_Gratiot_rangedate <- fit_phenology(data=data_Gratiot_rangedate, fitted.parameters=parg, fixed.parameters=NULL) likelihood_phenology(result=result_Gratiot_rangedate) plot(result_Gratiot_rangedate) ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # or output <- summary(result_Gratiot) # With one sinusoid parg <- c('LengthB' = 95.187648986054285, 'Peak' = 173.86111755419921, 'LengthE' = 63.183281230994481, 'Max_Complete' = 33.052091519419136, 'MinB_Complete' = 0.21716081973776738, 'MinE_Complete' = 0.42444245288475702, 'Theta' = 3.9554976911657187, 'Alpha' = 0, 'Beta' = 0.21019683898423902, 'Delta' = 3.6798422076201724, result_Gratiot1 <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot1) # With two sinusoids parg <- c('LengthB' = 95.821859173220659, 'Peak' = 174.89756503758881, 'LengthE' = 60.954167489825352, 'Max_Complete' = 33.497846416498774, 'MinB_Complete' = 0.21331670808871037, 'MinE_Complete' = 0.43730327416828613, 'Theta' = 4.2569203480842814, 'Alpha1' = 0.15305562092653918, 'Beta1' = 0, 'Delta1' = 9.435730952200263, 'Phi1' = 15.7944494669335, 'Alpha' = 0, 'Beta' = 0.28212926001402688, 'Delta' = 18.651087311957518, 'Phi' = 9.7549929595313056) result_Gratiot2 <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot2) compare_AICc(no=result_Gratiot, one=result_Gratiot1, two=result_Gratiot2) # With parametrization based on Girondot 2010 parg <- c('Peak' = 173.52272236775076, 'Flat' = 0, 'LengthB' = 94.433284359804205, 'LengthE' = 64.288485646867329, 'Max_Complete' = 32.841568389778033, 'PMin' = 1.0368261725650889, 'Theta' = 3.5534818927979592) result_Gratiot_par1 <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # With new parametrization based on Omeyer et al. (2022): # Omeyer, L. C. M., McKinley, T. J., Bréheret, N., Bal, G., Balchin, G. P., # Bitsindou, A., Chauvet, E., Collins, T., Curran, B. K., Formia, A., Girard, A., # Girondot, M., Godley, B. J., Mavoungou, J.-G., Poli, L., Tilley, D., # VanLeeuwe, H. & Metcalfe, K. 2022. Missing data in sea turtle population # monitoring: a Bayesian statistical framework accounting for incomplete # sampling Front. Mar. Sci. (IF 3.661), 9, 817014. parg <- result_Gratiot_par1$par parg <- c(tp=unname(parg["Peak"]), tf=unname(parg["Flat"]), s1=unname(parg["LengthB"])/4.8, s2=unname(parg["LengthE"])/4.8, alpha=unname(parg["Max_Complete"]), Theta=0.66) result_Gratiot_par2 <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) compare_AICc(GirondotModel=result_Gratiot_par1, OmeyerModel=result_Gratiot_par2) plot(result_Gratiot_par1) plot(result_Gratiot_par2) # Use fit with co-factor # First extract tide information for that place td <- tide.info(year=2001, latitude=4.9167, longitude=-52.3333) # I keep only High tide level td2 <- td[td$Phase=="High Tide", ] # I get the date td3 <- cbind(td2, Date=as.Date(td2$DateTime.local)) td5 <- aggregate(x=td3[, c("Date", "DateTime.local", "Tide.meter")], by=list(Date=td3[, "Date"]), FUN=max)[, 2:4] with(td5, plot(DateTime.local, Tide.meter, type="l")) td6 <- td5[, c("Date", "Tide.meter")] parg <- par_init(data_Gratiot, fixed.parameters=NULL, add.cofactors="Tide.meter") likelihood_phenology(data=data_Gratiot, fitted.parameters = parg, cofactors=td6, add.cofactors="Tide.meter") result_Gratiot_CF <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL, cofactors=td6, add.cofactors="Tide.meter") compare_AIC(WithoutCF=result_Gratiot, WithCF=result_Gratiot_CF) plot(result_Gratiot_CF) # Example with two series fitted with different peaks but same Length of season Gratiot2 <- Gratiot Gratiot2[, 2] <- floor(Gratiot2[, 2]*runif(n=nrow(Gratiot2))) data_Gratiot <- add_phenology(Gratiot, name="Complete1", reference=as.Date("2001-01-01"), format="%d/%m/%Y") data_Gratiot <- add_phenology(Gratiot2, name="Complete2", reference=as.Date("2001-01-01"), format="%d/%m/%Y", previous=data_Gratiot) pfixed=c(Min=0) p <- par_init(data_Gratiot, fixed.parameters = pfixed) p <- c(p, Peak_Complete1=175, Peak_Complete2=175) p <- p[-4] p <- c(p, Length=90) p <- p[-(3:4)] result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=p, fixed.parameters=pfixed) # An example with bimodality g <- Gratiot g[30:60, 2] <- sample(10:20, 31, replace = TRUE) data_g <- add_phenology(g, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") parg <- c('Max.1_Complete' = 5.6344636692341856, 'MinB.1_Complete' = 0.15488810581002324, 'MinE.1_Complete' = 0.2, 'LengthB.1' = 22.366647176407636, 'Peak.1' = 47.902473939250036, 'LengthE.1' = 17.828495918533015, 'Max.2_Complete' = 33.053364083447434, 'MinE.2_Complete' = 0.42438173496989717, 'LengthB.2' = 96.651564706802702, 'Peak.2' = 175.3451874571835, 'LengthE.2' = 62.481968743789835, 'Theta' = 3.6423908093342572) pfixed <- c('MinB.2_Complete' = 0, 'Flat.1' = 0, 'Flat.2' = 0) result_g <- fit_phenology(data=data_g, fitted.parameters=parg, fixed.parameters=pfixed) plot(result_g) # Exemple with some minimum counts nb <- Gratiot[, 2] nbs <- sample(0:1, length(nb), replace=TRUE) nb <- ifelse(nb != 0, ifelse(nbs == 1, 1, nb), 0) nbc <- ifelse(nb != 0, ifelse(nbs == 1, "minimum", "exact"), "exact") Gratiot_minimal <- cbind(Gratiot, CountTypes=nbc) Gratiot_minimal[, 2] <- nb data_Gratiot_minimal <- add_phenology(add=Gratiot_minimal, colname.CountTypes = "CountTypes", month_ref=1) parg <- par_init(data_Gratiot_minimal, fixed.parameters=NULL) result_Gratiot_minimal <- fit_phenology(data=data_Gratiot_minimal, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot_minimal) summary(result_Gratiot_minimal) # Exemple with all zero counts being not recorded Gratiot_NoZeroCounts <- cbind(Gratiot[Gratiot[, 2] != 0, ], ZeroCounts=FALSE) data_Gratiot_NoZeroCounts <- add_phenology(add=Gratiot_NoZeroCounts, colname.ZeroCounts = "ZeroCounts", ZeroCounts.defaul=FALSE, month_ref=1) # or data_Gratiot_NoZeroCounts <- add_phenology(add=Gratiot[Gratiot[, 2] != 0, ], ZeroCounts.default=FALSE, month_ref=1) parg <- par_init(data_Gratiot_NoZeroCounts, fixed.parameters=NULL) result_Gratiot_NoZeroCounts <- fit_phenology(data=data_Gratiot_NoZeroCounts, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot_NoZeroCounts) summary(result_Gratiot_NoZeroCounts) # Exemple with data in range of date Gratiot_rangedate <- Gratiot Gratiot_rangedate[, 1] <- as.character(Gratiot_rangedate[, 1]) Gratiot_rangedate[148, 1] <- paste0(Gratiot_rangedate[148, 1], "-", Gratiot_rangedate[157, 1]) Gratiot_rangedate[148, 2] <- sum(Gratiot_rangedate[148:157, 2]) Gratiot_rangedate <- Gratiot_rangedate[-(149:157), ] data_Gratiot_rangedate <- add_phenology(add=Gratiot_rangedate, month_ref=1) parg <- par_init(data_Gratiot_rangedate, fixed.parameters=NULL) likelihood_phenology(data=data_Gratiot_rangedate, fitted.parameters=parg) result_Gratiot_rangedate <- fit_phenology(data=data_Gratiot_rangedate, fitted.parameters=parg, fixed.parameters=NULL) plot(result_Gratiot_rangedate) likelihood_phenology(result=result_Gratiot_rangedate) # Exemple with data in range of date and CountTypes being minimum Gratiot_rangedate <- Gratiot Gratiot_rangedate[, 1] <- as.character(Gratiot_rangedate[, 1]) Gratiot_rangedate[148, 1] <- paste0(Gratiot_rangedate[148, 1], "-", Gratiot_rangedate[157, 1]) Gratiot_rangedate[148, 2] <- sum(Gratiot_rangedate[148:157, 2]) Gratiot_rangedate <- Gratiot_rangedate[-(149:157), ] Gratiot_rangedate <- cbind(Gratiot_rangedate, CountTypes="exact") Gratiot_rangedate[148, 2] <- 100 Gratiot_rangedate[148, "CountTypes"] <- "minimum" Gratiot_rangedate[28, "CountTypes"] <- "minimum" data_Gratiot_rangedate <- add_phenology(add=Gratiot_rangedate, colname.CountTypes="CountTypes", month_ref=1) parg <- par_init(data_Gratiot_rangedate, fixed.parameters=NULL) result_Gratiot_rangedate <- fit_phenology(data=data_Gratiot_rangedate, fitted.parameters=parg, fixed.parameters=NULL) likelihood_phenology(result=result_Gratiot_rangedate) plot(result_Gratiot_rangedate) ## End(Not run)
This function fits a model of clutch frequency.
This model is an enhanced version of the one published by Briane et al. (2007).
Parameters are mu and sd being the parameters of a
distribution used to model the clutch frequency.
This distribution is used only as a guide but has not statistical meaning.
The parameter p is the -logit probability that a female is seen
on the beach for a particular nesting event. It includes both the probability
that it is captured but also the probability that it uses that specific beach.
Several categories of females can be included in the model using index after
the name of the parameter, for example mu1, sd1 and mu2,
sd2 indicates that two categories of females with different clutch
frequencies distribution are present. Similarly p1 and p2 indicates
that two categories of females with different capture probabilities are present.
If more than one category is used, then it is necessary to include the
parameter OTN to indicate the relative frequencies of each category.
If two categories are used, one OTN parameter named ONT1 must
be included. The OTN2 is forced to be 1. Then the relative frequency
for category 1 is OTN1/(OTN1+1) and for category 2 is 1/(OTN1+1).
Same logic must be applied for 3 and more categories with always the last one
being fixed to 1.
if p or a (logit of the capture probability) are equal to -Inf,
the probability of capture is 0 and if they are equal to
+Inf, the probability is 1.
The value of p out of the period
of nesting must be set to +Inf (capture probability=1)
to indicate that no turtle is nesting in this period.
p must be set to -Inf (capture probability=0) to indicate that no
monitoring has been done during a specific period of the nesting season.
The best way to indicate capture probability for 3D model (OCF, ECF, Period)
is to indicate p.period common for all categories and a1, a2, etc for each category.
The capture probability for category 1 will be p.period * a1, and for category 2
will be p.period * a2, etc.
In this case, the parameters p.period should be indicated in fitted parameters
as well as a1, but a2 must be fixed to +Inf in fixed.parameters. Then the capture
probability for category 2 will be p.period and for category 1 a1 * p.period.
If itnmax is equal to 0, it will return the model using the parameters without fitting them.
fitCF( x = c(mu = 4, sd = 100, p = 0), fixed.parameters = NULL, data = stop("Data formated with TableECFOCF() must be provided"), method = c("Nelder-Mead", "BFGS"), control = list(trace = 1, REPORT = 100, maxit = 500), itnmax = c(500, 100), hessian = TRUE, parallel = TRUE, verbose = FALSE )fitCF( x = c(mu = 4, sd = 100, p = 0), fixed.parameters = NULL, data = stop("Data formated with TableECFOCF() must be provided"), method = c("Nelder-Mead", "BFGS"), control = list(trace = 1, REPORT = 100, maxit = 500), itnmax = c(500, 100), hessian = TRUE, parallel = TRUE, verbose = FALSE )
x |
Initial parameters to be fitted |
fixed.parameters |
Parameters that are fixed. |
data |
CMR data formated using TableECFOCF() |
method |
Method to be used by optimx() |
control |
List of controls for optimx() |
itnmax |
A vector with maximum iterations for each method. |
hessian |
Logical to estimate SE of parameters |
parallel |
If TRUE, will use parallel computing for ECFOCF_f() |
verbose |
If TRUE, print the parameters at each step |
fitCF fit a model of Clutch Frequency for marine turtles.
Return a list of class ECFOCF with the fit information.
The list has the following items:
data: The observations to be fitted
par: The fitted parameters
SE: The standard error of parameters if hessian is TRUE
value: The -log likelihood of observations within the fitted model
AIC: The AIC of fitted model
mu: The vector of fitted mu values
sd: The vector of fitted sd values
prob: The vector of fitted capture probabilities
a: The vector of fitted capture probabilities multiplier
OTN: The vector of fitted relative probabilities of contribution
period_categories: A list with the different period probabilities as named vectors for each category
period: The combined period probabilities using OTN as named vector
CF_categories: A list with the different CF probabilities as named vectors for each category
CF: The combined CF probabilities using OTN as named vector
ECFOCF_categories: A list with the different probability ECFOCF tables for each category
ECFOCF: The combined table of ECFOCF using OTN probabilities tables
ECFOCF_0: The combined table of ECFOCF probabilities tables using OTN without the OCF=0
SE_df: A data.frame with SE and 95\
Marc Girondot [email protected]
Briane J-P, Rivalan P, Girondot M (2007) The inverse problem applied to the Observed Clutch Frequency of Leatherbacks from Yalimapo beach, French Guiana. Chelonian Conservation and Biology 6:63-69
Fossette S, Kelle L, Girondot M, Goverse E, Hilterman ML, Verhage B, Thoisy B, de, Georges J-Y (2008) The world's largest leatherback rookeries: A review of conservation-oriented research in French Guiana/Suriname and Gabon. Journal of Experimental Marine Biology and Ecology 356:69-82
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
TableECFOCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
generateCF(),
lnLCF(),
logLik.ECFOCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library(phenology) # Example data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) # Parametric model for clutch frequency o_mu1p1_CFp <- fitCF(x = c(mu = 2.1653229641404539, sd = 1.1465246643327098, p = 0.25785366120357966), fixed.parameters=NULL, data=ECFOCF_2002, hessian = TRUE) # Non parametric model for clutch frequency o_mu1p1_CFnp <- fitCF(x = c(mu.1 = 18.246619595610383, mu.2 = 4.2702163522832892, mu.3 = 2.6289986859556458, mu.4 = 3.2496360919228611, mu.5 = 2.1602522716550943, mu.6 = 0.68617023351032846, mu.7 = 4.2623607001877026, mu.8 = 1.1805600042630455, mu.9 = 2.2786176350939731, mu.10 = 0.47676265496204945, mu.11 = 5.8988238539197062e-08, mu.12 = 1.4003187851424953e-07, mu.13 = 2.4128444894899776e-07, mu.14 = 2.4223748020049825e-07, p = 0.32094401970037578), fixed.parameters=c(mu.15 = 1E-10), data=ECFOCF_2002, hessian = TRUE) o_mu2p1 <- fitCF(x = c(mu1 = 1.2190766766978423, sd1 = 0.80646454821956925, mu2 = 7.1886819592223246, sd2 = 0.18152887523015518, p = 0.29347220802963259, OTN = 2.9137627675219533), fixed.parameters=NULL, data=ECFOCF_2002, hessian = TRUE) o_mu1p2 <- fitCF(x = c(mu = 5.3628701816871462, sd = 0.39390555498088764, p1 = 0.61159637544418755, p2 = -2.4212753004659189, OTN = 0.31898004668901009), data=ECFOCF_2002, hessian = TRUE) o_mu2p2 <- fitCF(x = c(mu1 = 0.043692606004492131, sd1 = 1.9446036983033428, mu2 = 7.3007868915644751, sd2 = 0.16109296152913491, p1 = 1.6860260469536992, p2 = -0.096816113083788985, OTN = 2.2604431232973501), data=ECFOCF_2002, hessian = TRUE) compare_AIC(mu1p1=o_mu1p1_CFp, mu2p1=o_mu2p1, mu1p2=o_mu1p2, mu2p2=o_mu2p2) o_mu3p3 <- fitCF(x = c(mu1 = 0.24286312214288761, sd1 = 0.34542255091729313, mu2 = 5.0817174343025551, sd2 = 1.87435099405695, mu3 = 5.2009265101740683, sd3 = 1.79700447678357, p1 = 8.8961708614726156, p2 = 0.94790116453886453, p3 = -0.76572930634505421, OTN1 = 1.2936848663276974, OTN2 = 0.81164278235645926), data=ECFOCF_2002, hessian = TRUE) o_mu3p1 <- fitCF(x = structure(c(0.24387978183477, 1.2639261745506, 4.94288464711349, 1.945082889758, 4.9431672350811, 1.287663104591, 0.323636536050397, 1.37072039291397, 9.28055412564559e-06), .Names = c("mu1", "sd1", "mu2", "sd2", "mu3", "sd3", "p", "OTN1", "OTN2")), data=ECFOCF_2002, hessian = TRUE) o_mu1p3 <- fitCF(x = structure(c(4.65792402108387, 1.58445909785, -2.35414198317177, 0.623757854800649, -3.62623634029326, 11.6950204755787, 4.05273728846523), .Names = c("mu", "sd", "p1", "p2", "p3", "OTN1", "OTN2")), data=ECFOCF_2002, hessian = TRUE) compare_AIC(mu1p1=o_mu1p1, mu2p1=o_mu2p1, mu1p2=o_mu1p2, mu2p2=o_mu2p2, mu3p3=o_mu3p3, mu1p3=o_mu1p3, mu3p1=o_mu3p1) # 3D model for (ECF, OCF, period) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(mu = 2.6404831115214353, sd = 0.69362774786433479, mu_season = 12.6404831115214353, sd_season = 1.69362774786433479) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]: attributes(ECFOCF_2002)$table["final"]]) # The value of p (logit of the capture probability) out of the period # of nesting must be set to +Inf (capture probability=1) # to indicate that no turtle is nesting in this period # p must be set to -Inf (capture probability=0) to indicate that no # monitoring has been done during a specific period of the nesting season. fixed.parameters <- c(p=+Inf) # The fitted values are: par <- c(mu = 2.4911638591178051, sd = 0.96855483039640977, mu_season = 13.836059118657793, sd_season = 0.17440085345943984, p.10 = 1.3348233607728222, p.11 = 1.1960387774393837, p.12 = 0.63025680979544774, p.13 = 0.38648155002707452, p.14 = 0.31547864054366048, p.15 = 0.19720001827017075, p.16 = 0.083199496372073328, p.17 = 0.32969130595897905, p.18 = 0.36582777525265819, p.19 = 0.30301248314170637, p.20 = 0.69993987591518514, p.21 = 0.13642423871641118, p.22 = -1.3949268190534629) o_mu1p1season1 <- fitCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) # Same model but with two different models of capture probabilities fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p1.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(mu = 2.6404831115214353, sd = 0.69362774786433479, mu_season = 12.6404831115214353, sd_season = 1.69362774786433479) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]: attributes(ECFOCF_2002)$table["final"]]) names(fp) <- paste0("p2.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]: attributes(ECFOCF_2002)$table["final"]]) fixed.parameters <- c(p1=+Inf, p2=+Inf) o_mu1p2season1 <- fitCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) # Here the two different capture probabilities are different # by a constant: # p1=invlogit(-p) [Note that invlogit(-a1) = 1] # p2=invlogit(-p)*invlogit(-a2) fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(mu = 2.6404831115214353, sd = 0.69362774786433479, mu_season = 12.6404831115214353, sd_season = 1.69362774786433479, a2=0) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]: attributes(ECFOCF_2002)$table["final"]]) fixed.parameters <- c(a1=+Inf, p=+Inf) o_mu1p1aseason1 <- fitCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) data=ECFOCF_2002) ## End(Not run)## Not run: library(phenology) # Example data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) # Parametric model for clutch frequency o_mu1p1_CFp <- fitCF(x = c(mu = 2.1653229641404539, sd = 1.1465246643327098, p = 0.25785366120357966), fixed.parameters=NULL, data=ECFOCF_2002, hessian = TRUE) # Non parametric model for clutch frequency o_mu1p1_CFnp <- fitCF(x = c(mu.1 = 18.246619595610383, mu.2 = 4.2702163522832892, mu.3 = 2.6289986859556458, mu.4 = 3.2496360919228611, mu.5 = 2.1602522716550943, mu.6 = 0.68617023351032846, mu.7 = 4.2623607001877026, mu.8 = 1.1805600042630455, mu.9 = 2.2786176350939731, mu.10 = 0.47676265496204945, mu.11 = 5.8988238539197062e-08, mu.12 = 1.4003187851424953e-07, mu.13 = 2.4128444894899776e-07, mu.14 = 2.4223748020049825e-07, p = 0.32094401970037578), fixed.parameters=c(mu.15 = 1E-10), data=ECFOCF_2002, hessian = TRUE) o_mu2p1 <- fitCF(x = c(mu1 = 1.2190766766978423, sd1 = 0.80646454821956925, mu2 = 7.1886819592223246, sd2 = 0.18152887523015518, p = 0.29347220802963259, OTN = 2.9137627675219533), fixed.parameters=NULL, data=ECFOCF_2002, hessian = TRUE) o_mu1p2 <- fitCF(x = c(mu = 5.3628701816871462, sd = 0.39390555498088764, p1 = 0.61159637544418755, p2 = -2.4212753004659189, OTN = 0.31898004668901009), data=ECFOCF_2002, hessian = TRUE) o_mu2p2 <- fitCF(x = c(mu1 = 0.043692606004492131, sd1 = 1.9446036983033428, mu2 = 7.3007868915644751, sd2 = 0.16109296152913491, p1 = 1.6860260469536992, p2 = -0.096816113083788985, OTN = 2.2604431232973501), data=ECFOCF_2002, hessian = TRUE) compare_AIC(mu1p1=o_mu1p1_CFp, mu2p1=o_mu2p1, mu1p2=o_mu1p2, mu2p2=o_mu2p2) o_mu3p3 <- fitCF(x = c(mu1 = 0.24286312214288761, sd1 = 0.34542255091729313, mu2 = 5.0817174343025551, sd2 = 1.87435099405695, mu3 = 5.2009265101740683, sd3 = 1.79700447678357, p1 = 8.8961708614726156, p2 = 0.94790116453886453, p3 = -0.76572930634505421, OTN1 = 1.2936848663276974, OTN2 = 0.81164278235645926), data=ECFOCF_2002, hessian = TRUE) o_mu3p1 <- fitCF(x = structure(c(0.24387978183477, 1.2639261745506, 4.94288464711349, 1.945082889758, 4.9431672350811, 1.287663104591, 0.323636536050397, 1.37072039291397, 9.28055412564559e-06), .Names = c("mu1", "sd1", "mu2", "sd2", "mu3", "sd3", "p", "OTN1", "OTN2")), data=ECFOCF_2002, hessian = TRUE) o_mu1p3 <- fitCF(x = structure(c(4.65792402108387, 1.58445909785, -2.35414198317177, 0.623757854800649, -3.62623634029326, 11.6950204755787, 4.05273728846523), .Names = c("mu", "sd", "p1", "p2", "p3", "OTN1", "OTN2")), data=ECFOCF_2002, hessian = TRUE) compare_AIC(mu1p1=o_mu1p1, mu2p1=o_mu2p1, mu1p2=o_mu1p2, mu2p2=o_mu2p2, mu3p3=o_mu3p3, mu1p3=o_mu1p3, mu3p1=o_mu3p1) # 3D model for (ECF, OCF, period) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(mu = 2.6404831115214353, sd = 0.69362774786433479, mu_season = 12.6404831115214353, sd_season = 1.69362774786433479) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]: attributes(ECFOCF_2002)$table["final"]]) # The value of p (logit of the capture probability) out of the period # of nesting must be set to +Inf (capture probability=1) # to indicate that no turtle is nesting in this period # p must be set to -Inf (capture probability=0) to indicate that no # monitoring has been done during a specific period of the nesting season. fixed.parameters <- c(p=+Inf) # The fitted values are: par <- c(mu = 2.4911638591178051, sd = 0.96855483039640977, mu_season = 13.836059118657793, sd_season = 0.17440085345943984, p.10 = 1.3348233607728222, p.11 = 1.1960387774393837, p.12 = 0.63025680979544774, p.13 = 0.38648155002707452, p.14 = 0.31547864054366048, p.15 = 0.19720001827017075, p.16 = 0.083199496372073328, p.17 = 0.32969130595897905, p.18 = 0.36582777525265819, p.19 = 0.30301248314170637, p.20 = 0.69993987591518514, p.21 = 0.13642423871641118, p.22 = -1.3949268190534629) o_mu1p1season1 <- fitCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) # Same model but with two different models of capture probabilities fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p1.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(mu = 2.6404831115214353, sd = 0.69362774786433479, mu_season = 12.6404831115214353, sd_season = 1.69362774786433479) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]: attributes(ECFOCF_2002)$table["final"]]) names(fp) <- paste0("p2.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]: attributes(ECFOCF_2002)$table["final"]]) fixed.parameters <- c(p1=+Inf, p2=+Inf) o_mu1p2season1 <- fitCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) # Here the two different capture probabilities are different # by a constant: # p1=invlogit(-p) [Note that invlogit(-a1) = 1] # p2=invlogit(-p)*invlogit(-a2) fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(mu = 2.6404831115214353, sd = 0.69362774786433479, mu_season = 12.6404831115214353, sd_season = 1.69362774786433479, a2=0) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]: attributes(ECFOCF_2002)$table["final"]]) fixed.parameters <- c(a1=+Inf, p=+Inf) o_mu1p1aseason1 <- fitCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) data=ECFOCF_2002) ## End(Not run)
Run the Metropolis-Hastings algorithm for RMU.data.
The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed.
I recommend thin=1 because the method to estimate SE uses resampling.
As initial point is maximum likelihood, n.adapt = 0 is a good solution.
The parameters intermediate and filename are used to save intermediate results every 'intermediate' iterations (for example 1000). Results are saved in a file of name filename.
The parameter previous is used to indicate the list that has been save using the parameters intermediate and filename. It permits to continue a mcmc search.
These options are used to prevent the consequences of computer crash or if the run is very very long and computer processes at time limited.
fitCF_MHmcmc( result = stop("An output from fitCF() must be provided"), n.iter = 10000, parametersMCMC = stop("A parameter set from fitCF_MHmcmc_p() must be provided"), n.chains = 1, n.adapt = 0, thin = 1, adaptive = FALSE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, trace = FALSE, traceML = FALSE, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )fitCF_MHmcmc( result = stop("An output from fitCF() must be provided"), n.iter = 10000, parametersMCMC = stop("A parameter set from fitCF_MHmcmc_p() must be provided"), n.chains = 1, n.adapt = 0, thin = 1, adaptive = FALSE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, trace = FALSE, traceML = FALSE, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )
result |
An object obtained after a SearchR fit |
n.iter |
Number of iterations for each step |
parametersMCMC |
A set of parameters used as initial point for searching with information on priors |
n.chains |
Number of replicates |
n.adapt |
Number of iterations before to store outputs |
thin |
Number of iterations between each stored output |
adaptive |
Should an adaptive process for SDProp be used |
adaptive.lag |
Lag to analyze the SDProp value in an adaptive content |
adaptive.fun |
Function used to change the SDProp |
trace |
TRUE or FALSE or period, shows progress |
traceML |
TRUE or FALSE to show ML |
intermediate |
Period for saving intermediate result, NULL for no save |
filename |
If intermediate is not NULL, save intermediate result in this file |
previous |
Previous result to be continued. Can be the filename in which intermediate results are saved. |
fitCF_MHmcmc runs the Metropolis-Hastings algorithm for ECFOCF (Bayesian MCMC)
A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used
Marc Girondot [email protected]
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc_p(),
generateCF(),
lnLCF(),
logLik.ECFOCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library("phenology") data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) # Paraetric model for clutch frequency o_mu1p1_CFp <- fitCF(x = c(mu = 2.1653229641404539, sd = 1.1465246643327098, p = 0.25785366120357966), fixed.parameters=NULL, data=ECFOCF_2002, hessian = TRUE) pMCMC <- fitCF_MHmcmc_p(result=o_mu1p1_CFp, accept=TRUE) fitCF_MCMC <- fitCF_MHmcmc(result = o_mu1p1_CFp, n.iter = 1000, parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0, adaptive=TRUE, thin = 1, trace = TRUE) plot(fitCF_MCMC, parameters="mu") plot(fitCF_MCMC, parameters="sd") plot(fitCF_MCMC, parameters="p", xlim=c(0, 0.5), breaks=seq(from=0, to=0.5, by=0.05)) plot(fitCF_MCMC, parameters="p", transform = invlogit, xlim=c(0, 1), breaks=c(seq(from=0, to=1, by=0.05))) ## End(Not run)## Not run: library("phenology") data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) # Paraetric model for clutch frequency o_mu1p1_CFp <- fitCF(x = c(mu = 2.1653229641404539, sd = 1.1465246643327098, p = 0.25785366120357966), fixed.parameters=NULL, data=ECFOCF_2002, hessian = TRUE) pMCMC <- fitCF_MHmcmc_p(result=o_mu1p1_CFp, accept=TRUE) fitCF_MCMC <- fitCF_MHmcmc(result = o_mu1p1_CFp, n.iter = 1000, parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0, adaptive=TRUE, thin = 1, trace = TRUE) plot(fitCF_MCMC, parameters="mu") plot(fitCF_MCMC, parameters="sd") plot(fitCF_MCMC, parameters="p", xlim=c(0, 0.5), breaks=seq(from=0, to=0.5, by=0.05)) plot(fitCF_MCMC, parameters="p", transform = invlogit, xlim=c(0, 1), breaks=c(seq(from=0, to=1, by=0.05))) ## End(Not run)
Interactive script used to generate set of parameters to be used with fitCF_MHmcmc().
fitCF_MHmcmc_p( result = stop("An output from fitCF() must be provided"), density = "dunif", accept = FALSE )fitCF_MHmcmc_p( result = stop("An output from fitCF() must be provided"), density = "dunif", accept = FALSE )
result |
An object obtained after a fitCF() fit |
density |
Preset of density; can be dnorm or dunif |
accept |
If TRUE, does not wait for user interaction |
fitCF_MHmcmc_p generates set of parameters to be used with fitCF_MHmcmc()
A matrix with the parameters
Marc Girondot
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc(),
generateCF(),
lnLCF(),
logLik.ECFOCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library("phenology") data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) # Paraetric model for clutch frequency o_mu1p1_CFp <- fitCF(x = c(mu = 2.1653229641404539, sd = 1.1465246643327098, p = 0.25785366120357966), fixed.parameters=NULL, data=ECFOCF_2002, hessian = TRUE) pMCMC <- fitCF_MHmcmc_p(result=o_mu1p1_CFp, accept=TRUE) fitCF_MCMC <- fitCF_MHmcmc(result = o_mu1p1_CFp, n.iter = 10000, parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) plot(fitCF_MCMC, parameters="mu") plot(fitCF_MCMC, parameters="sd") plot(fitCF_MCMC, parameters="p", xlim=c(0, 0.5), breaks=seq(from=0, to=0.5, by=0.05)) plot(fitCF_MCMC, parameters="p", transform = invlogit, xlim=c(0, 1), breaks=c(seq(from=0, to=1, by=0.05))) ## End(Not run)## Not run: library("phenology") data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) # Paraetric model for clutch frequency o_mu1p1_CFp <- fitCF(x = c(mu = 2.1653229641404539, sd = 1.1465246643327098, p = 0.25785366120357966), fixed.parameters=NULL, data=ECFOCF_2002, hessian = TRUE) pMCMC <- fitCF_MHmcmc_p(result=o_mu1p1_CFp, accept=TRUE) fitCF_MCMC <- fitCF_MHmcmc(result = o_mu1p1_CFp, n.iter = 10000, parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) plot(fitCF_MCMC, parameters="mu") plot(fitCF_MCMC, parameters="sd") plot(fitCF_MCMC, parameters="p", xlim=c(0, 0.5), breaks=seq(from=0, to=0.5, by=0.05)) plot(fitCF_MCMC, parameters="p", transform = invlogit, xlim=c(0, 1), breaks=c(seq(from=0, to=1, by=0.05))) ## End(Not run)
The data must be a data.frame with the first column being years
and two columns for each beach: the average and the se for the estimate.
The correspondence between mean, se and density for each rookery are given in the RMU.names data.frame.
This data.frame must have a column named mean, another named se and a third named density. If
no sd column exists, no sd will be considered for the series and if no density column exists, it
will be considered as being "dnorm" (Gaussian distribution).
The aggregated number of nests and its confidence interval can be obtained using CI.RMU().
The names of beach columns must not begin by T_, SD_, a0_, a1_ or a2_ and cannot be r.
A RMU is the acronyme for Regional Managment Unit. See:
Wallace, B.P., DiMatteo, A.D., Hurley, B.J., Finkbeiner, E.M., Bolten, A.B.,
Chaloupka, M.Y., Hutchinson, B.J., Abreu-Grobois, F.A., Amorocho, D., Bjorndal, K.A.,
Bourjea, J., Bowen, B.W., Dueñas, R.B., Casale, P., Choudhury, B.C., Costa, A.,
Dutton, P.H., Fallabrino, A., Girard, A., Girondot, M., Godfrey, M.H., Hamann, M.,
López-Mendilaharsu, M., Marcovaldi, M.A., Mortimer, J.A., Musick, J.A., Nel, R.,
Seminoff, J.A., Troëng, S., Witherington, B., Mast, R.B., 2010. Regional
management units for marine turtles: a novel framework for prioritizing
conservation and research across multiple scales. PLoS One 5, e15465.
Variance for each value is additive based on both the observed SE (in the RMU.data
object) and a value.
The value is a global constant when model.SD is "global-constant".
The value is proportional to the observed number of nests when model.SD is
"global-proportional" with aSD_*observed+SD_ with aSD_ and SD_ being fitted
values. This value is fixed to zero when model.SD is "Zero".
The value is dependent on the rookery when model.SD is equal to
"Rookery-constant" or "Rookery-proportional" with a similar formula as previously
described for "global".
if method is NULL, it will simply return the names of required parameters.
fitRMU( RMU.data = stop("data parameter must be provided"), years.byrow = TRUE, RMU.names = NULL, model.trend = "Constant", model.rookeries = "Constant", model.SD = "Global-constant", parameters = NULL, fixed.parameters = NULL, SE = NULL, method = c("Nelder-Mead", "BFGS"), control = list(trace = 1), itnmax = c(1500, 1500), cptmax.optim = 100, limit.cpt.optim = 1e-05, hessian = TRUE, replicate.CI = 1000, colname.year = "Year", maxL = 1e+09 )fitRMU( RMU.data = stop("data parameter must be provided"), years.byrow = TRUE, RMU.names = NULL, model.trend = "Constant", model.rookeries = "Constant", model.SD = "Global-constant", parameters = NULL, fixed.parameters = NULL, SE = NULL, method = c("Nelder-Mead", "BFGS"), control = list(trace = 1), itnmax = c(1500, 1500), cptmax.optim = 100, limit.cpt.optim = 1e-05, hessian = TRUE, replicate.CI = 1000, colname.year = "Year", maxL = 1e+09 )
RMU.data |
A data.frame with a column Year (the name is defined in colname.year) and one to three columns per rookery defined in RMU.names |
years.byrow |
If TRUE, the RMU.data data.frame is organized with years in rows |
RMU.names |
A dataframe with one to three columns indicating name of columns for mean, standard deviation, and distribution for roockeris |
model.trend |
Can be Constant, Exponential or Year-specific |
model.rookeries |
Description temporal change in rookeries proportion. It be Constant, First-order or Second-order |
model.SD |
Can be Zero, Global-constant, Global-proportional or Rookery-constant. See description. |
parameters |
Parameters to fit |
fixed.parameters |
Parameters that are fixed |
SE |
Parameters SE for example from fitRMU_MHmcmc() |
method |
Methods to be used by optim() |
control |
List of controls for optim() |
itnmax |
A vector with maximum iterations for each method. |
cptmax.optim |
How many times optim can be ran when likelihood is better. |
limit.cpt.optim |
Limit to consider that likelihood is better. |
hessian |
If TRUE, the hessian matrix is calculated and then the standard error of parameters. |
replicate.CI |
Number of replicates to estimate CI of proportion for each rookery |
colname.year |
Name of the column to be used as time index |
maxL |
If an error is produced during the estimation of likelihood, replace -Ln L by this value |
fitRMU is used to estimate missing information when several linked values are observed along a timeseries
Return a list with the results from optim and synthesis for proportions and numbers
Marc Girondot [email protected]
Other Fill gaps in RMU:
CI.RMU(),
fitRMU_MHmcmc(),
fitRMU_MHmcmc_p(),
logLik.fitRMU(),
plot.fitRMU()
## Not run: library("phenology") RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), density_Yalimapo.French.Guiana=rep("dnorm", 11), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), density_Galibi.Suriname=rep("dnorm", 11), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), density_Irakumpapy.French.Guiana=rep("dnorm", 11) ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", control=list(trace=1, REPORT=100, maxit=500, parscale = c(3000, -0.2, 0.6))) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", method=c("Nelder-Mead","BFGS"), control = list(trace = 0, REPORT = 100, maxit = 500, parscale = c(3000, -0.2, 0.6))) expo <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Exponential", model.SD="Zero", method=c("Nelder-Mead","BFGS"), control = list(trace = 0, REPORT = 100, maxit = 500, parscale = c(6000, -0.05, -0.25, 0.6))) YS <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), model.SD="Zero") YS1 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), model.SD="Zero", model.rookeries="First-order") YS1_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="First-order", parameters=YS1$par, method=c("Nelder-Mead","BFGS")) YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="Second-order", parameters=YS1$par, method=c("Nelder-Mead","BFGS")) YS2_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="Second-order", parameters=YS1_cst$par, method=c("Nelder-Mead","BFGS")) compare_AIC(Constant=cst, Exponential=expo, YearSpecific=YS) compare_AIC(YearSpecific_ProportionsFirstOrder_Zero=YS1, YearSpecific_ProportionsFirstOrder_Constant=YS1_cst) compare_AIC(YearSpecific_ProportionsConstant=YS, YearSpecific_ProportionsFirstOrder=YS1, YearSpecific_ProportionsSecondOrder=YS2) compare_AIC(YearSpecific_ProportionsFirstOrder=YS1_cst, YearSpecific_ProportionsSecondOrder=YS2_cst) # Example of different types of plots plot(cst, main="Use of different beaches along the time", what="total", ylim=c(0, 4000)) plot(cst, main="Use of different beaches along the time", what = "proportions", replicate.CI=0) plot(cst, main="Use of different beaches along the time", what = "numbers", aggregate="model", ylim=c(0, 4000), replicate.CI=0) plot(cst, main="Use of different beaches along the time", what = "numbers", aggregate="both", ylim=c(0, 11000), replicate.CI=0) plot(expo, main="Use of different beaches along the time", what="total") plot(YS2_cst, main="Use of different beaches along the time", what="total") plot(YS1, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time", what="numbers") # Gamma distribution should be used for MCMC outputs RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_Irakumpapy.French.Guiana"), stringsAsFactors = FALSE) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), density_Yalimapo.French.Guiana=rep("dgamma", 11), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), density_Galibi.Suriname=rep("dgamma", 11), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), density_Irakumpapy.French.Guiana=rep("dgamma", 11), stringsAsFactors = FALSE ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") ## End(Not run)## Not run: library("phenology") RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), density_Yalimapo.French.Guiana=rep("dnorm", 11), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), density_Galibi.Suriname=rep("dnorm", 11), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), density_Irakumpapy.French.Guiana=rep("dnorm", 11) ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", control=list(trace=1, REPORT=100, maxit=500, parscale = c(3000, -0.2, 0.6))) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", method=c("Nelder-Mead","BFGS"), control = list(trace = 0, REPORT = 100, maxit = 500, parscale = c(3000, -0.2, 0.6))) expo <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Exponential", model.SD="Zero", method=c("Nelder-Mead","BFGS"), control = list(trace = 0, REPORT = 100, maxit = 500, parscale = c(6000, -0.05, -0.25, 0.6))) YS <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), model.SD="Zero") YS1 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), model.SD="Zero", model.rookeries="First-order") YS1_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="First-order", parameters=YS1$par, method=c("Nelder-Mead","BFGS")) YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="Second-order", parameters=YS1$par, method=c("Nelder-Mead","BFGS")) YS2_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="Second-order", parameters=YS1_cst$par, method=c("Nelder-Mead","BFGS")) compare_AIC(Constant=cst, Exponential=expo, YearSpecific=YS) compare_AIC(YearSpecific_ProportionsFirstOrder_Zero=YS1, YearSpecific_ProportionsFirstOrder_Constant=YS1_cst) compare_AIC(YearSpecific_ProportionsConstant=YS, YearSpecific_ProportionsFirstOrder=YS1, YearSpecific_ProportionsSecondOrder=YS2) compare_AIC(YearSpecific_ProportionsFirstOrder=YS1_cst, YearSpecific_ProportionsSecondOrder=YS2_cst) # Example of different types of plots plot(cst, main="Use of different beaches along the time", what="total", ylim=c(0, 4000)) plot(cst, main="Use of different beaches along the time", what = "proportions", replicate.CI=0) plot(cst, main="Use of different beaches along the time", what = "numbers", aggregate="model", ylim=c(0, 4000), replicate.CI=0) plot(cst, main="Use of different beaches along the time", what = "numbers", aggregate="both", ylim=c(0, 11000), replicate.CI=0) plot(expo, main="Use of different beaches along the time", what="total") plot(YS2_cst, main="Use of different beaches along the time", what="total") plot(YS1, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time", what="numbers") # Gamma distribution should be used for MCMC outputs RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_Irakumpapy.French.Guiana"), stringsAsFactors = FALSE) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), density_Yalimapo.French.Guiana=rep("dgamma", 11), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), density_Galibi.Suriname=rep("dgamma", 11), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), density_Irakumpapy.French.Guiana=rep("dgamma", 11), stringsAsFactors = FALSE ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") ## End(Not run)
Run the Metropolis-Hastings algorithm for RMU.data.
The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed.
I recommend thin=1 because the method to estimate SE uses resampling.
As initial point is maximum likelihood, n.adapt = 0 is a good solution.
The parameters intermediate and filename are used to save intermediate results every 'intermediate' iterations (for example 1000). Results are saved in a file of name filename.
The parameter previous is used to indicate the list that has been save using the parameters intermediate and filename. It permits to continue a mcmc search.
These options are used to prevent the consequences of computer crash or if the run is very very long and computer processes at time limited.
fitRMU_MHmcmc( result = stop("An output from fitRMU() must be provided"), n.iter = 10000, parametersMCMC = stop("A parameter set from fitRMU_MHmcmc_p() must be provided"), n.chains = 1, n.adapt = 0, thin = 1, adaptive = FALSE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, trace = FALSE, traceML = FALSE, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )fitRMU_MHmcmc( result = stop("An output from fitRMU() must be provided"), n.iter = 10000, parametersMCMC = stop("A parameter set from fitRMU_MHmcmc_p() must be provided"), n.chains = 1, n.adapt = 0, thin = 1, adaptive = FALSE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, trace = FALSE, traceML = FALSE, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )
result |
An object obtained after a SearchR fit |
n.iter |
Number of iterations for each step |
parametersMCMC |
A set of parameters used as initial point for searching with information on priors |
n.chains |
Number of replicates |
n.adapt |
Number of iterations before to store outputs |
thin |
Number of iterations between each stored output |
adaptive |
Should an adaptive process for SDProp be used |
adaptive.lag |
Lag to analyze the SDProp value in an adaptive content |
adaptive.fun |
Function used to change the SDProp |
trace |
TRUE or FALSE or period, shows progress |
traceML |
TRUE or FALSE to show ML |
intermediate |
Period for saving intermediate result, NULL for no save |
filename |
If intermediate is not NULL, save intermediate result in this file |
previous |
Previous result to be continued. Can be the filename in which intermediate results are saved. |
fitRMU_MHmcmc runs the Metropolis-Hastings algorithm for RMU.data (Bayesian MCMC)
A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used
Marc Girondot
Other Fill gaps in RMU:
CI.RMU(),
fitRMU(),
fitRMU_MHmcmc_p(),
logLik.fitRMU(),
plot.fitRMU()
## Not run: library("phenology") RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20)) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") pMCMC <- fitRMU_MHmcmc_p(result=cst, accept=TRUE) fitRMU_MCMC <- fitRMU_MHmcmc(result = cst, n.iter = 10000, parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) ## End(Not run)## Not run: library("phenology") RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20)) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") pMCMC <- fitRMU_MHmcmc_p(result=cst, accept=TRUE) fitRMU_MCMC <- fitRMU_MHmcmc(result = cst, n.iter = 10000, parametersMCMC = pMCMC, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) ## End(Not run)
Interactive or automatic script used to generate set of parameters to be
used with fitRMU_MHmcmc().
If density="dgamma" is used, a uniform distribution is used for r, as
r can be negative.
fitRMU_MHmcmc_p( result = stop("An output from fitRMU() must be provided"), density = "dunif", accept = FALSE )fitRMU_MHmcmc_p( result = stop("An output from fitRMU() must be provided"), density = "dunif", accept = FALSE )
result |
An object obtained after a fitRMU() fit |
density |
Preset of density; can be dnorm, dunif, or dgamma |
accept |
If TRUE, does not wait for user interaction |
fitRMU_MHmcmc_p generates set of parameters to be used with fitRMU_MHmcmc()
A matrix with the parameters
Marc Girondot
Other Fill gaps in RMU:
CI.RMU(),
fitRMU(),
fitRMU_MHmcmc(),
logLik.fitRMU(),
plot.fitRMU()
## Not run: library("phenology") RMU.name.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20)) cst <- fitRMU(data=data.AtlanticW, RMU.name=RMU.name.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") pMCMC <- fitRMU_MHmcmc_p(result=cst, accept=TRUE) ## End(Not run)## Not run: library("phenology") RMU.name.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20)) cst <- fitRMU(data=data.AtlanticW, RMU.name=RMU.name.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") pMCMC <- fitRMU_MHmcmc_p(result=cst, accept=TRUE) ## End(Not run)
This function generates a set of fixed parameters for series with only 0 counts.
The parameter series must be a result from add_phenology().
fixed.parameters0( series = stop("A result from add_phenology() must be provided.") )fixed.parameters0( series = stop("A result from add_phenology() must be provided.") )
series |
Set of series generated with add_phenology() |
fixed.parameters0 generates a set of fixed parameters for series with only 0 counts
Return a set of parameters
Marc Girondot
## Not run: refdate <- as.Date("2001-01-01") data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") pfixed <- fixed.parameters0(data_Gratiot) ## End(Not run)## Not run: refdate <- as.Date("2001-01-01") data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") pfixed <- fixed.parameters0(data_Gratiot) ## End(Not run)
This function generates a dataframe to test fitCF().
This model is an enhanced version of the one published by Briane et al. (2007).
Parameters are mu and sd being the parameters of a
distribution used to model the clutch frequency.
This distribution is used only as a guide but has not statistical meaning.
The parameter p is the -logit probability that a female is seen
on the beach for a particular nesting event. It includes both the probability
that it is captured but also the probability that it uses that specific beach.
Several categories of females can be included in the model using index after
the name of the parameter, for example mu1, sd1 and mu2,
sd2 indicates that two categories of females with different clutch
frequencies distribution are present. Similarly p1 and p2 indicates
that two categories of females with different capture probabilities are present.
If more than one category is used, then it is necessary to include the
parameter OTN to indicate the relative frequencies of each category.
If two categories are used, one OTN parameter named ONT1 must
be included. The OTN2 is forced to be 1. Then the relative frequency
for category 1 is OTN1/(OTN1+1) and for category 2 is 1/(OTN1+1).
Same logic must be applied for 3 and more categories with always the last one
being fixed to 1.
if p or a (logit of the capture probability) are equal to -Inf,
the probability of capture is 0 and if they are equal to
+Inf, the probability is 1.
The value of p out of the period
of nesting must be set to +Inf (capture probability=1)
to indicate that no turtle is nesting in this period.
p must be set to -Inf (capture probability=0) to indicate that no
monitoring has been done during a specific period of the nesting season.
The best way to indicate capture probability for 3D model (OCF, ECF, Period)
is to indicate p.period common for all categories and a1, a2, etc for each category.
The capture probability for category 1 will be p.period * a1, and for category 2
will be p.period * a2, etc.
In this case, the parameters p.period should be indicated in fitted parameters
as well as a1, but a2 must be fixed to +Inf in fixed.parameters. Then the capture
probability for category 2 will be p.period and for category 1 a1 * p.period.
generateCF( x = c(mu = 4, sd = 1, p = +Inf, mu_season = 13.8360591186578, sd_season = 0.17440085345944), MeanDaysBetween2Nests = 9.8, date0 = as.Date("2020-01-01"), n = 1, verbose = TRUE )generateCF( x = c(mu = 4, sd = 1, p = +Inf, mu_season = 13.8360591186578, sd_season = 0.17440085345944), MeanDaysBetween2Nests = 9.8, date0 = as.Date("2020-01-01"), n = 1, verbose = TRUE )
x |
Initial parameters to be used |
MeanDaysBetween2Nests |
Number of days in average between two nests |
date0 |
Initial date to generate data |
n |
Number of individuals to model |
verbose |
If TRUE, give information about each animal. |
generateCF generates set of data to test fitCF.
Return a list with 4 elements: Category, CF, Beginning and Observations being a dataframe of individuals.
Marc Girondot
Briane J-P, Rivalan P, Girondot M (2007) The inverse problem applied to the Observed Clutch Frequency of Leatherbacks from Yalimapo beach, French Guiana. Chelonian Conservation and Biology 6:63-69
Fossette S, Kelle L, Girondot M, Goverse E, Hilterman ML, Verhage B, Thoisy B, de, Georges J-Y (2008) The world's largest leatherback rookeries: A review of conservation-oriented research in French Guiana/Suriname and Gabon. Journal of Experimental Marine Biology and Ecology 356:69-82
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
lnLCF(),
logLik.ECFOCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library(phenology) # Example par <- c(mu = 2.4911638591178051, sd = 0.96855483039640977, mu_season = 13.836059118657793, sd_season = 0.17440085345943984, p.10 = 1.3348233607728222, p.11 = 1.1960387774393837, p.12 = 0.63025680979544774, p.13 = 0.38648155002707452, p.14 = 0.31547864054366048, p.15 = 0.19720001827017075, p.16 = 0.083199496372073328, p.17 = 0.32969130595897905, p.18 = 0.36582777525265819, p.19 = 0.30301248314170637, p.20 = 0.69993987591518514, p.21 = 0.13642423871641118, p.22 = -1.3949268190534629, p=+Inf) o_mu1p1season1 <- generateCF(x=par, n=1, verbose=TRUE) o_mu1p1season1 <- generateCF(x=par, n=1000) plot(o_mu1p1season1$CF) hist(o_mu1p1season1$Beginning) ## End(Not run)## Not run: library(phenology) # Example par <- c(mu = 2.4911638591178051, sd = 0.96855483039640977, mu_season = 13.836059118657793, sd_season = 0.17440085345943984, p.10 = 1.3348233607728222, p.11 = 1.1960387774393837, p.12 = 0.63025680979544774, p.13 = 0.38648155002707452, p.14 = 0.31547864054366048, p.15 = 0.19720001827017075, p.16 = 0.083199496372073328, p.17 = 0.32969130595897905, p.18 = 0.36582777525265819, p.19 = 0.30301248314170637, p.20 = 0.69993987591518514, p.21 = 0.13642423871641118, p.22 = -1.3949268190534629, p=+Inf) o_mu1p1season1 <- generateCF(x=par, n=1, verbose=TRUE) o_mu1p1season1 <- generateCF(x=par, n=1000) plot(o_mu1p1season1$CF) hist(o_mu1p1season1$Beginning) ## End(Not run)
Leatherback nest counts from Gratiot et al. (2006) Figure 1. These
data have been collected by the ONG Kwata in French Guiana.
The data have been obtained from the graph of the publication (see reference).
GratiotGratiot
data.frame with the morning date in the first column and the nest counts on the second one.
Leatherback nest counts from Gratiot et al. (2006) Figure 1
KWATA ONG
Gratiot, N., Gratiot, J., de Thoisy, B. & Kelle, L. 2006. Estimation of marine turtles nesting season from incomplete data ; statistical adjustment of a sinusoidal function. Animal Conservation, 9, 95-102.
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Read a file with data data(Gratiot)library(phenology) # Read a file with data data(Gratiot)
This function fits a model of internesting period using maximum
likelihood or using Metropolis-Hastings algorithm with Bayesian model.
The fit using maximum likelihood is not the best strategy because the objective
function is based on a stochastic model (and then a single set of parameters
does not produce exactly the same output each time).
The use of Metropolis-Hastings algorithm (a Markov chain Monte Carlo method)
should be prefered.
IPFit( x = NULL, fixed.parameters = NULL, data = stop("Formated data must be provided"), method = c("Nelder-Mead", "BFGS"), control = list(trace = 1, REPORT = 100, maxit = 500), itnmax = c(500, 100), hessian = TRUE, verbose = TRUE, parallel = TRUE, model = c("MH", "ML"), parametersMH, n.iter = 10000, n.chains = 1, n.adapt = 100, thin = 30, trace = TRUE, adaptive = TRUE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata" )IPFit( x = NULL, fixed.parameters = NULL, data = stop("Formated data must be provided"), method = c("Nelder-Mead", "BFGS"), control = list(trace = 1, REPORT = 100, maxit = 500), itnmax = c(500, 100), hessian = TRUE, verbose = TRUE, parallel = TRUE, model = c("MH", "ML"), parametersMH, n.iter = 10000, n.chains = 1, n.adapt = 100, thin = 30, trace = TRUE, adaptive = TRUE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata" )
x |
Initial parameters to be fitted |
fixed.parameters |
Parameters that are fixed. |
data |
Data as a vector |
method |
Method to be used by optimx() |
control |
List of controls for optimx() |
itnmax |
A vector with maximum iterations for each method. |
hessian |
Logical to estimate SE of parameters |
verbose |
If TRUE, show the parameters for each tested model |
parallel |
If TRUE, will use parallel computing |
model |
Can be ML for Maximum likelihood or MH for Metropolis Hastings |
parametersMH |
The priors. See MHalgoGen |
n.iter |
See MHalgoGen |
n.chains |
See MHalgoGen |
n.adapt |
See MHalgoGen |
thin |
See MHalgoGen |
trace |
See MHalgoGen |
adaptive |
See MHalgoGen |
adaptive.lag |
See MHalgoGen |
adaptive.fun |
See MHalgoGen |
intermediate |
See MHalgoGen |
filename |
See MHalgoGen |
IPFit fit a model of Internesting Period for marine turtles.
Return a list of class IP with the fit informations and the fitted model.
Marc Girondot [email protected]
Other Model of Internesting Period:
IPModel(),
IPPredict(),
plot.IP(),
summary.IP()
## Not run: library(phenology) # Example data <- structure(c(`0` = 0, `1` = 47, `2` = 15, `3` = 6, `4` = 5, `5` = 4, `6` = 2, `7` = 5, `8` = 57, `9` = 203, `10` = 205, `11` = 103, `12` = 35, `13` = 24, `14` = 12, `15` = 10, `16` = 13, `17` = 49, `18` = 86, `19` = 107, `20` = 111, `21` = 73, `22` = 47, `23` = 30, `24` = 19, `25` = 17, `26` = 33, `27` = 48, `28` = 77, `29` = 83, `30` = 65, `31` = 37, `32` = 27, `33` = 23, `34` = 24, `35` = 22, `36` = 41, `37` = 42, `38` = 44, `39` = 33, `40` = 39, `41` = 24, `42` = 18, `43` = 18, `44` = 22, `45` = 22, `46` = 19, `47` = 24, `48` = 28, `49` = 17, `50` = 18, `51` = 19, `52` = 17, `53` = 4, `54` = 12, `55` = 9, `56` = 6, `57` = 11, `58` = 7, `59` = 11, `60` = 12, `61` = 5, `62` = 4, `63` = 6, `64` = 11, `65` = 5, `66` = 6, `67` = 7, `68` = 3, `69` = 2, `70` = 1, `71` = 3, `72` = 2, `73` = 1, `74` = 2, `75` = 0, `76` = 0, `77` = 3, `78` = 1, `79` = 0, `80` = 2, `81` = 0, `82` = 0, `83` = 1), Year = "1994", Species = "Dermochelys coriacea", location = "Yalimapo beach, French Guiana", totalnumber = 2526L, class = "IP") par(mar=c(4, 4, 1, 1)+0.4) plot(data, xlim=c(0,100)) text(100, 190, labels=bquote(italic(.(attributes(data)$Species))), pos=2) text(100, 150, labels=attributes(data)$location, pos=2, cex=0.8) text(100, 110, labels=paste0(as.character(attributes(data)$totalnumber), " females"), pos=2) ######### Fit using Maximum-Likelihood par <- c(meanIP = 9.8229005713237623, sdIP = 0.079176011861863474, minIP = 6.8128364577569309, pAbort = 1.5441529841959203, meanAbort = 2.7958742380756121, sdAbort = 0.99370406770777175, pCapture = -0.80294884905867658, meanECF = 4.5253772889275758, sdECF = 0.20334743335612529) fML <- IPFit(x=par, fixed.parameters=c(N=20000), data=data, verbose=FALSE, model="ML") # Plot the fitted ECF plot(fML, result="ECF") # Plot the Internesting Period distribution plot(fML, result="IP") # Plot the distribution of days between tentatives plot(fML, result="Abort", xlim=c(0, 15)) #' ######### Fit using ML and non parametric ECF par <- c(ECF.2 = 0.044151921569961131, ECF.3 = 2.0020778325280748, ECF.4 = 2.6128345101617083, ECF.5 = 2.6450582416622375, ECF.6 = 2.715198206774927, ECF.7 = 2.0288031327239904, ECF.8 = 1.0028041546528881, ECF.9 = 0.70977432157689235, ECF.10 = 0.086052204035003091, ECF.11 = 0.011400419961702518, ECF.12 = 0.001825219438794076, ECF.13 = 0.00029398731859899116, ECF.14 = 0.002784886479846703, meanIP = 9.9887100433529721, sdIP = 0.10580250625108811, minIP = 6.5159124624132048, pAbort = 2.5702251748938956, meanAbort = 2.2721679285648841, sdAbort = 0.52006431730489933, pCapture = 0.079471782729506113) fML_NP <- IPFit(x=par, fixed.parameters=c(N=20000), data=data, verbose=FALSE, model="ML") par <- fML_NP$ML$par fML_NP <- IPFit(x=par, fixed.parameters=c(N=1000000), data=data, verbose=FALSE, model="ML") par <- c(ECF.2 = 0.016195025683080871, ECF.3 = 2.0858089267994315, ECF.4 = 3.1307578727979348, ECF.5 = 2.7495760827322622, ECF.6 = 2.8770821670450939, ECF.7 = 2.1592708144943145, ECF.8 = 1.0016227335391867, ECF.9 = 0.80990178270345259, ECF.10 = 0.081051214954249967, ECF.11 = 0.039757901443389344, ECF.12 = 6.3324056808464527e-05, ECF.13 = 0.00037500864146146936, ECF.14 = 0.0010383506745475582, meanIP = 10.004121090603523, sdIP = 0.10229422354470977, minIP = 6.5051758088487883, pAbort = 2.5335985958484839, meanAbort = 2.3145895392189173, sdAbort = 0.51192514362374153, pCapture = 0.055440514236842105, DeltameanIP = -0.046478049165483697) fML_NP_Delta <- IPFit(x=par, fixed.parameters=c(N=20000), data=data, verbose=FALSE, model="ML") par <- fML_NP_Delta$ML$par fML_NP_Delta <- IPFit(x=par, fixed.parameters=c(N=1000000), data=data, verbose=FALSE, model="ML") # Test for stability of -Ln L value according to N grandL.mean <- NULL grandL.sd <- NULL N <- c(10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 200000, 300000, 400000, 500000, 600000, 700000, 800000, 900000, 1000000) for (Ni in N) { print(Ni) smallL <- NULL for (replicate in 1:100) { smallL <- c(smallL, getFromNamespace(".IPlnL", ns="phenology") (x=par, fixed.parameters=c(N=Ni), data=data)) } grandL.mean <- c(grandL.mean, mean(smallL)) grandL.sd <- c(grandL.sd, sd(smallL)) } grandL.mean <- c(242.619750064524, 239.596145944548, 238.640010536147, 237.965573853263, 237.727506424543, 237.240740566494, 237.527948232993, 237.297225856515, 237.17073080938, 237.103397800143, 236.855939567838, 236.704861853456, 236.82264801458, 236.606065021519, 236.685930841831, 236.697562908131, 236.568003663293, 236.58097471402, 236.594282543024 ) grandL.sd <- c(6.54334049298099, 3.04916614991682, 2.57932397492509, 2.15990307710982, 1.59826856034413, 1.54505295915354, 1.59734964880484, 1.41845032728396, 1.43096821211286, 1.20048923027244, 0.912467350448495, 0.75814052890774, 0.668841336554019, 0.539505594152166, 0.554662419326559, 0.501551009304687, 0.415199780254872, 0.472274287714195, 0.386237047201706 ) plot_errbar(x=N, y=grandL.mean, errbar.y = 2*grandL.sd, xlab="N", ylab="-Ln L (2 SD)", bty="n", las=1) # Plot the fitted ECF plot(fML_NP_Delta, result="ECF") # Plot the Internesting Period distribution plot(fML_NP_Delta, result="IP") # Plot the distribution of days between tentatives plot(fML_NP_Delta, result="Abort", xlim=c(0, 15)) print(paste("Probability of capture", invlogit(-fML_NP_Delta$ML$par["pCapture"]))) # Confidence interval at 95% print(paste(invlogit(-fML_NP_Delta$ML$par["pCapture"]-1.96*fML_NP_Delta$ML$SE["pCapture"]), "-", invlogit(-fML_NP_Delta$ML$par["pCapture"]+1.96*fML_NP_Delta$ML$SE["pCapture"]))) print(paste("Probability of abort", invlogit(-fML_NP_Delta$ML$par["pAbort"]))) # Confidence interval at 95% print(paste(invlogit(-fML_NP_Delta$ML$par["pAbort"]-1.96*fML_NP_Delta$ML$SE["pAbort"]), "-", invlogit(-fML_NP_Delta$ML$par["pAbort"]+1.96*fML_NP_Delta$ML$SE["pAbort"]))) compare_AIC(parametric=fML$ML, nonparameteric=fML_NP$ML, nonparametricDelta=fML_NP_Delta$ML) ######### Fit using Metropolis-Hastings algorithm # ECF.1 = 1 is fixed par <- c(ECF.2 = 0.044151921569961131, ECF.3 = 2.0020778325280748, ECF.4 = 2.6128345101617083, ECF.5 = 2.6450582416622375, ECF.6 = 2.715198206774927, ECF.7 = 2.0288031327239904, ECF.8 = 1.0028041546528881, ECF.9 = 0.70977432157689235, ECF.10 = 0.086052204035003091, ECF.11 = 0.011400419961702518, ECF.12 = 0.001825219438794076, ECF.13 = 0.00029398731859899116, ECF.14 = 0.002784886479846703, meanIP = 9.9887100433529721, sdIP = 0.10580250625108811, minIP = 6.5159124624132048, pAbort = 2.5702251748938956, meanAbort = 2.2721679285648841, sdAbort = 0.52006431730489933, pCapture = 0.079471782729506113) df <- data.frame(Density=rep("dunif", length(par)), Prior1=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), Prior2=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), SDProp=unname(c(ECF.2 = 6.366805760909012e-05, ECF.3 = 6.366805760909012e-05, ECF.4 = 6.366805760909012e-05, ECF.5 = 6.366805760909012e-05, ECF.6 = 6.366805760909012e-05, ECF.7 = 6.366805760909012e-05, ECF.8 = 6.366805760909012e-05, ECF.9 = 6.366805760909012e-05, ECF.10 = 6.366805760909012e-05, ECF.11 = 6.366805760909012e-05, ECF.12 = 6.366805760909012e-05, ECF.13 = 6.366805760909012e-05, ECF.14 = 6.366805760909012e-05, meanIP = 6.366805760909012e-05, sdIP = 6.366805760909012e-05, minIP = 6.366805760909012e-05, pAbort = 6.366805760909012e-05, meanAbort = 6.366805760909012e-05, sdAbort = 6.366805760909012e-05, pCapture = 6.366805760909012e-05)), Min=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), Max=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), Init=par, stringsAsFactors = FALSE) rownames(df)<- names(par) fMH <- IPFit(parametersMH=df, fixed.parameters=c(N=10000), data=data, verbose=FALSE, n.iter = 10000, n.chains = 1, n.adapt = 100, thin = 1, trace = TRUE, adaptive = TRUE, model="MH") # Plot the fitted ECF plot(fMH, result="ECF") ## End(Not run)## Not run: library(phenology) # Example data <- structure(c(`0` = 0, `1` = 47, `2` = 15, `3` = 6, `4` = 5, `5` = 4, `6` = 2, `7` = 5, `8` = 57, `9` = 203, `10` = 205, `11` = 103, `12` = 35, `13` = 24, `14` = 12, `15` = 10, `16` = 13, `17` = 49, `18` = 86, `19` = 107, `20` = 111, `21` = 73, `22` = 47, `23` = 30, `24` = 19, `25` = 17, `26` = 33, `27` = 48, `28` = 77, `29` = 83, `30` = 65, `31` = 37, `32` = 27, `33` = 23, `34` = 24, `35` = 22, `36` = 41, `37` = 42, `38` = 44, `39` = 33, `40` = 39, `41` = 24, `42` = 18, `43` = 18, `44` = 22, `45` = 22, `46` = 19, `47` = 24, `48` = 28, `49` = 17, `50` = 18, `51` = 19, `52` = 17, `53` = 4, `54` = 12, `55` = 9, `56` = 6, `57` = 11, `58` = 7, `59` = 11, `60` = 12, `61` = 5, `62` = 4, `63` = 6, `64` = 11, `65` = 5, `66` = 6, `67` = 7, `68` = 3, `69` = 2, `70` = 1, `71` = 3, `72` = 2, `73` = 1, `74` = 2, `75` = 0, `76` = 0, `77` = 3, `78` = 1, `79` = 0, `80` = 2, `81` = 0, `82` = 0, `83` = 1), Year = "1994", Species = "Dermochelys coriacea", location = "Yalimapo beach, French Guiana", totalnumber = 2526L, class = "IP") par(mar=c(4, 4, 1, 1)+0.4) plot(data, xlim=c(0,100)) text(100, 190, labels=bquote(italic(.(attributes(data)$Species))), pos=2) text(100, 150, labels=attributes(data)$location, pos=2, cex=0.8) text(100, 110, labels=paste0(as.character(attributes(data)$totalnumber), " females"), pos=2) ######### Fit using Maximum-Likelihood par <- c(meanIP = 9.8229005713237623, sdIP = 0.079176011861863474, minIP = 6.8128364577569309, pAbort = 1.5441529841959203, meanAbort = 2.7958742380756121, sdAbort = 0.99370406770777175, pCapture = -0.80294884905867658, meanECF = 4.5253772889275758, sdECF = 0.20334743335612529) fML <- IPFit(x=par, fixed.parameters=c(N=20000), data=data, verbose=FALSE, model="ML") # Plot the fitted ECF plot(fML, result="ECF") # Plot the Internesting Period distribution plot(fML, result="IP") # Plot the distribution of days between tentatives plot(fML, result="Abort", xlim=c(0, 15)) #' ######### Fit using ML and non parametric ECF par <- c(ECF.2 = 0.044151921569961131, ECF.3 = 2.0020778325280748, ECF.4 = 2.6128345101617083, ECF.5 = 2.6450582416622375, ECF.6 = 2.715198206774927, ECF.7 = 2.0288031327239904, ECF.8 = 1.0028041546528881, ECF.9 = 0.70977432157689235, ECF.10 = 0.086052204035003091, ECF.11 = 0.011400419961702518, ECF.12 = 0.001825219438794076, ECF.13 = 0.00029398731859899116, ECF.14 = 0.002784886479846703, meanIP = 9.9887100433529721, sdIP = 0.10580250625108811, minIP = 6.5159124624132048, pAbort = 2.5702251748938956, meanAbort = 2.2721679285648841, sdAbort = 0.52006431730489933, pCapture = 0.079471782729506113) fML_NP <- IPFit(x=par, fixed.parameters=c(N=20000), data=data, verbose=FALSE, model="ML") par <- fML_NP$ML$par fML_NP <- IPFit(x=par, fixed.parameters=c(N=1000000), data=data, verbose=FALSE, model="ML") par <- c(ECF.2 = 0.016195025683080871, ECF.3 = 2.0858089267994315, ECF.4 = 3.1307578727979348, ECF.5 = 2.7495760827322622, ECF.6 = 2.8770821670450939, ECF.7 = 2.1592708144943145, ECF.8 = 1.0016227335391867, ECF.9 = 0.80990178270345259, ECF.10 = 0.081051214954249967, ECF.11 = 0.039757901443389344, ECF.12 = 6.3324056808464527e-05, ECF.13 = 0.00037500864146146936, ECF.14 = 0.0010383506745475582, meanIP = 10.004121090603523, sdIP = 0.10229422354470977, minIP = 6.5051758088487883, pAbort = 2.5335985958484839, meanAbort = 2.3145895392189173, sdAbort = 0.51192514362374153, pCapture = 0.055440514236842105, DeltameanIP = -0.046478049165483697) fML_NP_Delta <- IPFit(x=par, fixed.parameters=c(N=20000), data=data, verbose=FALSE, model="ML") par <- fML_NP_Delta$ML$par fML_NP_Delta <- IPFit(x=par, fixed.parameters=c(N=1000000), data=data, verbose=FALSE, model="ML") # Test for stability of -Ln L value according to N grandL.mean <- NULL grandL.sd <- NULL N <- c(10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 200000, 300000, 400000, 500000, 600000, 700000, 800000, 900000, 1000000) for (Ni in N) { print(Ni) smallL <- NULL for (replicate in 1:100) { smallL <- c(smallL, getFromNamespace(".IPlnL", ns="phenology") (x=par, fixed.parameters=c(N=Ni), data=data)) } grandL.mean <- c(grandL.mean, mean(smallL)) grandL.sd <- c(grandL.sd, sd(smallL)) } grandL.mean <- c(242.619750064524, 239.596145944548, 238.640010536147, 237.965573853263, 237.727506424543, 237.240740566494, 237.527948232993, 237.297225856515, 237.17073080938, 237.103397800143, 236.855939567838, 236.704861853456, 236.82264801458, 236.606065021519, 236.685930841831, 236.697562908131, 236.568003663293, 236.58097471402, 236.594282543024 ) grandL.sd <- c(6.54334049298099, 3.04916614991682, 2.57932397492509, 2.15990307710982, 1.59826856034413, 1.54505295915354, 1.59734964880484, 1.41845032728396, 1.43096821211286, 1.20048923027244, 0.912467350448495, 0.75814052890774, 0.668841336554019, 0.539505594152166, 0.554662419326559, 0.501551009304687, 0.415199780254872, 0.472274287714195, 0.386237047201706 ) plot_errbar(x=N, y=grandL.mean, errbar.y = 2*grandL.sd, xlab="N", ylab="-Ln L (2 SD)", bty="n", las=1) # Plot the fitted ECF plot(fML_NP_Delta, result="ECF") # Plot the Internesting Period distribution plot(fML_NP_Delta, result="IP") # Plot the distribution of days between tentatives plot(fML_NP_Delta, result="Abort", xlim=c(0, 15)) print(paste("Probability of capture", invlogit(-fML_NP_Delta$ML$par["pCapture"]))) # Confidence interval at 95% print(paste(invlogit(-fML_NP_Delta$ML$par["pCapture"]-1.96*fML_NP_Delta$ML$SE["pCapture"]), "-", invlogit(-fML_NP_Delta$ML$par["pCapture"]+1.96*fML_NP_Delta$ML$SE["pCapture"]))) print(paste("Probability of abort", invlogit(-fML_NP_Delta$ML$par["pAbort"]))) # Confidence interval at 95% print(paste(invlogit(-fML_NP_Delta$ML$par["pAbort"]-1.96*fML_NP_Delta$ML$SE["pAbort"]), "-", invlogit(-fML_NP_Delta$ML$par["pAbort"]+1.96*fML_NP_Delta$ML$SE["pAbort"]))) compare_AIC(parametric=fML$ML, nonparameteric=fML_NP$ML, nonparametricDelta=fML_NP_Delta$ML) ######### Fit using Metropolis-Hastings algorithm # ECF.1 = 1 is fixed par <- c(ECF.2 = 0.044151921569961131, ECF.3 = 2.0020778325280748, ECF.4 = 2.6128345101617083, ECF.5 = 2.6450582416622375, ECF.6 = 2.715198206774927, ECF.7 = 2.0288031327239904, ECF.8 = 1.0028041546528881, ECF.9 = 0.70977432157689235, ECF.10 = 0.086052204035003091, ECF.11 = 0.011400419961702518, ECF.12 = 0.001825219438794076, ECF.13 = 0.00029398731859899116, ECF.14 = 0.002784886479846703, meanIP = 9.9887100433529721, sdIP = 0.10580250625108811, minIP = 6.5159124624132048, pAbort = 2.5702251748938956, meanAbort = 2.2721679285648841, sdAbort = 0.52006431730489933, pCapture = 0.079471782729506113) df <- data.frame(Density=rep("dunif", length(par)), Prior1=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), Prior2=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), SDProp=unname(c(ECF.2 = 6.366805760909012e-05, ECF.3 = 6.366805760909012e-05, ECF.4 = 6.366805760909012e-05, ECF.5 = 6.366805760909012e-05, ECF.6 = 6.366805760909012e-05, ECF.7 = 6.366805760909012e-05, ECF.8 = 6.366805760909012e-05, ECF.9 = 6.366805760909012e-05, ECF.10 = 6.366805760909012e-05, ECF.11 = 6.366805760909012e-05, ECF.12 = 6.366805760909012e-05, ECF.13 = 6.366805760909012e-05, ECF.14 = 6.366805760909012e-05, meanIP = 6.366805760909012e-05, sdIP = 6.366805760909012e-05, minIP = 6.366805760909012e-05, pAbort = 6.366805760909012e-05, meanAbort = 6.366805760909012e-05, sdAbort = 6.366805760909012e-05, pCapture = 6.366805760909012e-05)), Min=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), Max=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), Init=par, stringsAsFactors = FALSE) rownames(df)<- names(par) fMH <- IPFit(parametersMH=df, fixed.parameters=c(N=10000), data=data, verbose=FALSE, n.iter = 10000, n.chains = 1, n.adapt = 100, thin = 1, trace = TRUE, adaptive = TRUE, model="MH") # Plot the fitted ECF plot(fMH, result="ECF") ## End(Not run)
This function fits a model of internesting period.
The parameters are:
meanIP : The average number of days between two nesting processes
DeltameanIP : The shift in days for IP at each new clutch.
sdIP : The standard deviation of number of days between two nesting processes
minIP : The minimum number of days between two nesting processes
pAbort : The -logit of the probability to abort a nesting process
meanAbort : The average of the number of days after the abortion of a nesting process
sdAbort : The standard deviation of the number of days after the abortion of a nesting process
pCapture : The -logit of the probability to capture a female on the beach
meanECF : The average number of clutch a female will try to do being reprensented as ECF
sdECF : The standard deviation of number of clutch a female will try to do
N : The number of replicates to generate the distribution (default is 10000 if not indicated)
ECF.x : The relative proportion of females nesting with ECF = x (ECF.1 being fixed to 1)
IPModel( par, parallel = TRUE, limits = list(meanIP = 40, meanECF = 4, minIP = 15, sdAbort = 1, sdIP = 1, sdECF = 1, DeltameanIP = 0.5, maxDays = 365) )IPModel( par, parallel = TRUE, limits = list(meanIP = 40, meanECF = 4, minIP = 15, sdAbort = 1, sdIP = 1, sdECF = 1, DeltameanIP = 0.5, maxDays = 365) )
par |
Set of parameters |
parallel |
If TRUE, will use parallel computing |
limits |
A list of limits for various parameters |
IPModel estimates the pattern of internesting intervals for a set of parameters.
Return a list with two elements.
Marc Girondot [email protected]
Other Model of Internesting Period:
IPFit(),
IPPredict(),
plot.IP(),
summary.IP()
## Not run: library(phenology) # Example par <- c(meanIP = 9.8, sdIP = 0.1, minIP = 7, pAbort = -logit(0.1), meanAbort = 2, sdAbort = 0.05, pCapture = -logit(0.8), meanECF = 4, sdECF = 0.1) model <- IPModel(c(par, N=10000)) plot(model) ## End(Not run)## Not run: library(phenology) # Example par <- c(meanIP = 9.8, sdIP = 0.1, minIP = 7, pAbort = -logit(0.1), meanAbort = 2, sdAbort = 0.05, pCapture = -logit(0.8), meanECF = 4, sdECF = 0.1) model <- IPModel(c(par, N=10000)) plot(model) ## End(Not run)
This function predicts the possible clutch number
based on observed Internesting Period.
IPPredict(x = NULL, par = NULL, N = NULL, IP = 0:100)IPPredict(x = NULL, par = NULL, N = NULL, IP = 0:100)
x |
A result of IPFit(). |
par |
A set of parameters. |
N |
Number of replicates |
IP |
A vector of Internesting Period |
IPPredict calculates the possible clutch number based on observed Internesting Period.
A data.frame
Marc Girondot
Other Model of Internesting Period:
IPFit(),
IPModel(),
plot.IP(),
summary.IP()
## Not run: library(phenology) # Example ######### Fit using Maximum-Likelihood par <- c(meanIP = 9.9959691992722917, sdIP = 0.10066664270893474, minIP = 7.5684588178888754, pAbort = 2.2510012544630911, meanAbort = 2.8969185085603386, sdAbort = 0.92688983853803242, pCapture = -1.0393803705929086, meanECF = 3.9551519427394255, sdECF = 0.31657679943365019) IPPredict(par=par, IP=c(10, 80)) ## End(Not run)## Not run: library(phenology) # Example ######### Fit using Maximum-Likelihood par <- c(meanIP = 9.9959691992722917, sdIP = 0.10066664270893474, minIP = 7.5684588178888754, pAbort = 2.2510012544630911, meanAbort = 2.8969185085603386, sdAbort = 0.92688983853803242, pCapture = -1.0393803705929086, meanECF = 3.9551519427394255, sdECF = 0.31657679943365019) IPPredict(par=par, IP=c(10, 80)) ## End(Not run)
This function is used to transform a set of parameters that uses Length to a set of parameters uses LengthB and LengthE.
L_to_LBLE(parameters = stop("Set of parameters must be given"))L_to_LBLE(parameters = stop("Set of parameters must be given"))
parameters |
Set of current parameters |
L_to_LBLE transforms a set of parameters from Length format to LengthB LengthE.
Return the set of modified parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Change the parameters to Begin End format parg1<-LBLE_to_L(parameters=parg) # And change back to LengthB LengthE. parg2<-L_to_LBLE(parameters=parg1)# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Change the parameters to Begin End format parg1<-LBLE_to_L(parameters=parg) # And change back to LengthB LengthE. parg2<-L_to_LBLE(parameters=parg1)
This function is used to transform a set of parameters that uses LengthB, Peak and LengthE to a set of parameters that uses Begin, Peak and End.
LBLE_to_BE(parameters = NULL, help = FALSE)LBLE_to_BE(parameters = NULL, help = FALSE)
parameters |
Set of current parameters |
help |
If TRUE, an help is displayed |
LBLE_to_BE transforms a set of parameters from LengthB LengthE to Begin End.
Return a set of modified parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Change the parameters to Begin End format parg1<-LBLE_to_BE(parameters=parg) # And change back to LengthB LengthE parg2<-BE_to_LBLE(parameters=parg1)# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Change the parameters to Begin End format parg1<-LBLE_to_BE(parameters=parg) # And change back to LengthB LengthE parg2<-BE_to_LBLE(parameters=parg1)
This function is used to transform a set of parameters that uses LengthB and LengthE to a set of parameters uses Length.
LBLE_to_L(parameters = stop("Set of parameters must be given"))LBLE_to_L(parameters = stop("Set of parameters must be given"))
parameters |
Set of current parameters |
LBLE_to_L transforms a set of parameters from LengthB LengthE format to Length.
Return the set of modified parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Change the parameters to Begin End format parg1<-LBLE_to_L(parameters=parg) # And change back to LengthB LengthE. parg2<-L_to_LBLE(parameters=parg1)# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Change the parameters to Begin End format parg1<-LBLE_to_L(parameters=parg) # And change back to LengthB LengthE. parg2<-L_to_LBLE(parameters=parg1)
This function is used to estimate the likelihood based on a set of parameters.
likelihood_phenology( data = NULL, fitted.parameters = NULL, fixed.parameters = NULL, parallel = TRUE, result = NULL, model_before = NULL, cofactors = NULL, add.cofactors = NULL, zero = 1e-09, out = TRUE )likelihood_phenology( data = NULL, fitted.parameters = NULL, fixed.parameters = NULL, parallel = TRUE, result = NULL, model_before = NULL, cofactors = NULL, add.cofactors = NULL, zero = 1e-09, out = TRUE )
data |
Dataset generated with add_format |
fitted.parameters |
Set of parameters to be fitted |
fixed.parameters |
Set of fixed parameters |
parallel |
If TRUE, parallel computing is used. |
result |
An object obtained after fit_phenology() |
model_before |
The change of parameters before to estimate daily counts. |
cofactors |
data.frame with a column Date and a column for each cofactor |
add.cofactors |
Names of the column of parameter cofactors to use as a cofactor |
zero |
If the theoretical nest number is under this value, this value wll be used |
out |
If TRUE, return the global likelihood; if FALSE, the likelihood for each series |
likelihood_phenology estimate likelihood for a set of parameters.
The likelihood of the data with the parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: # Read a file with data data(Gratiot) # Generate a formated list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Estimate likelihood with this initial set of parameters likelihood_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # Or directly from a result object likelihood_phenology(result=result_Gratiot) # With new parametrization based on Omeyer et al. (2022) # Omeyer, L. C. M., McKinley, T. J., Bréheret, N., Bal, G., Balchin, G. P., Bitsindou, A., # Chauvet, E., Collins, T., Curran, B. K., Formia, A., Girard, A., Girondot, M., Godley, B. J., # Mavoungou, J.-G., Poli, L., Tilley, D., VanLeeuwe, H. & Metcalfe, K. 2022. Missing data in # sea turtle population monitoring: a Bayesian statistical framework accounting for incomplete # sampling Front. Mar. Sci. (IF 3.661), 9, 817014. parg <- c(tp=unname(parg["Peak"]), tf=unname(parg["Flat"]), s1=unname(parg["LengthB"])/4.8, s2=unname(parg["LengthE"])/4.8, alpha=unname(parg["Max_Complete"]), Theta=unname(parg["Theta"])) likelihood_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run)## Not run: # Read a file with data data(Gratiot) # Generate a formated list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Estimate likelihood with this initial set of parameters likelihood_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # Or directly from a result object likelihood_phenology(result=result_Gratiot) # With new parametrization based on Omeyer et al. (2022) # Omeyer, L. C. M., McKinley, T. J., Bréheret, N., Bal, G., Balchin, G. P., Bitsindou, A., # Chauvet, E., Collins, T., Curran, B. K., Formia, A., Girard, A., Girondot, M., Godley, B. J., # Mavoungou, J.-G., Poli, L., Tilley, D., VanLeeuwe, H. & Metcalfe, K. 2022. Missing data in # sea turtle population monitoring: a Bayesian statistical framework accounting for incomplete # sampling Front. Mar. Sci. (IF 3.661), 9, 817014. parg <- c(tp=unname(parg["Peak"]), tf=unname(parg["Flat"]), s1=unname(parg["LengthB"])/4.8, s2=unname(parg["LengthE"])/4.8, alpha=unname(parg["Max_Complete"]), Theta=unname(parg["Theta"])) likelihood_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run)
Calculate the -log likelihood of data within a model.
lnLCF(x, data, fixed.parameters = NULL, parallel = TRUE, verbose = FALSE)lnLCF(x, data, fixed.parameters = NULL, parallel = TRUE, verbose = FALSE)
x |
A named vector of parameters (mu, sd, mu_season, sd_season, a, p and OTN). |
data |
CMR database formated using TableECFOCF(). |
fixed.parameters |
Parameters that are fixed. |
parallel |
If TRUE, parallel computing in ECFOCF_f is used. |
verbose |
if TRUE, show the parameters. |
lnLCF calculate the -log likelihood of data within a model.
Return the -log likelihood of data within a model.
Marc Girondot
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
generateCF(),
logLik.ECFOCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library(phenology) # Example ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) lnLCF(x=c(mu=4.71768454279272, sd=1.075711951667, p=-1.79746277312909), data=ECFOCF_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(mu1 = 0.6404831115214353, sd1 = 0.69362774786433479, mu2 = 5.6404831115214353, sd2 = 5.69362774786433479, mu_season = 12.6404831115214353, sd_season = 1.69362774786433479, OTN=1) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]:attributes(ECFOCF_2002)$table["final"]]) fixed.parameters <- c(p=-Inf) lnLCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) ## End(Not run)## Not run: library(phenology) # Example ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) lnLCF(x=c(mu=4.71768454279272, sd=1.075711951667, p=-1.79746277312909), data=ECFOCF_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(mu1 = 0.6404831115214353, sd1 = 0.69362774786433479, mu2 = 5.6404831115214353, sd2 = 5.69362774786433479, mu_season = 12.6404831115214353, sd_season = 1.69362774786433479, OTN=1) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]:attributes(ECFOCF_2002)$table["final"]]) fixed.parameters <- c(p=-Inf) lnLCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) ## End(Not run)
Model of remigration interval
The vector of parameters must include:
sx, survival for year x
if s is included, all years have the same survival
tx, Tag retention for year x
rx, probability of return for year x
cx, probability of return for year x
px, probability of observation for year x
LnRI_norm(data, x, kl = NULL)LnRI_norm(data, x, kl = NULL)
data |
Data with remigration intervals |
x |
Vector of parameters |
kl |
Maximum number of years for remigration intervals. |
lnRI_norm returns a ln L
Return a remigration interval.
Marc Girondot
Other Model of Remigration Interval:
Bayesian.remigration(),
RI(),
plot.Remigration()
## Not run: library(phenology) # Example # Each year a fraction of 0.9 is surviving s <- c(s1=0.9, s2=0.9, s3=0.9, s4=0.9, s5=0.9) # Probability of tag retention; 0.95 the first year then after no loss t <- c(t1=0.95, t2=1, t3=1, t4=1, t5=1) # Time-conditional return probability - This is the true remigration rate r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) # Capture probability p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # Number of observations for 400 tagged females after 1, 2, 3, 4, and 5 years OBS <- c(400, 10, 120, 40, 20, 10) # Likelihood of the observed number based on the model LnRI_norm(data=OBS, x = c(s, t, r, p, sd=2) ) LnRI_norm(data=OBS, x = c(s=0.97, t, r, p, sd=2) ) ## End(Not run)## Not run: library(phenology) # Example # Each year a fraction of 0.9 is surviving s <- c(s1=0.9, s2=0.9, s3=0.9, s4=0.9, s5=0.9) # Probability of tag retention; 0.95 the first year then after no loss t <- c(t1=0.95, t2=1, t3=1, t4=1, t5=1) # Time-conditional return probability - This is the true remigration rate r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) # Capture probability p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # Number of observations for 400 tagged females after 1, 2, 3, 4, and 5 years OBS <- c(400, 10, 120, 40, 20, 10) # Likelihood of the observed number based on the model LnRI_norm(data=OBS, x = c(s, t, r, p, sd=2) ) LnRI_norm(data=OBS, x = c(s=0.97, t, r, p, sd=2) ) ## End(Not run)
Return Log Likelihood of a fit generated by fitCF.
## S3 method for class 'ECFOCF' logLik(object, ...)## S3 method for class 'ECFOCF' logLik(object, ...)
object |
A result file generated by fitCF |
... |
Not used |
logLik.ECFOCF return Log Likelihood of a fit done using fitCF
The Log Likelihood value for the fitted model with data
Marc Girondot
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
generateCF(),
lnLCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library(phenology) # Example ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) par <- c(mu = 2.6404831115214353, size = 0.69362774786433479, mu_season = 12.6404831115214353, size_season = 1.69362774786433479, a2=0) fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]:attributes(ECFOCF_2002)$table["final"]]) fixed.parameters <- c(a1=Inf, p=-Inf) lnLCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) o_mu1season1a2p <- fitCF(x=par, fixed.parameters=fixed.parameters, data=ECFOCF_2002) logLik(o_mu1season1a2p) AIC(o_mu1season1a2p) ## End(Not run)## Not run: library(phenology) # Example ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) par <- c(mu = 2.6404831115214353, size = 0.69362774786433479, mu_season = 12.6404831115214353, size_season = 1.69362774786433479, a2=0) fp <- rep(0, dim(ECFOCF_2002)[3]) names(fp) <- paste0("p.", formatC(1:(dim(ECFOCF_2002)[3]), width=2, flag="0")) par <- c(par, fp[attributes(ECFOCF_2002)$table["begin"]:attributes(ECFOCF_2002)$table["final"]]) fixed.parameters <- c(a1=Inf, p=-Inf) lnLCF(x=par, data=ECFOCF_2002, fixed.parameters=fixed.parameters) o_mu1season1a2p <- fitCF(x=par, fixed.parameters=fixed.parameters, data=ECFOCF_2002) logLik(o_mu1season1a2p) AIC(o_mu1season1a2p) ## End(Not run)
Return Log Likelihood of a fit generated by fitRMU
## S3 method for class 'fitRMU' logLik(object, ...)## S3 method for class 'fitRMU' logLik(object, ...)
object |
A result file generated by fitRMU |
... |
Not used |
logLik.fitRMU Return Log Likelihood of a fit for fitRMU
The Log Likelihood value for the fitted model with data
Marc Girondot
Other Fill gaps in RMU:
CI.RMU(),
fitRMU(),
fitRMU_MHmcmc(),
fitRMU_MHmcmc_p(),
plot.fitRMU()
## Not run: library(phenology) RMU.name.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20)) cst <- fitRMU(data=data.AtlanticW, RMU.name=RMU.name.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") logLik(cst) AIC(cst) ## End(Not run)## Not run: library(phenology) RMU.name.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana")) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20)) cst <- fitRMU(data=data.AtlanticW, RMU.name=RMU.name.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") logLik(cst) AIC(cst) ## End(Not run)
Return Log Likelihood of a fit generated by fit_phenology
## S3 method for class 'phenology' logLik(object, ...)## S3 method for class 'phenology' logLik(object, ...)
object |
A result file generated by fit_phenology |
... |
Not used |
logLik.phenology Return Log Likelihood of a fit
The Log Likelihood value of the fitted model and data
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) data(result_Gratiot) logLik(result_Gratiot) AIC(result_Gratiot) ## End(Not run)## Not run: library(phenology) data(result_Gratiot) logLik(result_Gratiot) AIC(result_Gratiot) ## End(Not run)
Return Log Likelihood of a fit generated by Tagloss_fit
## S3 method for class 'Tagloss' logLik(object, ...)## S3 method for class 'Tagloss' logLik(object, ...)
object |
A result file generated by Tagloss_fit |
... |
Not used |
logLik.Tagloss returns Log Likelihood of a fit for tag loss
The Log Likelihood value for the fitted model with data
Marc Girondot
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- structure(c(48.8292784204825, 1039.02842229274, -89.3162940697861, 5.21817463244988, 8.00575451188548, 8.32971268127933, 161.265553603601, 602.935748681661, 2643.57415102633, 16.752815732218, 10.181616195839, 7.14279063312016), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par) logLik(o) AIC(o) ## End(Not run)## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- structure(c(48.8292784204825, 1039.02842229274, -89.3162940697861, 5.21817463244988, 8.00575451188548, 8.32971268127933, 161.265553603601, 602.935748681661, 2643.57415102633, 16.752815732218, 10.181616195839, 7.14279063312016), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par) logLik(o) AIC(o) ## End(Not run)
Likelihood map of Leatherback nest counts from Gratiot et al. (2006) Figure 1. A intraseasonal periodic pattern was searched for varying Phi and Delta parameters.
map_Gratiotmap_Gratiot
A list with Gratiot data and the result of the fit.
Likelihood map of Leatherback nest counts from Gratiot et al. (2006) Figure 1
Marc Girondot [email protected]
Gratiot, N., Gratiot, J., de Thoisy, B. & Kelle, L. 2006. Estimation of marine turtles nesting season from incomplete data ; statistical adjustment of a sinusoidal function. Animal Conservation, 9, 95-102.
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Read a file with likelihood map data(map_Gratiot)library(phenology) # Read a file with likelihood map data(map_Gratiot)
This function generates a map of likelihood varying Phi and Delta.
Parameters are the same than for the fit_phenology() function except for trace that is disabled.
If Alpha, Beta or Tau are not indicated, Alpha and Tau are set to 0 and 1 and Beta is fitted.
Only one set of Alpha, Beta, Tau, Phi and Delta are used for all timeseries present in data.
Note that it is possible to fit or fixed Alpha[n], Beta[n], Tau[n], Phi[n] and Delta[n] with [n]=1 or 2
and then it is possible to use this function to establish the likelihood map for a
second or third sinusoids added to the global pattern.
If Delta is not specified, it is estimated from Phi and the same precision as Phi is used.
map_phenology( data = NULL, fitted.parameters = NULL, fixed.parameters = NA, Phi = seq(from = 0.2, to = 20, length.out = 100), Delta = NULL, progressbar = any(installed.packages()[, "Package"] == "pbapply"), cofactors = NULL, add.cofactors = NULL, zero = 1e-09 )map_phenology( data = NULL, fitted.parameters = NULL, fixed.parameters = NA, Phi = seq(from = 0.2, to = 20, length.out = 100), Delta = NULL, progressbar = any(installed.packages()[, "Package"] == "pbapply"), cofactors = NULL, add.cofactors = NULL, zero = 1e-09 )
data |
dataset generated with add_format |
fitted.parameters |
Set of parameters to be fitted |
fixed.parameters |
Set of fixed parameters |
Phi |
Phi values to be analyzed |
Delta |
Delta value to be analyzed |
progressbar |
If FALSE, do not show the progress bar |
cofactors |
data.frame with a column Date and a column for each cofactor |
add.cofactors |
Names of the column of parameter cofactors to use as a cofactor |
zero |
If the theoretical nest number is under this value, this value wll be used |
map_phenology generates a likelihood map.
Display a likelihood map
Marc Girondot [email protected]
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name = "Complete", reference = as.Date("2001-01-01"), format = "%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters = NULL) # Run the optimisation ## Not run: result_Gratiot <- fit_phenology(data = data_Gratiot, fitted.parameters = parg, fixed.parameters = NULL) ## End(Not run) data(result_Gratiot) # Extract the fitted parameters parg1 <- extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed <- c(parg1, Alpha=0, Tau=1) pfixed <- pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2 <- c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result ## Not run: library(phenology) map_Gratiot <- map_phenology(data = data_Gratiot, Phi = seq(from=0.1, to=30, length.out=100), fitted.parameters = parg2, fixed.parameters = pfixed) ## End(Not run) data(map_Gratiot) # Plot the map plot(map_Gratiot, col = heat.colors(128)) # Plot the min(-Ln L) for Phi varying at any delta value plot_phi(map = map_Gratiot) # Plot the min(-Ln L) for Delta varying with Phi equal to the value for maximum likelihood plot_delta(map = map_Gratiot) # Plot the min(-Ln L) for Delta varying with Phi the nearest to 15 plot_delta(map = map_Gratiot, Phi = 15)library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name = "Complete", reference = as.Date("2001-01-01"), format = "%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters = NULL) # Run the optimisation ## Not run: result_Gratiot <- fit_phenology(data = data_Gratiot, fitted.parameters = parg, fixed.parameters = NULL) ## End(Not run) data(result_Gratiot) # Extract the fitted parameters parg1 <- extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed <- c(parg1, Alpha=0, Tau=1) pfixed <- pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2 <- c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result ## Not run: library(phenology) map_Gratiot <- map_phenology(data = data_Gratiot, Phi = seq(from=0.1, to=30, length.out=100), fitted.parameters = parg2, fixed.parameters = pfixed) ## End(Not run) data(map_Gratiot) # Plot the map plot(map_Gratiot, col = heat.colors(128)) # Plot the min(-Ln L) for Phi varying at any delta value plot_phi(map = map_Gratiot) # Plot the min(-Ln L) for Delta varying with Phi equal to the value for maximum likelihood plot_delta(map = map_Gratiot) # Plot the min(-Ln L) for Delta varying with Phi the nearest to 15 plot_delta(map = map_Gratiot, Phi = 15)
Extraction of 2002 PIT tagged marine turtles database
MarineTurtles_2002MarineTurtles_2002
data.frame with 2 columns:
Date: The date the female has been seen on the beach (morning date of the night)
ID: The unique identifier of the female
Database of tagged marine turtles in 2002
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) data(MarineTurtles_2002)library(phenology) data(MarineTurtles_2002)
This function is used to transform a set of parameters that uses MinB and MinE to a set of parameters that uses Min.
MinBMinE_to_Min(parameters = stop("A set of parameters must be indicated"))MinBMinE_to_Min(parameters = stop("A set of parameters must be indicated"))
parameters |
Set of current parameters |
MinBMinE_to_Min transforms a set of parameters from MinB and MinE to Min
Return a set of modified parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot) # Change the parameters to PMinB and PMinE parg1<-MinBMinE_to_Min(parameters=parg)# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot) # Change the parameters to PMinB and PMinE parg1<-MinBMinE_to_Min(parameters=parg)
Model of tagloss based on Rivalan data
o_4p_p1p2o_4p_p1p2
Model of tagloss based on Rivalan data
Marc Girondot
Rivalan, P., Godfrey, M.H., Prévot-Julliard, A.-C., Girondot, M., 2005. Maximum likelihood estimates of tag loss in leatherback sea turtles. Journal of Wildlife Management 69, 540-548.
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # Same data fitted with new model par <- c(D1_1 = 100.15324837975547, A_1 = 5.9576927964120188, B_1 = 8.769924225871069, B_2 = 8.2353860179664125) pfixed <- c(D2D1_1 = 2568, D3D2_1 = 2568, D2D1_2 = 2568, D3D2_2 = 2568) o_4p_p1p2 <- Tagloss_fit(data=data_f_21, fitted.parameters = par, fixed.parameters = pfixed, model_before = "par['C_1']=par['B_1']; par['A_2']=par['A_1']; par['C_2']=par['B_2']; par['D1_2']=par['D1_1']", hessian=TRUE) data(o_4p_p1p2) plot(o_4p_p1p2, model="1", col="red") plot(o_4p_p1p2, model="2", col="blue", add=TRUE) legend("topright", legend=c("2->1", "1->0"), lty=1, col=c("blue", "red")) plot(o_4p_p1p2, model="Cumul") ## End(Not run)## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # Same data fitted with new model par <- c(D1_1 = 100.15324837975547, A_1 = 5.9576927964120188, B_1 = 8.769924225871069, B_2 = 8.2353860179664125) pfixed <- c(D2D1_1 = 2568, D3D2_1 = 2568, D2D1_2 = 2568, D3D2_2 = 2568) o_4p_p1p2 <- Tagloss_fit(data=data_f_21, fitted.parameters = par, fixed.parameters = pfixed, model_before = "par['C_1']=par['B_1']; par['A_2']=par['A_1']; par['C_2']=par['B_2']; par['D1_2']=par['D1_1']", hessian=TRUE) data(o_4p_p1p2) plot(o_4p_p1p2, model="1", col="red") plot(o_4p_p1p2, model="2", col="blue", add=TRUE) legend("topright", legend=c("2->1", "1->0"), lty=1, col=c("blue", "red")) plot(o_4p_p1p2, model="Cumul") ## End(Not run)
Database of leatherback CMR in French Guiana
outLRoutLR
A dataframe with database of leatherback CMR in French Guiana
Database of leatherback CMR in French Guiana
Marc Girondot [email protected]
Rivalan, P., Godfrey, M.H., Prévot-Julliard, A.-C., Girondot, M., 2005. Maximum likelihood estimates of tag loss in leatherback sea turtles. Journal of Wildlife Management 69, 540-548.
## Not run: library(phenology) # Read a file with result data(outLR) data_f_21 <- Tagloss_format(outLR, model="21") ## End(Not run)## Not run: library(phenology) # Read a file with result data(outLR) data_f_21 <- Tagloss_format(outLR, model="21") ## End(Not run)
This function is used to generate an initial set of parameters for fitting
that is expected to be not to far from the final.
The parameters can be:
Min, MinE, MinB, PMin, PMinB, PMinE;
Max;
Begin, Peak, Flat, End;
Length, LengthB, LengthE;
Theta;
Alpha, Beta, Tau, Phi, Delta;
Alpha1, Beta1, Tau1, Phi1, Delta1;
Alpha2, Beta2, Tau2, Phi2, Delta2;
Alpha3, Beta3, Tau3, Phi3, Delta3;
And the name of level if a cofactor is used.
The parameters Max, Min, MinE, MinB, Length,
LengthB, LengthE, and Peak can be followed with _ and part of the
name of the rookery.
The model for scale effect of sinusoid is: Alpha + Beta * n(t) ^ Tau where
n(t) is the expected number for the day t without the sinusoid effect.
par_init( data = stop("A dataset must be provided"), fixed.parameters = NULL, add.cofactors = NULL )par_init( data = stop("A dataset must be provided"), fixed.parameters = NULL, add.cofactors = NULL )
data |
Dataset generated with add_phenology() |
fixed.parameters |
Set of fixed parameters |
add.cofactors |
Names of cofactors that will be used (see fit_phenology) |
par_init calculates initial set of parameters.
The initial set of parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output<-plot(result_Gratiot) ## When a series has only 0, it should be used in two steps ## Let see an example # Let create a times series with only 0 data0 <- data.frame(Date=c("11/3/2015", "12/3/2015", "13/3/2015-18/3/2015", "25/3/2015"), Number=c(0, 0, 0, 0), Beach=rep("Site0", 4), stringsAsFactors=FALSE) data1 <- data.frame(Date=c("15/3/2015", "16/3/2015", "20/3/2015-22/3/2015", "25/3/2015"), Number=c(1, 0, 3, 0), Beach=rep("Site1", 4), stringsAsFactors=FALSE) data <- rbind(data0, data1) # Here I include timeseries with no observation try1 <- add_phenology(data, format="%d/%m/%Y", month_ref=1, include0=TRUE) pfixed <- c(Min=0, Flat=0) parg <- par_init(try1, fixed.parameters=pfixed) # The Max value for the series without observations should not be fitted. The ML is for Max being 0 pfixed <- c(pfixed, parg[(substr(names(parg), 1, 4)=="Max_") & (parg == 0)]) parg <- parg[!(names(parg) %in% names(pfixed))] ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output<-plot(result_Gratiot) ## When a series has only 0, it should be used in two steps ## Let see an example # Let create a times series with only 0 data0 <- data.frame(Date=c("11/3/2015", "12/3/2015", "13/3/2015-18/3/2015", "25/3/2015"), Number=c(0, 0, 0, 0), Beach=rep("Site0", 4), stringsAsFactors=FALSE) data1 <- data.frame(Date=c("15/3/2015", "16/3/2015", "20/3/2015-22/3/2015", "25/3/2015"), Number=c(1, 0, 3, 0), Beach=rep("Site1", 4), stringsAsFactors=FALSE) data <- rbind(data0, data1) # Here I include timeseries with no observation try1 <- add_phenology(data, format="%d/%m/%Y", month_ref=1, include0=TRUE) pfixed <- c(Min=0, Flat=0) parg <- par_init(try1, fixed.parameters=pfixed) # The Max value for the series without observations should not be fitted. The ML is for Max being 0 pfixed <- c(pfixed, parg[(substr(names(parg), 1, 4)=="Max_") & (parg == 0)]) parg <- parg[!(names(parg) %in% names(pfixed))] ## End(Not run)
This function is used to transform a set of parameters
that uses Peak, LengthB, LengthE, B, E, or Length to the same parameter
with Year effect, or reverse.
The parameter series can be or a result from add_phenology() or from
fit_phenology() or simply a vector of names.
If this is a vector of names, it is checked to remove _ characters.
Parameter_Global_Year( parameters = stop("A set of parameters must be indicated"), parname = c("Peak", "LengthB", "LengthE", "B", "E", "Length"), series = NULL, sep_year = "-", perYear = TRUE )Parameter_Global_Year( parameters = stop("A set of parameters must be indicated"), parname = c("Peak", "LengthB", "LengthE", "B", "E", "Length"), series = NULL, sep_year = "-", perYear = TRUE )
parameters |
Set of current parameters |
parname |
Name of parameter to transform |
series |
Set of series (see description) |
sep_year |
Character used to separate the year |
perYear |
TRUE if year-specific values must be setup |
Parameter_Global_Year transforms a set of parameters from Year to global effect, or reverse
Return a set of modified parameters
Marc Girondot [email protected]
## Not run: Parameter_Global_Year(parameters=c("Peak_Beach1-2018"=151, "Peak_Beach1-2019"=161), parname="Peak", perYear=FALSE) Parameter_Global_Year(parameters=c("Peak"=151), series = c("beach1", "beach2"), parname="Peak", perYear=TRUE) ## End(Not run)## Not run: Parameter_Global_Year(parameters=c("Peak_Beach1-2018"=151, "Peak_Beach1-2019"=161), parname="Peak", perYear=FALSE) Parameter_Global_Year(parameters=c("Peak"=151), series = c("beach1", "beach2"), parname="Peak", perYear=TRUE) ## End(Not run)
Run a shiny application for basic functions of phenology package. Thanks to Adriana Cortés Gomés and Joana Hancock for their help with translation.
phenology()phenology()
phenology runs a shiny application for basic functions of phenology package
Nothing
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) phenology() ## End(Not run)## Not run: library(phenology) phenology() ## End(Not run)
Run the Metropolis-Hastings algorithm for data.
The number of iterations is n.iter+n.adapt+1 because the initial likelihood is also displayed.
I recommend thin=10.
If initial point is maximum likelihood, n.adapt = 0 is a good solution.
The parameters intermediate and filename are used to save intermediate results every 'intermediate' iterations (for example 1000). Results are saved in a file of name filename.
The parameter previous is used to indicate the list that has been save using the parameters intermediate and filename. It permits to continue a mcmc search.
These options are used to prevent the consequences of computer crash or if the run is very very long and computer processes are time limited.
phenology_MHmcmc( result = stop("An output from fit_phenology() must be provided"), n.iter = 10000, parametersMCMC = stop("A model generated with phenology_MHmcmc_p() must be provided"), n.chains = 1, n.adapt = 1000, thin = 1, trace = FALSE, traceML = FALSE, adaptive = TRUE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )phenology_MHmcmc( result = stop("An output from fit_phenology() must be provided"), n.iter = 10000, parametersMCMC = stop("A model generated with phenology_MHmcmc_p() must be provided"), n.chains = 1, n.adapt = 1000, thin = 1, trace = FALSE, traceML = FALSE, adaptive = TRUE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )
result |
An object obtained after a SearchR fit |
n.iter |
Number of iterations for each step |
parametersMCMC |
A set of parameters used as initial point for searching with information on priors |
n.chains |
Number of replicates |
n.adapt |
Number of iterations before to store outputs |
thin |
Number of iterations between each stored output |
trace |
TRUE or FALSE or period, shows progress |
traceML |
TRUE or FALSE to show ML |
adaptive |
Should an adaptive process for SDProp be used |
adaptive.lag |
Lag to analyze the SDProp value in an adaptive content |
adaptive.fun |
Function used to change the SDProp |
intermediate |
Period for saving intermediate result, NULL for no save |
filename |
If intermediate is not NULL, save intermediate result in this file |
previous |
Previous result to be continued. Can be the filename in which intermediate results are saved. |
phenology_MHmcmc runs the Metropolis-Hastings algorithm for data (Bayesian MCMC)
A list with resultMCMC being mcmc.list object, resultLnL being likelihoods and parametersMCMC being the parameters used
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) data(Gratiot) # Generate a formatted list named data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # Generate set of priors for Bayesian analysis pmcmc <- phenology_MHmcmc_p(result_Gratiot, accept = TRUE) result_Gratiot_mcmc <- phenology_MHmcmc(result = result_Gratiot, n.iter = 10000, parametersMCMC = pmcmc, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) # Get standard error of parameters summary(result_Gratiot_mcmc) # Make diagnostics of the mcmc results using coda package mcmc <- as.mcmc(result_Gratiot_mcmc) require(coda) heidel.diag(mcmc) raftery.diag(mcmc) autocorr.diag(mcmc) acf(mcmc[[1]][,"LengthB"], lag.max=200, bty="n", las=1) acf(mcmc[[1]][,"Max_Gratiot"], lag.max=50, bty="n", las=1) batchSE(mcmc, batchSize=100) # The batch standard error procedure is usually thought to # be not as accurate as the time series methods used in summary summary(mcmc)$statistics[,"Time-series SE"] plot(result_Gratiot_mcmc, parameters=3, las=1, xlim=c(-10, 300)) ## End(Not run)## Not run: library(phenology) data(Gratiot) # Generate a formatted list named data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # Generate set of priors for Bayesian analysis pmcmc <- phenology_MHmcmc_p(result_Gratiot, accept = TRUE) result_Gratiot_mcmc <- phenology_MHmcmc(result = result_Gratiot, n.iter = 10000, parametersMCMC = pmcmc, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) # Get standard error of parameters summary(result_Gratiot_mcmc) # Make diagnostics of the mcmc results using coda package mcmc <- as.mcmc(result_Gratiot_mcmc) require(coda) heidel.diag(mcmc) raftery.diag(mcmc) autocorr.diag(mcmc) acf(mcmc[[1]][,"LengthB"], lag.max=200, bty="n", las=1) acf(mcmc[[1]][,"Max_Gratiot"], lag.max=50, bty="n", las=1) batchSE(mcmc, batchSize=100) # The batch standard error procedure is usually thought to # be not as accurate as the time series methods used in summary summary(mcmc)$statistics[,"Time-series SE"] plot(result_Gratiot_mcmc, parameters=3, las=1, xlim=c(-10, 300)) ## End(Not run)
Interactive script used to generate set of parameters to be used with phenology_MHmcmc().
phenology_MHmcmc_p( result = stop("An output from fit_phenology() must be provided"), default.density = "dunif", accept = FALSE )phenology_MHmcmc_p( result = stop("An output from fit_phenology() must be provided"), default.density = "dunif", accept = FALSE )
result |
An object obtained after a fit_phenology() fit |
default.density |
The default density, "dnorm" or "dunif' |
accept |
If TRUE, does not wait for use interaction |
phenology_MHmcmc_p generates set of parameters to be used with phenology_MHmcmc()
A matrix with the parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) data(Gratiot) # Generate a formatted list named data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # Generate set of priors for Bayesian analysis pmcmc <- phenology_MHmcmc_p(result_Gratiot, accept = TRUE) result_Gratiot_mcmc <- phenology_MHmcmc(result = result_Gratiot, n.iter = 10000, parametersMCMC = pmcmc, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) # Get standard error of parameters summary(result_Gratiot_mcmc) # Make diagnostics of the mcmc results using coda package mcmc <- as.mcmc(result_Gratiot_mcmc) require(coda) heidel.diag(mcmc) raftery.diag(mcmc) autocorr.diag(mcmc) acf(mcmc[[1]][,"LengthB"], lag.max=200, bty="n", las=1) acf(mcmc[[1]][,"Max_Gratiot"], lag.max=50, bty="n", las=1) batchSE(mcmc, batchSize=100) # The batch standard error procedure is usually thought to # be not as accurate as the time series methods used in summary summary(mcmc)$statistics[,"Time-series SE"] plot(result_Gratiot_mcmc, parameters=3, las=1, xlim=c(-10, 300)) ## End(Not run)## Not run: library(phenology) data(Gratiot) # Generate a formatted list named data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # Generate set of priors for Bayesian analysis pmcmc <- phenology_MHmcmc_p(result_Gratiot, accept = TRUE) result_Gratiot_mcmc <- phenology_MHmcmc(result = result_Gratiot, n.iter = 10000, parametersMCMC = pmcmc, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) # Get standard error of parameters summary(result_Gratiot_mcmc) # Make diagnostics of the mcmc results using coda package mcmc <- as.mcmc(result_Gratiot_mcmc) require(coda) heidel.diag(mcmc) raftery.diag(mcmc) autocorr.diag(mcmc) acf(mcmc[[1]][,"LengthB"], lag.max=200, bty="n", las=1) acf(mcmc[[1]][,"Max_Gratiot"], lag.max=50, bty="n", las=1) batchSE(mcmc, batchSize=100) # The batch standard error procedure is usually thought to # be not as accurate as the time series methods used in summary summary(mcmc)$statistics[,"Time-series SE"] plot(result_Gratiot_mcmc, parameters=3, las=1, xlim=c(-10, 300)) ## End(Not run)
This function takes the result of plot.phenology() and generates the information
to be used with fitRMU().
The value of density can be dnorm or dgamma. dnorm is better if ML results are used and
dgamma is for MCMC.
Here are some example of regular expressions (regex) in grep:
If format of timeseries is beachname-2005:
rookeries.names.grep="(.+)-.+"
years.grep=".+-(.+)$"
Examples:
gsub("(.+)-.+", "\\1", "beachname-2005"); gsub(".+-(.+)$", "\\1", "beachname-2005")
If format of timeseries is beachname-2005-2006:
rookeries.names.grep="(.+)-.+-.+"
years.grep=".+-([0-9][0-9][0-9][0-9])-.+$"
Examples:
gsub("(.+)-.+-.+", "\\1", "beachname-2005-2006");
gsub(".+-([0-9][0-9][0-9][0-9])-.+$", "\\1", "beachname-2005-2006")
If format of timeseries is beachname-20052006:
rookeries.names.grep="(.+)-.+"
years.grep=".+-([0-9][0-9][0-9][0-9])([0-9][0-9][0-9][0-9])$"
Examples:
gsub("(.+)-.+", "\\1", "beachname-20052006");
gsub(".+-([0-9][0-9][0-9][0-9])([0-9][0-9][0-9][0-9])$", "\\1", "beachname-20052006")
The return is a list with these elements:
RMU.data, years.byrow, colname.year, and RMU.names.
If density is a vector, the density used is linked to the rank of the timeseries in phenologyout.
phenology2fitRMU( phenologyout = stop("A result obtained from summary(phenology)"), col.mean = "with_obs_Mean_ML", col.var = "with_obs_Var_ML", rookeries.names.grep = "(.+)-.+", years.grep = ".+-(.+)$", limit.cv = +Inf, limit.sd = +Inf, density = "dgamma" )phenology2fitRMU( phenologyout = stop("A result obtained from summary(phenology)"), col.mean = "with_obs_Mean_ML", col.var = "with_obs_Var_ML", rookeries.names.grep = "(.+)-.+", years.grep = ".+-(.+)$", limit.cv = +Inf, limit.sd = +Inf, density = "dgamma" )
phenologyout |
A result of plot.phenology() or summary() |
col.mean |
Name of the column to be used as mean value. Can be a vector. |
col.var |
Name of the column to be used as variance value. Can be a vector. |
rookeries.names.grep |
The pattern to return the rookery name from names of timeseries. |
years.grep |
The pattern to return the year from names of timeseries. |
limit.cv |
Remove data with higher coefficient of variation than the limit. |
limit.sd |
Remove data with higher standard deviation than the limit. |
density |
What density should be used. Can be dnorm or dgamma. Can be a vector. |
phenology2fitRMU is used to prepare output of phenology to be used by fitRMU()
Return a list with elements ready to be used with fitRMU().
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library("phenology") ## End(Not run)## Not run: library("phenology") ## End(Not run)
This function plots a likelihood lineplot obtained after map_phenology.
plot_delta(map = NULL, Phi = NULL, ...)plot_delta(map = NULL, Phi = NULL, ...)
map |
A map generated with map_phenology |
Phi |
Phi value or NULL |
... |
Parameters for plot |
plot_delta plots the likelihood delta for fixed Phi value.
Return None
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) data(map_Gratiot) # Plot the min(-Ln L) for Delta varying with Phi equal to the value for maximum likelihood plot_delta(map=map_Gratiot) # Plot the min(-Ln L) for Delta varying with Phi the nearest to 15 plot_delta(map=map_Gratiot, Phi=15) ## End(Not run)## Not run: library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) data(map_Gratiot) # Plot the min(-Ln L) for Delta varying with Phi equal to the value for maximum likelihood plot_delta(map=map_Gratiot) # Plot the min(-Ln L) for Delta varying with Phi the nearest to 15 plot_delta(map=map_Gratiot, Phi=15) ## End(Not run)
The function "plot_phi" plots the best likelihood for each Phi value.
plot_phi(map = NULL, ...)plot_phi(map = NULL, ...)
map |
A map generated with map_phenology |
... |
Parameters for plot |
plot_phi plots the best likelihood for fixed Phi value.
Return None
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result ## Not run: map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) ## End(Not run) data(map_Gratiot) # Plot the min(-Ln L) for Phi varying at any delta value plot_phi(map=map_Gratiot)library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result ## Not run: map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) ## End(Not run) data(map_Gratiot) # Plot the min(-Ln L) for Phi varying at any delta value plot_phi(map=map_Gratiot)
This function plots the result of fitCF().
The result data plots the observed ECF-OCF table.
The result dataOCF plots the observed OCF table.
The result dataECF plots the observed ECF table.
The result CF plots the true clutch frequency.
The result OCF plots the observed clutch frequency.
The result ECF plots the estimated clutch frequency.
The result ECFOCF plots the bivariate observed vs. estimated clutch frequency.
The result ECFOCF0 plots the bivariate observed vs. estimated clutch frequency without the 0 OCF.
The result prob plots the probabilities of capture.
The result period plots the probabilities of nesting according to period.
If category is left to NA, the compound value for all the population is plotted.
When result="data" is used, this is a parser for plot.TableECFOCF().
See this function for the parameters.
The parameter y.axis is the shift of the x legends for result="prob".
When a resultMCMC is indicated, if replicates is "all", all values are used;
if a value lower than number of iterations is indicated, a regular thinning is used and
if a value larger then number if iteration is indicated, a sampling with replacement is used.
## S3 method for class 'ECFOCF' plot( x, ..., result = "CF", category = NA, period = 1, resultMCMC = NULL, chain = 1, replicates = "all" )## S3 method for class 'ECFOCF' plot( x, ..., result = "CF", category = NA, period = 1, resultMCMC = NULL, chain = 1, replicates = "all" )
x |
A result for fitCF(). |
... |
Graphic parameters, see plot.TableECFOCF() or par. |
result |
What result will be plotted: data, dataOCF, dataECF, ECF, OCF, ECFOCF, ECFOCF0, CF, Prob, period |
category |
What category will be plotted, numeric or NA for all. |
period |
The period that will be plotted. |
resultMCMC |
A result from fitRMU_MHmcmc. |
chain |
Which chain to be used in resultMCMC. |
replicates |
How many replicates fron resultMCMC. |
plot.ECFOCF plots a result of clutch frequency fit.
Nothing
Marc Girondot
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
generateCF(),
lnLCF(),
logLik.ECFOCF(),
plot.TableECFOCF()
## Not run: library(phenology) # Example data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) o_mu1p2_NB <- fitCF(x = c(mu = 4.6426989650675701, sd = 75.828239144717074, p1 = 0.62036295627161053, p2 = -2.3923021862881511, OTN = 0.33107456308054345), data=ECFOCF_2002) par(mar=c(4, 4, 1, 1)+0.4) plot(o_mu1p2_NB, result="data", category=NA, bty="n", las=1, cex.points=3, cex.axis = 0.8) plot(o_mu1p2_NB,result="data", category=NA, bty="n", las=1, cex.points=3, pch=NA, col.labels = "red", show.labels=TRUE, cex.0=0.2, show.0 = TRUE, col.0="blue", pch.0=4) plot(o_mu1p2_NB, result="dataOCF", category=NA, bty="n", las=1) plot(o_mu1p2_NB, result="dataECF", category=NA, bty="n", las=1) plot(o_mu1p2_NB, result="CF", bty="n", las=1) plot(o_mu1p2_NB, result="OCF", category=1, bty="n", las=1) plot(o_mu1p2_NB, result="OCF", category=2, bty="n", las=1) plot(o_mu1p2_NB, result="ECFOCF", bty="n", las=1) plot(o_mu1p2_NB, result="ECFOCF0", bty="n", las=1) plot(o_mu1p2_NB, result="ECFOCF0", category=1, bty="n", las=1) plot(o_mu1p2_NB, result="ECFOCF0", category=2, bty="n", las=1) plot(o_mu1p2_NB, result="Prob", category=c(1, 2), bty="n", las=1) plot(o_mu1p2_NB, result="Prob", category=c(2, 1), bty="n", las=1) ## End(Not run)## Not run: library(phenology) # Example data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) o_mu1p2_NB <- fitCF(x = c(mu = 4.6426989650675701, sd = 75.828239144717074, p1 = 0.62036295627161053, p2 = -2.3923021862881511, OTN = 0.33107456308054345), data=ECFOCF_2002) par(mar=c(4, 4, 1, 1)+0.4) plot(o_mu1p2_NB, result="data", category=NA, bty="n", las=1, cex.points=3, cex.axis = 0.8) plot(o_mu1p2_NB,result="data", category=NA, bty="n", las=1, cex.points=3, pch=NA, col.labels = "red", show.labels=TRUE, cex.0=0.2, show.0 = TRUE, col.0="blue", pch.0=4) plot(o_mu1p2_NB, result="dataOCF", category=NA, bty="n", las=1) plot(o_mu1p2_NB, result="dataECF", category=NA, bty="n", las=1) plot(o_mu1p2_NB, result="CF", bty="n", las=1) plot(o_mu1p2_NB, result="OCF", category=1, bty="n", las=1) plot(o_mu1p2_NB, result="OCF", category=2, bty="n", las=1) plot(o_mu1p2_NB, result="ECFOCF", bty="n", las=1) plot(o_mu1p2_NB, result="ECFOCF0", bty="n", las=1) plot(o_mu1p2_NB, result="ECFOCF0", category=1, bty="n", las=1) plot(o_mu1p2_NB, result="ECFOCF0", category=2, bty="n", las=1) plot(o_mu1p2_NB, result="Prob", category=c(1, 2), bty="n", las=1) plot(o_mu1p2_NB, result="Prob", category=c(2, 1), bty="n", las=1) ## End(Not run)
The function plot.fitRMU plots the results of fitRMU().
In most of the cases, replicate.CI can be set to 0 for what="proportions" or "numbers".
The parameter CI.RMU can be used when this function is used several times with the same data.
## S3 method for class 'fitRMU' plot( x, ..., resultMCMC = NULL, chain = 1, replicate.CI = 10000, CI.RMU = NULL, what = "proportions", criteria = "50%", aggregate = "both", order = NULL, control.legend = list(), show.legend = TRUE, col = rainbow, border = NA, names.legend = NULL )## S3 method for class 'fitRMU' plot( x, ..., resultMCMC = NULL, chain = 1, replicate.CI = 10000, CI.RMU = NULL, what = "proportions", criteria = "50%", aggregate = "both", order = NULL, control.legend = list(), show.legend = TRUE, col = rainbow, border = NA, names.legend = NULL )
x |
A result file generated by fitRMU |
... |
Parameters used by plot |
resultMCMC |
MCMC result for fitRUM |
chain |
Chain to be plotted for MCMC |
replicate.CI |
Number of replicates to estimate CI |
CI.RMU |
A result of CI.RMU() |
what |
Can be proportions, numbers or total |
criteria |
What criteria will be used for proportions or numbers: mean or 50% |
aggregate |
Can be model or both |
order |
Give the order of series in plot, from bottom to top. Can be used to not show series. |
control.legend |
Parameters send to legend |
show.legend |
If FALSE, does not show legend |
col |
The function used to generate colors. |
border |
The border of polygons used to represent the proportions. |
names.legend |
Names to show in legend. |
plot.fitRMU plots the results of a fit RMU.
Return A list with result of CI.RMU()
Marc Girondot
Other Fill gaps in RMU:
CI.RMU(),
fitRMU(),
fitRMU_MHmcmc(),
fitRMU_MHmcmc_p(),
logLik.fitRMU()
## Not run: library("phenology") RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), stringsAsFactors = FALSE) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), stringsAsFactors = FALSE) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") expo <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Exponential", model.SD="Zero") YS <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero") YS1 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="First-order") YS1_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="First-order", parameters=YS1$par) YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="Second-order", parameters=YS1$par) YS2_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="Second-order", parameters=YS1_cst$par) compare_AIC(Constant=cst, Exponential=expo, YearSpecific=YS) compare_AIC(YearSpecific_ProportionsFirstOrder_Zero=YS1, YearSpecific_ProportionsFirstOrder_Constant=YS1_cst) compare_AIC(YearSpecific_ProportionsConstant=YS, YearSpecific_ProportionsFirstOrder=YS1, YearSpecific_ProportionsSecondOrder=YS2) compare_AIC(YearSpecific_ProportionsFirstOrder=YS1_cst, YearSpecific_ProportionsSecondOrder=YS2_cst) barplot_errbar(YS1_cst$proportions[1, ], y.plus = YS1_cst$proportions.CI.0.95[1, ], y.minus = YS1_cst$proportions.CI.0.05[1, ], las=1, ylim=c(0, 0.7), main="Proportion of the different rookeries in the region") plot(cst, main="Use of different beaches along the time", what="total") plot(expo, main="Use of different beaches along the time", what="total") plot(YS2_cst, main="Use of different beaches along the time", what="total") plot(YS1, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time", what="numbers") parpre <- par(mar=c(4, 4, 2, 5)+0.4) par(xpd=TRUE) plot(YS, main="Use of different beaches along the time", control.legend=list(x=2000, y=0.4, legend=c("Yalimapo", "Galibi", "Irakumpapy"))) par(mar=parpre) # Example to modify order of series plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana")) plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana", "Yalimapo.French.Guiana")) # Example to change the color plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana", "Yalimapo.French.Guiana"), col=function(n) rep(c("gray", "lightgrey"), floor(n/2)), border="black", names.legend=c("Yalimapo", "Galibi", "Irakumpapy")) ## End(Not run)## Not run: library("phenology") RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", "Galibi.Suriname", "Irakumpapy.French.Guiana"), se=c("se_Yalimapo.French.Guiana", "se_Galibi.Suriname", "se_Irakumpapy.French.Guiana"), stringsAsFactors = FALSE) data.AtlanticW <- data.frame(Year=c(1990:2000), Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 6542, 5678, 1243, NA, 1566, 1566), se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 230, 129, 167, NA, 145, 20), Galibi.Suriname=c(276, 275, 290, NA, 267, 542, 678, NA, 243, 156, 123), se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 4.3, 2.3, NA, 10.3, 10.1, 8.9), Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 3542, 2678, 243, NA, 566, 566), se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 130, 29, 67, NA, 15, 20), stringsAsFactors = FALSE) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") expo <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Exponential", model.SD="Zero") YS <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero") YS1 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="First-order") YS1_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="First-order", parameters=YS1$par) YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="Second-order", parameters=YS1$par) YS2_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Constant", model.rookeries="Second-order", parameters=YS1_cst$par) compare_AIC(Constant=cst, Exponential=expo, YearSpecific=YS) compare_AIC(YearSpecific_ProportionsFirstOrder_Zero=YS1, YearSpecific_ProportionsFirstOrder_Constant=YS1_cst) compare_AIC(YearSpecific_ProportionsConstant=YS, YearSpecific_ProportionsFirstOrder=YS1, YearSpecific_ProportionsSecondOrder=YS2) compare_AIC(YearSpecific_ProportionsFirstOrder=YS1_cst, YearSpecific_ProportionsSecondOrder=YS2_cst) barplot_errbar(YS1_cst$proportions[1, ], y.plus = YS1_cst$proportions.CI.0.95[1, ], y.minus = YS1_cst$proportions.CI.0.05[1, ], las=1, ylim=c(0, 0.7), main="Proportion of the different rookeries in the region") plot(cst, main="Use of different beaches along the time", what="total") plot(expo, main="Use of different beaches along the time", what="total") plot(YS2_cst, main="Use of different beaches along the time", what="total") plot(YS1, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time") plot(YS1_cst, main="Use of different beaches along the time", what="numbers") parpre <- par(mar=c(4, 4, 2, 5)+0.4) par(xpd=TRUE) plot(YS, main="Use of different beaches along the time", control.legend=list(x=2000, y=0.4, legend=c("Yalimapo", "Galibi", "Irakumpapy"))) par(mar=parpre) # Example to modify order of series plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana")) plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana", "Yalimapo.French.Guiana")) # Example to change the color plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana", "Yalimapo.French.Guiana"), col=function(n) rep(c("gray", "lightgrey"), floor(n/2)), border="black", names.legend=c("Yalimapo", "Galibi", "Irakumpapy")) ## End(Not run)
This function plots the result of IPFit() or IPModel().
If col is defined with a number of colors, only these colors and shown in legend.
## S3 method for class 'IP' plot(x, ..., N = NULL, clutch = 1, result = "data")## S3 method for class 'IP' plot(x, ..., N = NULL, clutch = 1, result = "data")
x |
A result for IPFit() or IPModel(). |
... |
Graphic parameters, see par(). |
N |
Number of replicates for IPModel(). |
clutch |
The rank of clutch when DeltameanIP is used. |
result |
What result will be plotted: data, model, data&model, IP, Abort, ECF, reverseECF |
plot.IP plots a result of Internesting Period fit or data
Nothing
Marc Girondot [email protected]
Other Model of Internesting Period:
IPFit(),
IPModel(),
IPPredict(),
summary.IP()
## Not run: library(phenology) # Example data <- c(0, 47, 15, 6, 5, 4, 2, 5, 57, 203, 205, 103, 35, 24, 12, 10, 13, 49, 86, 107, 111, 73, 47, 30, 19, 17, 33, 48, 77, 83, 65, 37, 27, 23, 24, 22, 41, 42, 44, 33, 39, 24, 18, 18, 22, 22, 19, 24, 28, 17, 18, 19, 17, 4, 12, 9, 6, 11, 7, 11, 12, 5, 4, 6, 11, 5, 6, 7, 3, 2, 1, 3, 2, 1, 2, 0, 0, 3, 1, 0, 2, 0, 0, 1) class(data) <- unique(append("IP", class(data))) plot(data) ######### Fit parametric ECF using Maximum-Likelihood par <- c(meanIP = 9.9959691992722917, sdIP = 0.10066664270893474, minIP = 7.5684588178888754, pAbort = 2.2510012544630911, meanAbort = 2.8969185085603386, sdAbort = 0.92688983853803242, pCapture = -1.0393803705929086, meanECF = 3.9551519427394255, sdECF = 0.31657679943365019) fML <- IPFit(x=par, fixed.parameters=c(N=1000000), data=data, verbose=FALSE, model="ML") # Plot the fitted ECF plot(fML, result="ECF") # Plot the Internesting Period distribution plot(fML, result="IP") # Plot the distribution of days between tentatives plot(fML, result="Abort") plot(fML, result="Abort", xlim=c(0, 10)) # Plot the data plot(fML, result="data") # Plot the data and the model plot(fML, result="data&model") # Plot the cumulative proportion of ECF according to date of observation plot(fML, result="reverseECF") plot(fML_NP_Delta, result="reverseECF", col=grey.colors(128)) ######### Fit using Metropolis-Hastings # ECF.1 = 1 is fixed par <- c(ECF.2 = 0.044151921569961131, ECF.3 = 2.0020778325280748, ECF.4 = 2.6128345101617083, ECF.5 = 2.6450582416622375, ECF.6 = 2.715198206774927, ECF.7 = 2.0288031327239904, ECF.8 = 1.0028041546528881, ECF.9 = 0.70977432157689235, ECF.10 = 0.086052204035003091, ECF.11 = 0.011400419961702518, ECF.12 = 0.001825219438794076, ECF.13 = 0.00029398731859899116, ECF.14 = 0.002784886479846703, meanIP = 9.9887100433529721, sdIP = 0.10580250625108811, minIP = 6.5159124624132048, pAbort = 2.5702251748938956, meanAbort = 2.2721679285648841, sdAbort = 0.52006431730489933, pCapture = 0.079471782729506113) df <- data.frame(Density=rep("dunif", length(par)), Prior1=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), Prior2=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), SDProp=unname(c(ECF.2 = 6.366805760909012e-05, ECF.3 = 6.366805760909012e-05, ECF.4 = 6.366805760909012e-05, ECF.5 = 6.366805760909012e-05, ECF.6 = 6.366805760909012e-05, ECF.7 = 6.366805760909012e-05, ECF.8 = 6.366805760909012e-05, ECF.9 = 6.366805760909012e-05, ECF.10 = 6.366805760909012e-05, ECF.11 = 6.366805760909012e-05, ECF.12 = 6.366805760909012e-05, ECF.13 = 6.366805760909012e-05, ECF.14 = 6.366805760909012e-05, meanIP = 6.366805760909012e-05, sdIP = 6.366805760909012e-05, minIP = 6.366805760909012e-05, pAbort = 6.366805760909012e-05, meanAbort = 6.366805760909012e-05, sdAbort = 6.366805760909012e-05, pCapture = 6.366805760909012e-05)), Min=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), Max=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), Init=par, stringsAsFactors = FALSE) rownames(df)<- names(par) fMH <- IPFit(parametersMH=df, fixed.parameters=c(N=10000), data=data, verbose=FALSE, n.iter = 10000, n.chains = 1, n.adapt = 100, thin = 1, trace = TRUE, adaptive = TRUE, model="MH") # Plot the fitted ECF plot(fMH, result="ECF") # Plot the posteriors and priors plot(fMH$MH, parameters="meanIP", xlim=c(6, 14)) plot(x=1:length(fMH$MH$resultLnL[[1]]), y=fMH$MH$resultLnL[[1]], type="l", xlab="Iterations", ylab="Ln L", bty="n", las=1) ## End(Not run)## Not run: library(phenology) # Example data <- c(0, 47, 15, 6, 5, 4, 2, 5, 57, 203, 205, 103, 35, 24, 12, 10, 13, 49, 86, 107, 111, 73, 47, 30, 19, 17, 33, 48, 77, 83, 65, 37, 27, 23, 24, 22, 41, 42, 44, 33, 39, 24, 18, 18, 22, 22, 19, 24, 28, 17, 18, 19, 17, 4, 12, 9, 6, 11, 7, 11, 12, 5, 4, 6, 11, 5, 6, 7, 3, 2, 1, 3, 2, 1, 2, 0, 0, 3, 1, 0, 2, 0, 0, 1) class(data) <- unique(append("IP", class(data))) plot(data) ######### Fit parametric ECF using Maximum-Likelihood par <- c(meanIP = 9.9959691992722917, sdIP = 0.10066664270893474, minIP = 7.5684588178888754, pAbort = 2.2510012544630911, meanAbort = 2.8969185085603386, sdAbort = 0.92688983853803242, pCapture = -1.0393803705929086, meanECF = 3.9551519427394255, sdECF = 0.31657679943365019) fML <- IPFit(x=par, fixed.parameters=c(N=1000000), data=data, verbose=FALSE, model="ML") # Plot the fitted ECF plot(fML, result="ECF") # Plot the Internesting Period distribution plot(fML, result="IP") # Plot the distribution of days between tentatives plot(fML, result="Abort") plot(fML, result="Abort", xlim=c(0, 10)) # Plot the data plot(fML, result="data") # Plot the data and the model plot(fML, result="data&model") # Plot the cumulative proportion of ECF according to date of observation plot(fML, result="reverseECF") plot(fML_NP_Delta, result="reverseECF", col=grey.colors(128)) ######### Fit using Metropolis-Hastings # ECF.1 = 1 is fixed par <- c(ECF.2 = 0.044151921569961131, ECF.3 = 2.0020778325280748, ECF.4 = 2.6128345101617083, ECF.5 = 2.6450582416622375, ECF.6 = 2.715198206774927, ECF.7 = 2.0288031327239904, ECF.8 = 1.0028041546528881, ECF.9 = 0.70977432157689235, ECF.10 = 0.086052204035003091, ECF.11 = 0.011400419961702518, ECF.12 = 0.001825219438794076, ECF.13 = 0.00029398731859899116, ECF.14 = 0.002784886479846703, meanIP = 9.9887100433529721, sdIP = 0.10580250625108811, minIP = 6.5159124624132048, pAbort = 2.5702251748938956, meanAbort = 2.2721679285648841, sdAbort = 0.52006431730489933, pCapture = 0.079471782729506113) df <- data.frame(Density=rep("dunif", length(par)), Prior1=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), Prior2=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), SDProp=unname(c(ECF.2 = 6.366805760909012e-05, ECF.3 = 6.366805760909012e-05, ECF.4 = 6.366805760909012e-05, ECF.5 = 6.366805760909012e-05, ECF.6 = 6.366805760909012e-05, ECF.7 = 6.366805760909012e-05, ECF.8 = 6.366805760909012e-05, ECF.9 = 6.366805760909012e-05, ECF.10 = 6.366805760909012e-05, ECF.11 = 6.366805760909012e-05, ECF.12 = 6.366805760909012e-05, ECF.13 = 6.366805760909012e-05, ECF.14 = 6.366805760909012e-05, meanIP = 6.366805760909012e-05, sdIP = 6.366805760909012e-05, minIP = 6.366805760909012e-05, pAbort = 6.366805760909012e-05, meanAbort = 6.366805760909012e-05, sdAbort = 6.366805760909012e-05, pCapture = 6.366805760909012e-05)), Min=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), Max=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), Init=par, stringsAsFactors = FALSE) rownames(df)<- names(par) fMH <- IPFit(parametersMH=df, fixed.parameters=c(N=10000), data=data, verbose=FALSE, n.iter = 10000, n.chains = 1, n.adapt = 100, thin = 1, trace = TRUE, adaptive = TRUE, model="MH") # Plot the fitted ECF plot(fMH, result="ECF") # Plot the posteriors and priors plot(fMH$MH, parameters="meanIP", xlim=c(6, 14)) plot(x=1:length(fMH$MH$resultLnL[[1]]), y=fMH$MH$resultLnL[[1]], type="l", xlab="Iterations", ylab="Ln L", bty="n", las=1) ## End(Not run)
The function plot.phenology plots the phenology graph from a result object.
If cofactors have been added, the plot does not show their effects.
plot.objects can be "observations", "ML" for maximum likelihood, "ML.SD" for dispersion of
observations, "ML.quantiles" or "MCMC.quantiles" if a mcmc object is given
## S3 method for class 'phenology' plot( x, ..., series = "all", moon = FALSE, replicate.CI = 10000, resultmcmc = NULL, season = NULL, chain = 1, replicate.CI.mcmc = "all", level = 0.95, plot.objects = c("observations", "ML", "ML.SD", "ML.quantiles", "MCMC.quantiles"), col.ML = "black", col.SD = "red", col.SD.polygon = rgb(red = 1, green = 0, blue = 0, alpha = 0.2), col.MCMC.quantiles = "purple", col.MCMC.quantiles.polygon = rgb(red = 160/255, green = 32/255, blue = 240/255, alpha = 0.2), col.ML.quantiles = "black", col.ML.quantiles.polygon = rgb(red = 0, green = 0, blue = 0, alpha = 0.2), col.observations = "black", col.minimum.observations = "blue", col.grouped.observations = "green" )## S3 method for class 'phenology' plot( x, ..., series = "all", moon = FALSE, replicate.CI = 10000, resultmcmc = NULL, season = NULL, chain = 1, replicate.CI.mcmc = "all", level = 0.95, plot.objects = c("observations", "ML", "ML.SD", "ML.quantiles", "MCMC.quantiles"), col.ML = "black", col.SD = "red", col.SD.polygon = rgb(red = 1, green = 0, blue = 0, alpha = 0.2), col.MCMC.quantiles = "purple", col.MCMC.quantiles.polygon = rgb(red = 160/255, green = 32/255, blue = 240/255, alpha = 0.2), col.ML.quantiles = "black", col.ML.quantiles.polygon = rgb(red = 0, green = 0, blue = 0, alpha = 0.2), col.observations = "black", col.minimum.observations = "blue", col.grouped.observations = "green" )
x |
A result file generated by fit_phenology |
... |
Parameters used by plot |
series |
Name or number of series to be plotted or 'all' |
moon |
If TRUE, the moon phase is ploted. Default is FALSE |
replicate.CI |
Number of replicates for estimation of confidence interval |
resultmcmc |
A mcmc object |
season |
Which season to plot |
chain |
The number of chain to be used in resultmcmc |
replicate.CI.mcmc |
Number of iterations to be used or "all" |
level |
Level to estimate confidence interval or credibility interval |
plot.objects |
What to plot? |
col.ML |
Color of the ML mean curve |
col.SD |
Color of the SD curve (distribution of observations) |
col.SD.polygon |
Color of the polygon of the SD curve. If FALSE not shown. |
col.MCMC.quantiles |
Color of the quantiles curve based on mcmc |
col.MCMC.quantiles.polygon |
Color of the credibility interval polygon based on MCMC. If FALSE not shown. |
col.ML.quantiles |
Color of the SE curve based on ML |
col.ML.quantiles.polygon |
Color of the confidence interval polygon based on ML. If FALSE not shown. |
col.observations |
Color of the points |
col.minimum.observations |
Color of the points indicating minimum counts |
col.grouped.observations |
Color of the lines indicating grouped observations |
plot.phenology plots the phenology.
A list with four objects: synthesis is a data.frame with global estimate of nesting.
details_MCMC, details_ML and details_mean are lists with day by day information for each series.
Marc Girondot [email protected]
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name = "Complete", reference = as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) parg <- c('Max_Complete' = 33.076044848500167, 25, 'MinB_Complete' = 0.21758630798131923, 'MinE_Complete' = 0.42493953463205936, 'LengthB' = 96.158007568020523, 'Peak' = 174.62435300274245, 'LengthE' = 62.084876419654634, 'Flat' = 0, 'Theta' = 3.5864650991821954) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # Plot only part of the nesting season ptoutput <- plot(result_Gratiot, xlim=c(as.Date("2001-03-01"),as.Date("2001-08-31"))) # Use month names in English Sys.setlocale(category = "LC_TIME", locale = "en_GB.UTF-8") output <- plot(result_Gratiot) # set back the month name in local R language Sys.setlocale(category = "LC_TIME", locale = "") # plot based on quantiles of mcmc object plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, plot.objects=c("observations", "MCMC.quantiles")) plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, plot.objects=c("observations", "ML.SD", "ML.quantiles")) plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, plot.objects=c("observations", "ML.SD", "MCMC.quantiles")) plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, plot.objects=c("observations", "ML.quantiles", "MCMC.quantiles")) ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name = "Complete", reference = as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) parg <- c('Max_Complete' = 33.076044848500167, 25, 'MinB_Complete' = 0.21758630798131923, 'MinE_Complete' = 0.42493953463205936, 'LengthB' = 96.158007568020523, 'Peak' = 174.62435300274245, 'LengthE' = 62.084876419654634, 'Flat' = 0, 'Theta' = 3.5864650991821954) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # Plot only part of the nesting season ptoutput <- plot(result_Gratiot, xlim=c(as.Date("2001-03-01"),as.Date("2001-08-31"))) # Use month names in English Sys.setlocale(category = "LC_TIME", locale = "en_GB.UTF-8") output <- plot(result_Gratiot) # set back the month name in local R language Sys.setlocale(category = "LC_TIME", locale = "") # plot based on quantiles of mcmc object plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, plot.objects=c("observations", "MCMC.quantiles")) plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, plot.objects=c("observations", "ML.SD", "ML.quantiles")) plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, plot.objects=c("observations", "ML.SD", "MCMC.quantiles")) plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, plot.objects=c("observations", "ML.quantiles", "MCMC.quantiles")) ## End(Not run)
This function plots a likelihood map obtained after map_phenology.
## S3 method for class 'phenologymap' plot(x, ..., col = heat.colors(128), xlab = "Phi", ylab = "Delta")## S3 method for class 'phenologymap' plot(x, ..., col = heat.colors(128), xlab = "Phi", ylab = "Delta")
x |
A map generated with map_phenology. |
... |
not used |
col |
Colors could be heat.colors(128) or rainbow(64) or col=gray(c(seq(0, 1, length.out=128))) |
xlab |
Label for x axis |
ylab |
Label for y axis |
plot.phenologymap plots a likelihood map with Delta and Phi varying.
Return None
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) data(map_Gratiot) # Plot the map plot(map_Gratiot, col=heat.colors(128)) ## End(Not run)## Not run: library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) data(map_Gratiot) # Plot the map plot(map_Gratiot, col=heat.colors(128)) ## End(Not run)
Plot the remigration intervals.
## S3 method for class 'Remigration' plot(x, legend = TRUE, ...)## S3 method for class 'Remigration' plot(x, legend = TRUE, ...)
x |
Object obtained from Bayesian.remigration() |
legend |
TRUE or FALSE or c(x, y) |
... |
Parameters transmitted to plot |
plot.Remigration plots the remigration intervals.
An invisible dataframe with values used for plotting.
Marc Girondot
Other Model of Remigration Interval:
Bayesian.remigration(),
LnRI_norm(),
RI()
## Not run: library(phenology) # Example # Each year a fraction of 0.9 is surviving s <- c(s=0.9) # Probability of tag retention; 0.8 t <- c(t=0.8) # Time-conditional return probability - This is the true remigration rate r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) # Capture probability p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # Number of observations for 400 tagged females after 1, 2, 3, 4, and 5 years OBS <- c(400, 10, 120, 40, 20, 10) kl_s <- length(s) kl_t <- length(t) kl_r <- length(r) kl_p <- length(p) pMCMC <- data.frame(Density=c("newdbeta", "newdbeta", rep("dunif", kl_r), rep("newdbeta", kl_p), "dunif"), Prior1=c(s, t, rep(0, kl_r), rep(0.2, kl_p), 0), Prior2=c(0.02, 0.02, rep(1, kl_r), rep(0.08, kl_p), 10), SDProp=c(0.05, 0.05, rep(0.05, kl_r), rep(0.05, kl_p), 0.05), Min=c(0, 0, rep(0, kl_r), rep(0, kl_p), 0), Max=c(1, 1, rep(1, kl_r), rep(1, kl_p), 10), Init=c(s, t, r, p, 1), stringsAsFactors = FALSE, row.names=c("s", "t", names(r), names(p), "sd") ) rMCMC <- Bayesian.remigration(parameters = pMCMC, n.iter = 1000000, n.adapt = 300000, trace=10000, data=OBS) plot(rMCMC) ## End(Not run)## Not run: library(phenology) # Example # Each year a fraction of 0.9 is surviving s <- c(s=0.9) # Probability of tag retention; 0.8 t <- c(t=0.8) # Time-conditional return probability - This is the true remigration rate r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) # Capture probability p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # Number of observations for 400 tagged females after 1, 2, 3, 4, and 5 years OBS <- c(400, 10, 120, 40, 20, 10) kl_s <- length(s) kl_t <- length(t) kl_r <- length(r) kl_p <- length(p) pMCMC <- data.frame(Density=c("newdbeta", "newdbeta", rep("dunif", kl_r), rep("newdbeta", kl_p), "dunif"), Prior1=c(s, t, rep(0, kl_r), rep(0.2, kl_p), 0), Prior2=c(0.02, 0.02, rep(1, kl_r), rep(0.08, kl_p), 10), SDProp=c(0.05, 0.05, rep(0.05, kl_r), rep(0.05, kl_p), 0.05), Min=c(0, 0, rep(0, kl_r), rep(0, kl_p), 0), Max=c(1, 1, rep(1, kl_r), rep(1, kl_p), 10), Init=c(s, t, r, p, 1), stringsAsFactors = FALSE, row.names=c("s", "t", names(r), names(p), "sd") ) rMCMC <- Bayesian.remigration(parameters = pMCMC, n.iter = 1000000, n.adapt = 300000, trace=10000, data=OBS) plot(rMCMC) ## End(Not run)
This function plots a CMR file summarized using TableECFOCF().
## S3 method for class 'TableECFOCF' plot( x, ..., result = "ecfocf", period = 1, cex.points = 4, pch = 19, col = "black", cex.axis = 0.8, cex.labels = 0.5, col.labels = "red", show.labels = FALSE, show.0 = FALSE, pch.0 = 4, cex.0 = 0.5, col.0 = "blue", show.scale = TRUE, max.scale = NULL )## S3 method for class 'TableECFOCF' plot( x, ..., result = "ecfocf", period = 1, cex.points = 4, pch = 19, col = "black", cex.axis = 0.8, cex.labels = 0.5, col.labels = "red", show.labels = FALSE, show.0 = FALSE, pch.0 = 4, cex.0 = 0.5, col.0 = "blue", show.scale = TRUE, max.scale = NULL )
x |
A CMR file summarized using TableECFOCF() |
... |
Graphic parameters |
result |
What should be plotted: ECFOCF or data, ECF, OCF. |
period |
The period that will be plotted. |
cex.points |
The maximum magnification to be used for points relative to the current setting of cex. |
pch |
Character to be used for points. |
col |
Color to be used for points. |
cex.axis |
The magnification to be used for axis annotation relative to the current setting of cex. |
cex.labels |
The magnification to be used for figures. |
col.labels |
Color of figures. |
show.labels |
Logical to be used to show figures. |
show.0 |
Logical to show 0 counts. |
pch.0 |
Character used for 0 counts. |
cex.0 |
The magnification to be used for character for 0 counts. |
col.0 |
Color of characters for 0 counts. |
show.scale |
If TRUE, show the scale as a legend |
max.scale |
Maximum value for scale; if NULL it is maximum of observations. |
plot.TableECFOCF plots a TableECFOCF dataset.
Nothing
Marc Girondot
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
TableECFOCF(),
fitCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
generateCF(),
lnLCF(),
logLik.ECFOCF(),
plot.ECFOCF()
## Not run: library(phenology) # Example data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) par(mar=c(4, 4, 1, 1)+0.4) plot(ECFOCF_2002, bty="n", las=1, cex.points=3, cex.axis = 0.8, main="Year 2002") plot(ECFOCF_2002, bty="n", las=1, cex.points=5, cex.0=0.2, col="red", show.0 = TRUE, col.0="blue") plot(ECFOCF_2002, bty="n", las=1, cex.points=3, col="lightgrey", col.labels = "red", show.labels=TRUE) plot(ECFOCF_2002, bty="n", las=1, cex.points=3, pch=NA, col.labels = "red", show.labels=TRUE) plot(ECFOCF_2002, bty="n", las=1, cex.points=3, pch=NA, col.labels = "red", show.labels=TRUE, cex.0=0.2, show.0 = TRUE, col.0="blue", pch.0=4) plot(ECFOCF_2002, bty="n", las=1, result="OCF") plot(ECFOCF_2002, bty="n", las=1, result="ECF") plot(ECFOCF_2002, bty="n", las=1, result="ECF", type="l", main="2002 season", xlab="Clutch frequency") par(new=TRUE) plot(ECFOCF_2002, bty="n", las=1, result="OCF", type="l", main="", ylim=ScalePreviousPlot()$ylim[c("begin", "end")], xlab="", ylab="", col="red", xaxt="n", yaxt="n", axes=FALSE) legend("topright", legend=c("OCF", "ECF"), lty=1, col=c("red", "black")) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) plot(ECFOCF_2002, period=13) ## End(Not run)## Not run: library(phenology) # Example data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) par(mar=c(4, 4, 1, 1)+0.4) plot(ECFOCF_2002, bty="n", las=1, cex.points=3, cex.axis = 0.8, main="Year 2002") plot(ECFOCF_2002, bty="n", las=1, cex.points=5, cex.0=0.2, col="red", show.0 = TRUE, col.0="blue") plot(ECFOCF_2002, bty="n", las=1, cex.points=3, col="lightgrey", col.labels = "red", show.labels=TRUE) plot(ECFOCF_2002, bty="n", las=1, cex.points=3, pch=NA, col.labels = "red", show.labels=TRUE) plot(ECFOCF_2002, bty="n", las=1, cex.points=3, pch=NA, col.labels = "red", show.labels=TRUE, cex.0=0.2, show.0 = TRUE, col.0="blue", pch.0=4) plot(ECFOCF_2002, bty="n", las=1, result="OCF") plot(ECFOCF_2002, bty="n", las=1, result="ECF") plot(ECFOCF_2002, bty="n", las=1, result="ECF", type="l", main="2002 season", xlab="Clutch frequency") par(new=TRUE) plot(ECFOCF_2002, bty="n", las=1, result="OCF", type="l", main="", ylim=ScalePreviousPlot()$ylim[c("begin", "end")], xlab="", ylab="", col="red", xaxt="n", yaxt="n", axes=FALSE) legend("topright", legend=c("OCF", "ECF"), lty=1, col=c("red", "black")) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) plot(ECFOCF_2002, period=13) ## End(Not run)
Plot the daily rate of tag loss.
To use this function without a result of Tagloss_fit(), see the hack in examples.
## S3 method for class 'Tagloss' plot( x, t = NULL, fitted.parameters = NULL, fixed.parameters = NULL, scale = 1, model_before = NULL, model_after = NULL, model = c("1", "2", "R1", "R2", "L1", "L2", "cumul", "cumul1", "N2", "N1", "N0", "NLR", "N0R", "NL0", "N00"), col = rev(grey.colors(4, start = 0.9, end = 0.3)), text.col = grey.colors(4, start = 0.9, end = 0.3), label.col = "black", add = FALSE, mcmc = FALSE, Hessian = NULL, replicates = NULL, method = NULL, probs = c(0.025, 0.975), progressbar = FALSE, decoration = FALSE, ... )## S3 method for class 'Tagloss' plot( x, t = NULL, fitted.parameters = NULL, fixed.parameters = NULL, scale = 1, model_before = NULL, model_after = NULL, model = c("1", "2", "R1", "R2", "L1", "L2", "cumul", "cumul1", "N2", "N1", "N0", "NLR", "N0R", "NL0", "N00"), col = rev(grey.colors(4, start = 0.9, end = 0.3)), text.col = grey.colors(4, start = 0.9, end = 0.3), label.col = "black", add = FALSE, mcmc = FALSE, Hessian = NULL, replicates = NULL, method = NULL, probs = c(0.025, 0.975), progressbar = FALSE, decoration = FALSE, ... )
x |
Object obteined from Tagloss_fit() |
t |
Time for which values of model must be ploted |
fitted.parameters |
Set of parameters |
fixed.parameters |
Another set of parameters without standard error associated |
scale |
Scale value. When Cumul is used, scale is always 1. |
model_before |
Transformation of parameters before to use Tagloss_model() |
model_after |
Transformation of parameters after to use Tagloss_model() |
model |
Can be 1, 2, R1, R2, L1, L2 or Cumul (2 tags) or Cumul1 (1 tag) |
col |
The colors of shading areas of cumul or the color of line |
text.col |
The text color for cumul model |
label.col |
The text color used for labels when decoration is true |
add |
Should the data be added to a previous plot? |
mcmc |
The mcmc result |
Hessian |
Hessian matrix of parameters |
replicates |
Number of replicates for confidence interval |
method |
Which method to use to estimate confidence interval |
probs |
Quantiles to show for confidence interval |
progressbar |
Is shown a progressbar? |
decoration |
Try to add name of parameters on the graph |
... |
Parameters transmitted to plot |
plot.tagloss plots the daily rate of tag loss.
An invisible dataframe with values used for plotting.
Marc Girondot
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.TaglossData()
## Not run: library(phenology) # Example t <- 1:1000 par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, A_1=-logit(0.02), B_1=-logit(0.05), C_1=-logit(0.07)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="1") phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="1", scale=1000, decoration = TRUE) par <- c(D1_2=200, D2D1_2=100, D3D2_2=200, A_2=-logit(0.05), B_2=-logit(0.03), C_2=-logit(0.03)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, ylim=c(0, 1), scale = 10, model="2", decoration = TRUE) par <- c(D1_L2=200, D2D1_L2=100, D3D2_L2=200, A_L2=-logit(0.05), B_L2=-logit(0.07), C_L2=-logit(0.07)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="L2") par <- c(D1_R2=200, D2D1_R2=0, D3D2_R2=700, A_R2=-logit(0.02), B_R2=-logit(0.05), C_R2=-logit(0.07)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="R2", col="red", add=TRUE) par <- c(D1_L1=200, D2D1_L1=2000, D3D2_L1=2000, A_L1=-logit(0.05), B_L1=-logit(0.02), C_L1=-logit(0.1)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="L1") # To plot the history of individuals par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, A_1=-logit(5E-4), B_1=-logit(4E-4), C_1=-logit(5E-4), D1_2=200, D2D1_2=100, D3D2_2=200, A_2=-logit(6E-4), B_2=-logit(5E-4), C_2=-logit(6E-4)) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE, col=c("red", "green", "blue")) # To plot the history of individuals par <- c(D1_R1=200, D2D1_R1=300, D3D2_R1=200, A_R1=-logit(5E-4), B_R1=-logit(4E-4), C_R1=-logit(5E-4), D1_R2=200, D2D1_R2=200, D3D2_R2=200, A_R2=-logit(6E-4), B_R2=-logit(5E-4), C_R2=-logit(6E-4), D1_L1=200, D2D1_L1=400, D3D2_L1=200, A_L1=-logit(5E-4), B_L1=-logit(4E-4), C_L1=-logit(5E-4), D1_L2=200, D2D1_L2=100, D3D2_L2=200, A_L2=-logit(6E-4), B_L2=-logit(5E-4), C_L2=-logit(6E-4)) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="R1", decoration = TRUE) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="R2", decoration = TRUE) # Example of fit data_f_21 <- Tagloss_format(outLR, model="21") data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- c('D1_2' = 49.78891736351531, 'D2D1_2' = 1059.3635769732305, 'D3D2_2' = 12.434313273804602, 'A_2' = 5.2238379144659683, 'B_2' = 8.0050044071275543, 'C_2' = 8.4317863609499675, 'D1_1' = 701.80273287212935, 'D2D1_1' = 0.010951749100596819, 'D3D2_1' = 3773.6290607434876, 'A_1' = 205.42435592344776, 'B_1' = 9.9598342503239863, 'C_1' = 6.7234868237164722) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="1", decoration = TRUE) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="2", decoration = TRUE) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, hessian = TRUE) plot(x=o, model="1", replicates=0, method=NULL, decoration = TRUE) plot(x=o, model="1", replicates=1000, method="Hessian", decoration = TRUE) ## End(Not run)## Not run: library(phenology) # Example t <- 1:1000 par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, A_1=-logit(0.02), B_1=-logit(0.05), C_1=-logit(0.07)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="1") phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="1", scale=1000, decoration = TRUE) par <- c(D1_2=200, D2D1_2=100, D3D2_2=200, A_2=-logit(0.05), B_2=-logit(0.03), C_2=-logit(0.03)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, ylim=c(0, 1), scale = 10, model="2", decoration = TRUE) par <- c(D1_L2=200, D2D1_L2=100, D3D2_L2=200, A_L2=-logit(0.05), B_L2=-logit(0.07), C_L2=-logit(0.07)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="L2") par <- c(D1_R2=200, D2D1_R2=0, D3D2_R2=700, A_R2=-logit(0.02), B_R2=-logit(0.05), C_R2=-logit(0.07)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="R2", col="red", add=TRUE) par <- c(D1_L1=200, D2D1_L1=2000, D3D2_L1=2000, A_L1=-logit(0.05), B_L1=-logit(0.02), C_L1=-logit(0.1)) phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="L1") # To plot the history of individuals par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, A_1=-logit(5E-4), B_1=-logit(4E-4), C_1=-logit(5E-4), D1_2=200, D2D1_2=100, D3D2_2=200, A_2=-logit(6E-4), B_2=-logit(5E-4), C_2=-logit(6E-4)) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE, col=c("red", "green", "blue")) # To plot the history of individuals par <- c(D1_R1=200, D2D1_R1=300, D3D2_R1=200, A_R1=-logit(5E-4), B_R1=-logit(4E-4), C_R1=-logit(5E-4), D1_R2=200, D2D1_R2=200, D3D2_R2=200, A_R2=-logit(6E-4), B_R2=-logit(5E-4), C_R2=-logit(6E-4), D1_L1=200, D2D1_L1=400, D3D2_L1=200, A_L1=-logit(5E-4), B_L1=-logit(4E-4), C_L1=-logit(5E-4), D1_L2=200, D2D1_L2=100, D3D2_L2=200, A_L2=-logit(6E-4), B_L2=-logit(5E-4), C_L2=-logit(6E-4)) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="R1", decoration = TRUE) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="R2", decoration = TRUE) # Example of fit data_f_21 <- Tagloss_format(outLR, model="21") data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- c('D1_2' = 49.78891736351531, 'D2D1_2' = 1059.3635769732305, 'D3D2_2' = 12.434313273804602, 'A_2' = 5.2238379144659683, 'B_2' = 8.0050044071275543, 'C_2' = 8.4317863609499675, 'D1_1' = 701.80273287212935, 'D2D1_1' = 0.010951749100596819, 'D3D2_1' = 3773.6290607434876, 'A_1' = 205.42435592344776, 'B_1' = 9.9598342503239863, 'C_1' = 6.7234868237164722) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="1", decoration = TRUE) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="2", decoration = TRUE) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, hessian = TRUE) plot(x=o, model="1", replicates=0, method=NULL, decoration = TRUE) plot(x=o, model="1", replicates=1000, method="Hessian", decoration = TRUE) ## End(Not run)
This function plots the result of Tagloss_format().
The default ramp of colors is a grey ramp.
## S3 method for class 'TaglossData' plot( x, ..., categories = c("N22", "N21", "N11", "N10", "N20"), col = grey(seq(from = 0.9, to = 0, length.out = length(categories))), title.legend = "Tag history", categories.legend = categories, show.legend = TRUE )## S3 method for class 'TaglossData' plot( x, ..., categories = c("N22", "N21", "N11", "N10", "N20"), col = grey(seq(from = 0.9, to = 0, length.out = length(categories))), title.legend = "Tag history", categories.legend = categories, show.legend = TRUE )
x |
A result for Tagloss_format. |
... |
Graphic parameters, see par(). |
categories |
Categories to display. |
col |
The ramp of colors used for the categories. |
title.legend |
Title for legend box. |
categories.legend |
Name of categories to show in legend box. |
show.legend |
Should the legend box be shown ? |
plot.TaglossData plots formated data used for tagloss analysis
Nothing
Marc Girondot [email protected]
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss()
## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") plot(data_f_21) ## End(Not run)## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") plot(data_f_21) ## End(Not run)
The function print displays a phenology result.
## S3 method for class 'phenology' print(x, ...)## S3 method for class 'phenology' print(x, ...)
x |
A result file generated by fit_phenology |
... |
Not used |
print.phenology prints the information from a result x.
None
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Show the result result_Gratiotlibrary(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Show the result result_Gratiot
print.phenologymap print information on a phenologymap object
## S3 method for class 'phenologymap' print(x, ...)## S3 method for class 'phenologymap' print(x, ...)
x |
A map generated with map_phenology. |
... |
Not used |
Return None
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result ## Not run: map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) ## End(Not run) data(map_Gratiot) # Print the information on a map map_Gratiotlibrary("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result ## Not run: map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) ## End(Not run) data(map_Gratiot) # Print the information on a map map_Gratiot
The function print.phenologyout displays the output of a summary(phenology).
## S3 method for class 'phenologyout' print(x, ...)## S3 method for class 'phenologyout' print(x, ...)
x |
An output generated by plot_phenology |
... |
Not used |
print.phenologyout prints the information from a result x.
None
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # Show the output output ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # Show the output output ## End(Not run)
This function is used to remove the information of the site from a set of parameters. It can be used to other timeseries after.
remove_site(parameters = NULL, help = FALSE)remove_site(parameters = NULL, help = FALSE)
parameters |
Set of parameters |
help |
If TRUE, an help is displayed |
remove_site removes beach information from a set of parameters.
Return a set of modified parameters
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Extract parameters form result parg<-extract_result(result_Gratiot) # Remove site information parg1<-remove_site(parg)library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Extract parameters form result parg<-extract_result(result_Gratiot) # Remove site information parg1<-remove_site(parg)
Result of the fit of Leatherback nest counts from Gratiot et al. (2006) Figure 1 The phenology has been fitted with MinE, MinB, Max, Flat, LengthB, LengthE, Peak, Theta.
result_Gratiotresult_Gratiot
A list with Gratiot data and the result of the fit.
Result of the fit of Leatherback nest counts from Gratiot et al. (2006) Figure 1
Marc Girondot [email protected]
Gratiot, N., Gratiot, J., de Thoisy, B. & Kelle, L. 2006. Estimation of marine turtles nesting season from incomplete data ; statistical adjustment of a sinusoidal function. Animal Conservation, 9, 95-102.
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # Read a file with result data(result_Gratiot) ## End(Not run)## Not run: library(phenology) data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # Read a file with result data(result_Gratiot) ## End(Not run)
Result of the fit of Leatherback nest counts from Gratiot et al. (2006) Figure 1. The phenology has been fitted with MinE, MinB, Max, LengthB, LengthE, Peak, Theta. The Flat parameter is set to 0 and is not fitted.
result_Gratiot_Flatresult_Gratiot_Flat
A list with Gratiot data and the result of the fit.
Result of the fit of Leatherback nest counts from Gratiot et al. (2006) Figure 1
Marc Girondot [email protected]
Gratiot, N., Gratiot, J., de Thoisy, B. & Kelle, L. 2006. Estimation of marine turtles nesting season from incomplete data ; statistical adjustment of a sinusoidal function. Animal Conservation, 9, 95-102.
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Read a file with result data(result_Gratiot_Flat)library(phenology) # Read a file with result data(result_Gratiot_Flat)
Result of the mcmc for Leatherback nest counts from Gratiot et al. (2006) Figure 1 The phenology has been fitted with MinE, MinB, Max, Flat, LengthB, LengthE, Peak, Theta.
result_Gratiot_mcmcresult_Gratiot_mcmc
A mcmcComposite object with mcmc result.
Result of the mcmc for Leatherback nest counts from Gratiot et al. (2006) Figure 1
Marc Girondot [email protected]
Gratiot, N., Gratiot, J., de Thoisy, B. & Kelle, L. 2006. Estimation of marine turtles nesting season from incomplete data ; statistical adjustment of a sinusoidal function. Animal Conservation, 9, 95-102.
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) data(result_Gratiot) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # generate data for mcmc run pmcmc <- phenology_MHmcmc_p(result_Gratiot, accept = TRUE) result_Gratiot_mcmc <- phenology_MHmcmc(result = result_Gratiot, n.iter = 10000, adaptive=TRUE, parametersMCMC = pmcmc, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) # Read a file with result data(result_Gratiot_mcmc) 1-rejectionRate(as.mcmc(result_Gratiot_mcmc)) summary(result_Gratiot, resultmcmc=result_Gratiot_mcmc) ## End(Not run)## Not run: library(phenology) data(result_Gratiot) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg <- par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) # generate data for mcmc run pmcmc <- phenology_MHmcmc_p(result_Gratiot, accept = TRUE) result_Gratiot_mcmc <- phenology_MHmcmc(result = result_Gratiot, n.iter = 10000, adaptive=TRUE, parametersMCMC = pmcmc, n.chains = 1, n.adapt = 0, thin = 1, trace = FALSE) # Read a file with result data(result_Gratiot_mcmc) 1-rejectionRate(as.mcmc(result_Gratiot_mcmc)) summary(result_Gratiot, resultmcmc=result_Gratiot_mcmc) ## End(Not run)
Result of the fit of Leatherback nest counts from Gratiot et al. (2006) Figure 1 The phenology has been fitted with MinE, MinB, Max, Flat, LengthB, LengthE, Peak, Theta, Alpha, Beta, Tau, Phi, Delta
result_Gratiot1result_Gratiot1
A list with Gratiot data and the result of the fit.
Result of the fit of Leatherback nest counts from Gratiot et al. (2006) Figure 1
Marc Girondot [email protected]
Gratiot, N., Gratiot, J., de Thoisy, B. & Kelle, L. 2006. Estimation of marine turtles nesting season from incomplete data ; statistical adjustment of a sinusoidal function. Animal Conservation, 9, 95-102.
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Read a file with result data(result_Gratiot1)library(phenology) # Read a file with result data(result_Gratiot1)
Result of the fit of Leatherback nest counts from Gratiot et al. (2006) Figure 1 The phenology has been fitted with MinE, MinB, Max, Flat, LengthB, LengthE, Peak, Theta, Alpha, Beta, Tau, Phi, Delta, Alpha1, Beta1, Tau1, Phi1, Delta1.
result_Gratiot2result_Gratiot2
A list with Gratiot data and the result of the fit.
Result of the fit of Leatherback nest counts from Gratiot et al. (2006) Figure 1
Marc Girondot [email protected]
Gratiot, N., Gratiot, J., de Thoisy, B. & Kelle, L. 2006. Estimation of marine turtles nesting season from incomplete data ; statistical adjustment of a sinusoidal function. Animal Conservation, 9, 95-102.
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap(),
summary.phenologyout()
library(phenology) # Read a file with result data(result_Gratiot2)library(phenology) # Read a file with result data(result_Gratiot2)
Model of remigration interval
Note that r, s and t are conditional probabilities. If c is null, then return probabilities are
estimated from r. r can be named vector. For example:
r <- c(r1=0.5, r2=0.60, r3=1) is equivalent to c <- c(c1=0.5, c2=0.3, c3=0.2)
The vector of r described the probability that a female returned after
1, 2, 3 years among those who have not nested before.
The vector of r is the same but defining the return probability for an initial female.
RI(s, t, r = NULL, c = NULL, p)RI(s, t, r = NULL, c = NULL, p)
s |
Time-conditional probability of survival |
t |
Time-conditional probability of tag retention |
r |
Time-conditional probability of return |
c |
Probability of return |
p |
Annual probability of observation |
RI returns an expected remigration interval
Return a remigration interval.
Marc Girondot
Other Model of Remigration Interval:
Bayesian.remigration(),
LnRI_norm(),
plot.Remigration()
## Not run: library(phenology) # Example s <- c(s1=1, s2=1, s3=1, s4=1, s5=1) t <- c(t1=0.95, t2=1, t3=1, t4=1, t5=1) r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # r is equivalent to c <- c(c1=0.1, c2=0.72, c3=0.126, c4=0.0378, c5=0.0162) # Then the true remigration interval is: ri_true <- sum(1:5*c[1:5]) s_ri <- NULL for (sx in seq(from=0.01, to=1, by=0.01)) { s[] <- sx ri1 <- RI(s=s, t=t, r=r, p=p) s_ri <- c(s_ri,sum(1:5*ri1)/sum(ri1)) } par(mar=c(4, 4, 1, 1)+0.4) plot(x=seq(from=0.01, to=1, by=0.01), y=s_ri, type="l", las=1, bty="n", ylim=c(0, 4), xlab="Annuual survival probabilities", ylab="Naive Remigration Interval", main="") segments(x0=0.01, x1=1, y0=ri_true, y1=ri_true, lty=2, col="red") legend("topright", legend="True remigration interval", lty=2, col="red") ## End(Not run)## Not run: library(phenology) # Example s <- c(s1=1, s2=1, s3=1, s4=1, s5=1) t <- c(t1=0.95, t2=1, t3=1, t4=1, t5=1) r <- c(r1=0.1, r2=0.8, r3=0.7, r4=0.7, r5=1) p <- c(p1=0.6, p2=0.6, p3=0.6, p4=0.6, p5=0.6) # r is equivalent to c <- c(c1=0.1, c2=0.72, c3=0.126, c4=0.0378, c5=0.0162) # Then the true remigration interval is: ri_true <- sum(1:5*c[1:5]) s_ri <- NULL for (sx in seq(from=0.01, to=1, by=0.01)) { s[] <- sx ri1 <- RI(s=s, t=t, r=r, p=p) s_ri <- c(s_ri,sum(1:5*ri1)/sum(ri1)) } par(mar=c(4, 4, 1, 1)+0.4) plot(x=seq(from=0.01, to=1, by=0.01), y=s_ri, type="l", las=1, bty="n", ylim=c(0, 4), xlab="Annuual survival probabilities", ylab="Naive Remigration Interval", main="") segments(x0=0.01, x1=1, y0=ri_true, y1=ri_true, lty=2, col="red") legend("topright", legend="True remigration interval", lty=2, col="red") ## End(Not run)
This function is used to shift sinusoid parameters from ”, '1' or '2'.
shift_sinusoid(parameters = NULL, from = "", to = "1")shift_sinusoid(parameters = NULL, from = "", to = "1")
parameters |
set of parameters |
from |
The number of series to change |
to |
The number of series to change |
shift_sinusoid shift sinusoid information.
Return a set of modified parameters
Marc Girondot
# Read a file with data library("phenology") data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Fix parameter FLat to 0 pfixed=c(Flat=0) # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=pfixed) # Fit is done ## Not run: result_Gratiot_Flat<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=pfixed) ## End(Not run) data(result_Gratiot_Flat) parg<-extract_result(result_Gratiot_Flat) # Add data for one sinusoid superimposed # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] parg<-c(parg, Alpha=0.5, Beta=0.8, Delta=3, Phi=15) # Tau is fixed to 1 pfixed=c(Flat=0, Tau=1) # Run the optimisation ## Not run: result_Gratiot1<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=pfixed) # Plot the phenology output1<-plot(result_Gratiot1, moon=TRUE) #' ## End(Not run) data(result_Gratiot1) # Extract the fitted parameters parg1<-extract_result(result_Gratiot1) # Shift sunusoid information to the '1' parg2<-shift_sinusoid(parameters=parg1, from="", to="1") # Tau is fixed to 1 pfixed=c(Flat=0, Tau1=1, Tau=1) # Add data for another sinusoid superimposed # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] parg<-c(parg2, Alpha=0.5, Beta=0.8, Delta=3, Phi=10) # Run the optimisation ## Not run: result_Gratiot2<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=pfixed) # Plot the phenology output2<-plot(result_Gratiot2, moon=TRUE) ## End(Not run) data(result_Gratiot2)# Read a file with data library("phenology") data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Fix parameter FLat to 0 pfixed=c(Flat=0) # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=pfixed) # Fit is done ## Not run: result_Gratiot_Flat<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=pfixed) ## End(Not run) data(result_Gratiot_Flat) parg<-extract_result(result_Gratiot_Flat) # Add data for one sinusoid superimposed # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] parg<-c(parg, Alpha=0.5, Beta=0.8, Delta=3, Phi=15) # Tau is fixed to 1 pfixed=c(Flat=0, Tau=1) # Run the optimisation ## Not run: result_Gratiot1<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=pfixed) # Plot the phenology output1<-plot(result_Gratiot1, moon=TRUE) #' ## End(Not run) data(result_Gratiot1) # Extract the fitted parameters parg1<-extract_result(result_Gratiot1) # Shift sunusoid information to the '1' parg2<-shift_sinusoid(parameters=parg1, from="", to="1") # Tau is fixed to 1 pfixed=c(Flat=0, Tau1=1, Tau=1) # Add data for another sinusoid superimposed # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] parg<-c(parg2, Alpha=0.5, Beta=0.8, Delta=3, Phi=10) # Run the optimisation ## Not run: result_Gratiot2<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=pfixed) # Plot the phenology output2<-plot(result_Gratiot2, moon=TRUE) ## End(Not run) data(result_Gratiot2)
The function summary.IP shows result and estimates confidence interval.
## S3 method for class 'IP' summary(object, ..., N = NULL, probs = c(0.025, 0.975))## S3 method for class 'IP' summary(object, ..., N = NULL, probs = c(0.025, 0.975))
object |
A file of class IP |
... |
Not used |
N |
Number of replicates |
probs |
Probability of confidence interval |
summary.IP prints the information from a IP object.
Nothing
Marc Girondot
Other Model of Internesting Period:
IPFit(),
IPModel(),
IPPredict(),
plot.IP()
## Not run: library(phenology) # Read a file with data ## End(Not run)## Not run: library(phenology) # Read a file with data ## End(Not run)
The function summary.phenology shows result and estimates confidence interval.
If several years are analyzed, the sum_synthesis result can be obtained only if there is
not a mix of bisextile and non-bisextile years.
## S3 method for class 'phenology' summary( object, resultmcmc = NULL, season = NULL, chain = 1, series = "all", replicate.CI.mcmc = "all", replicate.CI = 10000, level = 0.95, print = TRUE, ... )## S3 method for class 'phenology' summary( object, resultmcmc = NULL, season = NULL, chain = 1, series = "all", replicate.CI.mcmc = "all", replicate.CI = 10000, level = 0.95, print = TRUE, ... )
object |
A result file generated by fit_phenology |
resultmcmc |
A mcmc object |
season |
The number of season to analyze |
chain |
The number of chain to be used in resultmcmc |
series |
Names of the series to be analyzed or "all" |
replicate.CI.mcmc |
Number of iterations to be used or "all" |
replicate.CI |
Number of replicates for ML resampling |
level |
Level to estimate confidence interval or credibility interval |
print |
Should information be shown |
... |
Not used |
summary.phenology prints the information from a result object.
A list with five objects: synthesis is a data.frame with global estimate of nesting.
details_MCMC, details_ML, details_mean are lists with day by day information for each series, and
sum_synthesis is the synthesis of the sum of all analyzed time-series.
Marc Girondot [email protected]
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenologymap(),
summary.phenologyout()
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Display information from the result s <- summary(result_Gratiot) # Using mcmc s <- summary(object=result_Gratiot, resultmcmc=result_Gratiot_mcmc) ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Display information from the result s <- summary(result_Gratiot) # Using mcmc s <- summary(object=result_Gratiot, resultmcmc=result_Gratiot_mcmc) ## End(Not run)
summary.phenologymap print information on a phenologymap object
## S3 method for class 'phenologymap' summary(object, ...)## S3 method for class 'phenologymap' summary(object, ...)
object |
A map generated with map_phenology. |
... |
Not used |
Return None
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologyout()
library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result ## Not run: map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) ## End(Not run) data(map_Gratiot) # Print the information on a map summary(map_Gratiot)library("phenology") # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation ## Not run: result_Gratiot<-fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) ## End(Not run) data(result_Gratiot) # Extract the fitted parameters parg1<-extract_result(result_Gratiot) # Add constant Alpha and Tau values # [day d amplitude=(Alpha+Nd*Beta)^Tau with Nd being the number of counts for day d] pfixed<-c(parg1, Alpha=0, Tau=1) pfixed<-pfixed[-which(names(pfixed)=="Theta")] # The only fitted parameter will be Beta parg2<-c(Beta=0.5, parg1["Theta"]) # Generate a likelihood map # [default Phi=seq(from=0.1, to=20, length.out=100) but it is very long] # Take care, it takes 20 hours ! The data map_Gratiot has the result ## Not run: map_Gratiot<-map_phenology(data=data_Gratiot, Phi=seq(from=0.1, to=20, length.out=100), fitted.parameters=parg2, fixed.parameters=pfixed) ## End(Not run) data(map_Gratiot) # Print the information on a map summary(map_Gratiot)
The function summary.phenologyout displays the output from a plot.
## S3 method for class 'phenologyout' summary(object, ...)## S3 method for class 'phenologyout' summary(object, ...)
object |
An output generated by plot.phenology() ou summary.phenology() |
... |
Not used |
summary.phenologyout prints the information from a result object.
None
Marc Girondot
Other Phenology model:
AutoFitPhenology(),
BE_to_LBLE(),
Gratiot,
LBLE_to_BE(),
LBLE_to_L(),
L_to_LBLE(),
MarineTurtles_2002,
MinBMinE_to_Min(),
adapt_parameters(),
add_SE(),
add_phenology(),
extract_result(),
fit_phenology(),
likelihood_phenology(),
logLik.phenology(),
map_Gratiot,
map_phenology(),
par_init(),
phenology(),
phenology2fitRMU(),
phenology_MHmcmc(),
phenology_MHmcmc_p(),
plot.phenology(),
plot.phenologymap(),
plot_delta(),
plot_phi(),
print.phenology(),
print.phenologymap(),
print.phenologyout(),
remove_site(),
result_Gratiot,
result_Gratiot1,
result_Gratiot2,
result_Gratiot_Flat,
result_Gratiot_mcmc,
summary.phenology(),
summary.phenologymap()
## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # Show the output summary(output) ## End(Not run)## Not run: library(phenology) # Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot data_Gratiot <- add_phenology(Gratiot, name="Complete", reference=as.Date("2001-01-01"), format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot, fixed.parameters=NULL) # Run the optimisation result_Gratiot <- fit_phenology(data=data_Gratiot, fitted.parameters=parg, fixed.parameters=NULL) data(result_Gratiot) # Plot the phenology and get some stats output <- plot(result_Gratiot) # Show the output summary(output) ## End(Not run)
This function formats a CMR dataset to a file that fitCF can use.
If date0 is not null, a 3D TableECFOCF is generated.
3D table (ECF, OCF, period) has two attributes:
table with 5 elements:
begin, end are the first and last elements with counts
final is the last period with information
min and max are the first and last period where a nest could have been laid based on MaxNests value
characteristics with 5 elements:
MinimumDaysBetween2Nest, MeanDaysBetween2Nest MaxNests, date0, length_season
p parameter can be setup to +Inf until begin and after end
TableECFOCF( data = stop("A dataframe with a column 'ID' and a column 'Date'"), columnID = "ID", columnDate = "Date", MinimumDaysBetween2Nest = 7, MeanDaysBetween2Nest = 9.8, MaxNests = 15, date0 = NULL, length_season = floor(365/MeanDaysBetween2Nest) + 1 )TableECFOCF( data = stop("A dataframe with a column 'ID' and a column 'Date'"), columnID = "ID", columnDate = "Date", MinimumDaysBetween2Nest = 7, MeanDaysBetween2Nest = 9.8, MaxNests = 15, date0 = NULL, length_season = floor(365/MeanDaysBetween2Nest) + 1 )
data |
CMR file. |
columnID |
Name of the column in data for unique identifier of females. |
columnDate |
Name of the column in data for morning date when female has been seen on the beach. |
MinimumDaysBetween2Nest |
Number of minimum days between two nests. |
MeanDaysBetween2Nest |
Average number of days between two nests. |
MaxNests |
Maximum number of nests by a female. |
date0 |
Date for the ordinal day 0. |
length_season |
The total length of season based on groups of interclutch intervals. |
TableECFOCF formats a CMR dataset into a file that fitCF can use.
Return a matrix with counts for all OCF and ECF combinations.
Marc Girondot
Other Model of Clutch Frequency:
ECFOCF_f(),
ECFOCF_full(),
fitCF(),
fitCF_MHmcmc(),
fitCF_MHmcmc_p(),
generateCF(),
lnLCF(),
logLik.ECFOCF(),
plot.ECFOCF(),
plot.TableECFOCF()
## Not run: library(phenology) # Example data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) plot(ECFOCF_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) plot(ECFOCF_2002, period=11) ## End(Not run)## Not run: library(phenology) # Example data(MarineTurtles_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002) plot(ECFOCF_2002) ECFOCF_2002 <- TableECFOCF(MarineTurtles_2002, date0=as.Date("2002-01-01")) plot(ECFOCF_2002, period=11) ## End(Not run)
This function compute a model of cumulative tag loss rate for days t
based on a set of parameters, par.
If hessian is not null, it will estimate standard error of the output using numerical delta method is replicates
is null or using resampling if replicates is not null.
Parameters are described in Tagloss_fit.
Tagloss_cumul( t, par = NULL, Hessian = NULL, mcmc = NULL, method = NULL, model_before = NULL, model_after = NULL, model = NULL, replicates = NULL, x = NULL )Tagloss_cumul( t, par = NULL, Hessian = NULL, mcmc = NULL, method = NULL, model_before = NULL, model_after = NULL, model = NULL, replicates = NULL, x = NULL )
t |
Time for which values of model must be estimated |
par |
Parameters |
Hessian |
Hessian matrix of parameters |
mcmc |
A mcmc result |
method |
Can be NULL, "delta", "SE", "Hessian", "MCMC", or "PseudoHessianFromMCMC" |
model_before |
Function to be used before estimation of daily tagloss rate |
model_after |
Function to be used after estimation of daily tagloss rate |
model |
The model of parameter to use, can be N2, N1 or N0; or NLR, NL0, N0R, or N00 or NULL if hessian is NULL. |
replicates |
Number of replicates to estimate se of output for resampling method |
x |
A Tagloss fitted model |
Tagloss_cumul returns the cumulative rate of tag loss.
Return the cumulative rate of tag loss if hessian is null or a data.frame with distribution of cumulative rate of tag loss if hessian is not null.
Marc Girondot [email protected]
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: # Example library(phenology) # Data from Rivalan et al. 2005 - Table 2, line 1 - Fig 1D par <- c(a0_2=-5.43E-2, a1_2=-103.52, a2_2=0, a3_2=0, a4_2=5.62E-4) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", bty="n", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 2 - Fig 1E par <- c(a0_2=-6.80E-2, a1_2=-81.15, a2_2=-2.20E-4, a3_2=6348.01, a4_2=1.65E-3) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 3 - Fig 1F par <- c(a0_2=-6.93E-2, a1_2=-78.92, a2_2=8.45E-4, a3_2=-16272.76, a4_2=2.87E-4) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 4 - Fig 1C par <- c(a0_2=-1.68E-3, a1_2=-4141.68, a2_2=0, a3_2=0, a4_2=0) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 5 - Fig 1B par <- c(a0_2=-3.77E-4, a1_2=-2000, a2_2=-0.001, a3_2=0, a4_2=5.00E-8) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 6 - Fig 1A par <- c(a0_2=-1E5, a1_2=-2000, a2_2=0, a3_2=4000, a4_2=8.34E-4) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 1 - Fig 1D # With tagloss rate dependency on tage number par <- c(a0_2=-5.43E-2, a1_2=-103.52, a2_2=0, a3_2=0, a4_2=5.62E-4, a0_1=-5.43E-2, a1_1=-103.52, a2_1=0, a3_1=0, a4_1=5.62E-4, delta_1=3.2E-4) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) p2 <- Tagloss_model(t=1:(6*365), par=par, model="2") p1 <- Tagloss_model(t=1:(6*365), par=par, model="1") par(mar=c(4, 5, 2, 1)) plot(x=1:(6*365), y=p2, bty="n", type="l", las=1, ylim=c(0,0.003), ylab="") mtext("Daily tag loss", side=2, line=4) lines(x=1:(6*365), y=p1, col="red") legend("topright", legend=c("2>1", "1>0"), lty=1, col=c("black", "red")) Tagloss_cumul(t=(1:6)*365, par=par) # Without tagloss rate dependency on tag number par <- c(a0_2=-5.43E-2, a1_2=-103.52, a2_2=0, a3_2=0, a4_2=5.62E-4, a0_1=-5.43E-2, a1_1=-103.52, a2_1=0, a3_1=0, a4_1=5.62E-4) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) #### Data from Casale et al. 2017 # Table 1 - Model II par <- c(CasaleModelIIa0_2=-0.0511, CasaleModelIIa1_2=-100, CasaleModelIIa4_2=0.00014) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) # Table 1 - Model IV par <- c(CasaleModelIVa0_2=-0.0132, CasaleModelIVa1_2=-100, CasaleModelIVa2_2=0.0327, CasaleModelIVa3_2=109.98, CasaleModelIVa4_2=0.00011) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) # Table 1 - Model I par <- c(CasaleModelIc_2=0.00027) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) # Table 1 - Model III par <- c(CasaleModelIIIa0_2=1.14E-10, CasaleModelIIIa1_2=-110.04, CasaleModelIIIa4_2=0.00055) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) # Table 1 - Model V par <- c(CasaleModelVa0_2=4.04E-10, CasaleModelVa1_2=-90, CasaleModelVa2_2=-0.0326, CasaleModelVa3_2=100.31, CasaleModelVa4_2=0.00006) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) ## End(Not run)## Not run: # Example library(phenology) # Data from Rivalan et al. 2005 - Table 2, line 1 - Fig 1D par <- c(a0_2=-5.43E-2, a1_2=-103.52, a2_2=0, a3_2=0, a4_2=5.62E-4) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", bty="n", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 2 - Fig 1E par <- c(a0_2=-6.80E-2, a1_2=-81.15, a2_2=-2.20E-4, a3_2=6348.01, a4_2=1.65E-3) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 3 - Fig 1F par <- c(a0_2=-6.93E-2, a1_2=-78.92, a2_2=8.45E-4, a3_2=-16272.76, a4_2=2.87E-4) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 4 - Fig 1C par <- c(a0_2=-1.68E-3, a1_2=-4141.68, a2_2=0, a3_2=0, a4_2=0) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 5 - Fig 1B par <- c(a0_2=-3.77E-4, a1_2=-2000, a2_2=-0.001, a3_2=0, a4_2=5.00E-8) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 6 - Fig 1A par <- c(a0_2=-1E5, a1_2=-2000, a2_2=0, a3_2=4000, a4_2=8.34E-4) (y <- Tagloss_cumul(t=(1:6)*365, par=par)) plot(y[, "time"], y[, "N2"], type="l", xlab="Days after tagging", ylab="N2 proportion") # Data from Rivalan et al. 2005 - Table 2, line 1 - Fig 1D # With tagloss rate dependency on tage number par <- c(a0_2=-5.43E-2, a1_2=-103.52, a2_2=0, a3_2=0, a4_2=5.62E-4, a0_1=-5.43E-2, a1_1=-103.52, a2_1=0, a3_1=0, a4_1=5.62E-4, delta_1=3.2E-4) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) p2 <- Tagloss_model(t=1:(6*365), par=par, model="2") p1 <- Tagloss_model(t=1:(6*365), par=par, model="1") par(mar=c(4, 5, 2, 1)) plot(x=1:(6*365), y=p2, bty="n", type="l", las=1, ylim=c(0,0.003), ylab="") mtext("Daily tag loss", side=2, line=4) lines(x=1:(6*365), y=p1, col="red") legend("topright", legend=c("2>1", "1>0"), lty=1, col=c("black", "red")) Tagloss_cumul(t=(1:6)*365, par=par) # Without tagloss rate dependency on tag number par <- c(a0_2=-5.43E-2, a1_2=-103.52, a2_2=0, a3_2=0, a4_2=5.62E-4, a0_1=-5.43E-2, a1_1=-103.52, a2_1=0, a3_1=0, a4_1=5.62E-4) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) #### Data from Casale et al. 2017 # Table 1 - Model II par <- c(CasaleModelIIa0_2=-0.0511, CasaleModelIIa1_2=-100, CasaleModelIIa4_2=0.00014) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) # Table 1 - Model IV par <- c(CasaleModelIVa0_2=-0.0132, CasaleModelIVa1_2=-100, CasaleModelIVa2_2=0.0327, CasaleModelIVa3_2=109.98, CasaleModelIVa4_2=0.00011) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) # Table 1 - Model I par <- c(CasaleModelIc_2=0.00027) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) # Table 1 - Model III par <- c(CasaleModelIIIa0_2=1.14E-10, CasaleModelIIIa1_2=-110.04, CasaleModelIIIa4_2=0.00055) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) # Table 1 - Model V par <- c(CasaleModelVa0_2=4.04E-10, CasaleModelVa1_2=-90, CasaleModelVa2_2=-0.0326, CasaleModelVa3_2=100.31, CasaleModelVa4_2=0.00006) phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", decoration = TRUE) Tagloss_cumul(t=(1:6)*365, par=par) ## End(Not run)
This function must be used to get the value of mx in Tagloss_L.
Tagloss_daymax(individuals, what = "max")Tagloss_daymax(individuals, what = "max")
individuals |
Set of indivuals |
what |
By default is max, but can be min, mean or all |
Tagloss_daymax returns the maximum number of days an individual has been observed in a dataset.
Return the maximum number of days an individual has been observed in a dataset.
Marc Girondot [email protected]
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") daymax(data_f_21) ## End(Not run)## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") daymax(data_f_21) ## End(Not run)
This function fits a model of tag loss using a CMR database.
The names of parameters can be:
Model Pfaller et al. (2019):
D1_L2, D2D1_L2, D3D2_L2, A_L2, B_L2, C_L2, delta_L2
D1_R2, D2D1_R2, D3D2_R2, A_R2, B_R2, C_R2, delta_R2
D1_L1, D2D1_L1, D3D2_L1, A_L1, B_L1, C_L1, delta_L1
D1_R1, D2D1_R1, D3D2_R1, A_R1, B_R1, C_R1, delta_R1
D1_2, D2D1_2, D3D2_2, A_2, B_2, C_2, delta_2
D1_1, D2D1_1, D3D2_1, A_1, B_1, C_1, delta_1
pA, pB and pC are the daily probabilities of tag loss with
pA=-logit(A), pB=-logit(B) and pC=-logit(C) .delta is used as: p = p + delta. Nothe that delta can be negative
Tag loss rate is pA at day 1
Tag loss rate changes gradually from pA to pB that is reached at day D1
Tag loss rate is pB from day D1 to day D2=D1+D2D1
Tag loss rate changes gradually from pB to pC that is reached at day D3=D2+D3D2
When parameters from Rivalan et al. (2005) are used:
a0_2, a1_2, a2_2, a3_2, a4_2, delta_2
a0_1, a1_1, a2_1, a3_1, a4_1, delta_1
When parameters from Casale et al. (2017) are used:
Model I
CasaleModelIc_2
CasaleModelIc_1
Model II
CasaleModelIIa0_2, CasaleModelIIa1_2, CasaleModelIIa4_2
CasaleModelIIa0_1, CasaleModelIIa1_1, CasaleModelIIa4_1
Model III
CasaleModelIIIa0_2, CasaleModelIIIa1_2, CasaleModelIIIa4_2
CasaleModelIIIa0_1, CasaleModelIIIa1_1, CasaleModelIIIa4_1
Model IV
CasaleModelIVa0_2, CasaleModelIVa1_2, CasaleModelIVa2_2, CasaleModelIVa3_2, CasaleModelIVa4_2
CasaleModelIVa0_1, CasaleModelIVa1_1, CasaleModelIVa2_1, CasaleModelIVa3_1, CasaleModelIVa4_1
Model V
CasaleModelVa0_2, CasaleModelVa1_2, CasaleModelVa2_2, CasaleModelVa3_2, CasaleModelVa4_2
CasaleModelVa0_1, CasaleModelVa1_1, CasaleModelVa2_1, CasaleModelVa3_1, CasaleModelVa4_1
If only one parameter is fitted, method must be "Brent" and upper and lower
parameters must be set up with finite values.
model_before can be ""par['a0_1']=par['a0_2'];par['a1_1']=par['a1_2']". model_after can be "p1=p2"
Tagloss_fit( data = stop("A database formated using Tagloss_format() must be used"), fitted.parameters = NULL, fixed.parameters = NULL, model_before = NULL, model_after = NULL, control = list(trace = 1, maxit = 10000), method = "Nelder-Mead", lower = -Inf, upper = Inf, hessian = FALSE, mc.cores = detectCores(all.tests = FALSE, logical = TRUE), groups = NULL )Tagloss_fit( data = stop("A database formated using Tagloss_format() must be used"), fitted.parameters = NULL, fixed.parameters = NULL, model_before = NULL, model_after = NULL, control = list(trace = 1, maxit = 10000), method = "Nelder-Mead", lower = -Inf, upper = Inf, hessian = FALSE, mc.cores = detectCores(all.tests = FALSE, logical = TRUE), groups = NULL )
data |
An object formated using Tagloss_format |
fitted.parameters |
Set of parameters to be fitted |
fixed.parameters |
Set of fixed parameters |
model_before |
Transformation of parameters before to use Tagloss_model() |
model_after |
Transformation of parameters after to use Tagloss_model() |
control |
Control parameters to be send to optim() |
method |
optim() method |
lower |
Lower value for parameter when Brent method is used |
upper |
Upper value for parameter when Brent method is used |
hessian |
Does the hessian matrix should be estimated |
mc.cores |
Number of cores to use for parallel computing |
groups |
Number of groups for parallel computing |
Tagloss_fit fits a model of tag loss using a CMR database.
Return a list object with the model describing tag loss.
Marc Girondot [email protected]
Rivalan, P., Godfrey, M.H., Prévot-Julliard, A.-C., Girondot, M., 2005. Maximum likelihood estimates of tag loss in leatherback sea turtles. Journal of Wildlife Management 69, 540-548.
Casale, P., Freggi, D., Salvemini, P., 2017. Tag loss is a minor limiting factor in sea turtle tagging programs relying on distant tag returns: the case of Mediterranean loggerhead sea turtles. European Journal of Wildlife Research 63.
Pfaller JB, Williams KL, Frick MG, Shamblin BM, Nairn CJ, Girondot M (2019) Genetic determination of tag loss dynamics in nesting loggerhead turtles: A new chapter in “the tag loss problem”. Marine Biology 166: 97 doi 10.1007/s00227-019-3545-x
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # model fitted by Rivalan et al. 2005 par <- c(a0_2=-5.43E-2, a1_2=-103.52, a4_2=5.62E-4, delta_1=3.2E-4) pfixed <- c(a2_2=0, a3_2=0, a2_1=0, a3_1=0) model_before <- "par['a0_1']=par['a0_2'];par['a1_1']=par['a1_2'];par['a4_1']=par['a4_2']" o <- Tagloss_fit(data=data_f_21, fitted.parameters=par, fixed.parameters=pfixed, model_before=model_before) plot(o, t=1:1000, model="cumul") plot(o, t=1:1000, model="1") plot(o, t=1:1000, model="2", add=TRUE, col="red") # Same data fitted with new model par <- c(D1_1 = 100.15324837975547, A_1 = 5.9576927964120188, B_1 = 8.769924225871069, B_2 = 8.2353860179664125) pfixed <- c(D2D1_1 = 2568, D3D2_1 = 2568, D2D1_2 = 2568, D3D2_2 = 2568) o_4p_p1p2 <- Tagloss_fit(data=data_f_21, fitted.parameters = par, fixed.parameters = pfixed, model_before = "par['C_1']=par['B_1']; par['A_2']=par['A_1']; par['C_2']=par['B_2']; par['D1_2']=par['D1_1']", hessian=TRUE) # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- c('D1_2' = 49.78891736351531, 'D2D1_2' = 1059.3635769732305, 'D3D2_2' = 12.434313273804602, 'A_2' = 5.2238379144659683, 'B_2' = 8.0050044071275543, 'C_2' = 8.4317863609499675, 'D1_1' = 701.80273287212935, 'D2D1_1' = 0.010951749100596819, 'D3D2_1' = 3773.6290607434876, 'A_1' = 205.42435592344776, 'B_1' = 9.9598342503239863, 'C_1' = 6.7234868237164722) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, hessian = TRUE) plot(o, model="1", col="red") plot(o, model="2", col="blue", add=TRUE) legend("topright", legend=c("2->1", "1->0"), lty=1, col=c("blue", "red")) ## End(Not run)## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # model fitted by Rivalan et al. 2005 par <- c(a0_2=-5.43E-2, a1_2=-103.52, a4_2=5.62E-4, delta_1=3.2E-4) pfixed <- c(a2_2=0, a3_2=0, a2_1=0, a3_1=0) model_before <- "par['a0_1']=par['a0_2'];par['a1_1']=par['a1_2'];par['a4_1']=par['a4_2']" o <- Tagloss_fit(data=data_f_21, fitted.parameters=par, fixed.parameters=pfixed, model_before=model_before) plot(o, t=1:1000, model="cumul") plot(o, t=1:1000, model="1") plot(o, t=1:1000, model="2", add=TRUE, col="red") # Same data fitted with new model par <- c(D1_1 = 100.15324837975547, A_1 = 5.9576927964120188, B_1 = 8.769924225871069, B_2 = 8.2353860179664125) pfixed <- c(D2D1_1 = 2568, D3D2_1 = 2568, D2D1_2 = 2568, D3D2_2 = 2568) o_4p_p1p2 <- Tagloss_fit(data=data_f_21, fitted.parameters = par, fixed.parameters = pfixed, model_before = "par['C_1']=par['B_1']; par['A_2']=par['A_1']; par['C_2']=par['B_2']; par['D1_2']=par['D1_1']", hessian=TRUE) # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- c('D1_2' = 49.78891736351531, 'D2D1_2' = 1059.3635769732305, 'D3D2_2' = 12.434313273804602, 'A_2' = 5.2238379144659683, 'B_2' = 8.0050044071275543, 'C_2' = 8.4317863609499675, 'D1_1' = 701.80273287212935, 'D2D1_1' = 0.010951749100596819, 'D3D2_1' = 3773.6290607434876, 'A_1' = 205.42435592344776, 'B_1' = 9.9598342503239863, 'C_1' = 6.7234868237164722) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, hessian = TRUE) plot(o, model="1", col="red") plot(o, model="2", col="blue", add=TRUE) legend("topright", legend=c("2->1", "1->0"), lty=1, col=c("blue", "red")) ## End(Not run)
This function formats a CMR dataset to a file that Tagloss_L can use.
The format of data is a data.frame with 4 columns:
ID is the column with the permanent identification code
L is the column with the non-permanent code located at left
R is the column with the non-permanent code located at right
Date is the column with the date of observation
Note that R and L columns can be exchanged if 21 model is used.
Tagloss_format( data, model = "21", progressbar = TRUE, column.Date = "Date", column.ID = "ID", column.left = "L", column.right = "R", keeponly2 = TRUE )Tagloss_format( data, model = "21", progressbar = TRUE, column.Date = "Date", column.ID = "ID", column.left = "L", column.right = "R", keeponly2 = TRUE )
data |
CMR file |
model |
Can be "21" or "LR" |
progressbar |
Is a progressbar been shown? |
column.Date |
Name of the column with Date |
column.ID |
Name of the column with ID |
column.left |
Name of the column with left tag |
column.right |
Name of the column with right tag |
keeponly2 |
If TRUE, keep only individuals tagged with 2 tags |
Tagloss_format formats a CMR dataset into a file that Tagloss_L can use.
Return the maximum number of days an individual has been observed in a dataset.
Marc Girondot
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example head(outLR) data_f_21 <- Tagloss_format(outLR, model="21") data_f_LR <- Tagloss_format(outLR, model="LR") ## End(Not run)## Not run: library(phenology) # Example head(outLR) data_f_21 <- Tagloss_format(outLR, model="21") data_f_LR <- Tagloss_format(outLR, model="LR") ## End(Not run)
This function must be used within optim().
model_before is applied to the par parameter.
model_after is applied after par is separated in p1, p2, pL1, pL2, pR1 and pR2 parameters.
progressbar is set to FALSE if mc.cores is different from 1.
If days.maximum is not indicated, it is estimated using Tagloss_daymax().
Tagloss_L( individuals, par, days.maximum = NULL, fixed.parameters = NULL, model_before = NULL, model_after = NULL, names.par = NULL, groups = NULL, mc.cores = detectCores(all.tests = FALSE, logical = TRUE), progressbar = FALSE )Tagloss_L( individuals, par, days.maximum = NULL, fixed.parameters = NULL, model_before = NULL, model_after = NULL, names.par = NULL, groups = NULL, mc.cores = detectCores(all.tests = FALSE, logical = TRUE), progressbar = FALSE )
individuals |
Set of individuals |
par |
Set of parameters |
days.maximum |
Maximum number of days. Can be determined using Tagloss_daymax() |
fixed.parameters |
Set of fixed parameters |
model_before |
Transformation of parameters before to use Tagloss_model() |
model_after |
Transformation of parameters after to use Tagloss_model() |
names.par |
Name of parameters. Normally unused. |
groups |
Number of groups for parallel computing |
mc.cores |
Number of cores to use for parallel computing |
progressbar |
Is shown a progressbar? |
Tagloss_L returns the -log likelihood of a set of individuals under a model of tagloss.
Return the -log likelihood of a set of individuals
Marc Girondot
Other Model of Tag-loss:
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example with 21 format of data data_f_21 <- Tagloss_format(outLR, model="21") par <- structure(c(49.5658922243074, 808.136085362158, 106.283783786853, 5.22150592456511, 8.00608716525864, 8.32718202233396, 150.612916258503, 715.865805125223, 2242.06574225966, 119.212383120678, 10.1860735529433, 7.14231725937626), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")) pfixed <- NULL # All the data are analyzed; the N20 are very long to compute Tagloss_L(individuals=data_f_21, par=par, days.maximum=Tagloss_daymax(data_f_21), fixed.parameters=pfixed, mc.cores=1, progressbar=TRUE) # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) Tagloss_L(individuals=data_f_21_fast, par=par, days.maximum=Tagloss_daymax(data_f_21_fast), fixed.par=pfixed, mc.cores=1, progressbar=TRUE) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par) # Here it is the result of the previous function o <- structure(list(par = structure(c(49.5658922243074, 808.136085362158, 106.283783786853, 5.22150592456511, 8.00608716525864, 8.32718202233396, 150.612916258503, 715.865805125223, 2242.06574225966, 119.212383120678, 10.1860735529433, 7.14231725937626), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")), value = 5841.93084262461, counts = structure(c(1093L, NA), .Names = c("function", "gradient")), convergence = 0L, message = NULL, hessian = structure(c(0.0469808583147824, 0.000133240973809734, 6.68478605803102e-05, -2.53581288234273, -1.25931342154217, -0.124650568977813, -2.46700437855907e-05, -1.11413100967184e-05, -3.18323145620525e-06, 0, -0.0182945996130002, -0.00510601694259094, 0.000133240973809734, 1.45519152283669e-05, 7.50333128962666e-06, -0.00452587300969753, -0.0191316757991444, -0.0255117811320815, -1.13686837721616e-06, -1.36424205265939e-06, -2.27373675443232e-07, 0, 0.000335830918629654, -0.000448608261649497, 6.68478605803102e-05, 7.50333128962666e-06, 4.32009983342141e-06, -0.00226373231271282, -0.00954059942159802, -0.0127809016703395, -4.54747350886464e-07, -4.54747350886464e-07, -2.27373675443232e-07, 0, 0.000176896719494835, -0.000224190443987027, -2.53581288234273, -0.00452587300969753, -0.00226373231271282, 223.422489398217, 41.4073996353181, 3.77875949197914, 0.000986460690910462, 0.000398813426727429, 0.000117665877041873, 0, 0.727547330825473, 0.194675862985605, -1.25931342154217, -0.0191316757991444, -0.00954059942159802, 41.4073996353181, 189.534394394286, 28.3386068531399, 0.00216437001654413, 0.00241834641201422, 0.000652562448522076, 0, 0.841939595375152, 1.0472297162778, -0.124650568977813, -0.0255117811320815, -0.0127809016703395, 3.77875949197914, 28.3386068531399, 70.250493081403, -0.00022441781766247, -0.000161662683240138, 0.000257614374277182, 0, -0.578908839088399, 1.08917492980254, -2.46700437855907e-05, -1.13686837721616e-06, -4.54747350886464e-07, 0.000986460690910462, 0.00216437001654413, -0.00022441781766247, 0.000148247636388987, 0.000145519152283669, 3.97903932025656e-05, 0, 0.0156976511789253, 0.0678746800986119, -1.11413100967184e-05, -1.36424205265939e-06, -4.54747350886464e-07, 0.000398813426727429, 0.00241834641201422, -0.000161662683240138, 0.000145519152283669, 0.000145519152283669, 3.9676706364844e-05, 0, 0.0138438736030366, 0.0678776359563926, -3.18323145620525e-06, -2.27373675443232e-07, -2.27373675443232e-07, 0.000117665877041873, 0.000652562448522076, 0.000257614374277182, 3.97903932025656e-05, 3.9676706364844e-05, 1.77351466845721e-05, 0, 0.00317095327773131, 0.0316927071253303, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0182945996130002, 0.000335830918629654, 0.000176896719494835, 0.727547330825473, 0.841939595375152, -0.578908839088399, 0.0156976511789253, 0.0138438736030366, 0.00317095327773131, 0, 8.85630879565724, 4.44044781033881, -0.00510601694259094, -0.000448608261649497, -0.000224190443987027, 0.194675862985605, 1.0472297162778, 1.08917492980254, 0.0678746800986119, 0.0678776359563926, 0.0316927071253303, 0, 4.44044781033881, 88.8524673428037 ), .Dim = c(12L, 12L), .Dimnames = list(c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1"), c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1" )))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1500:3000, model="1", scale=1000, add=TRUE) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) plot(o, t=1:300, model="2", scale=1000, ylim=c(0, 3), col="red", hessian=o$hessian) plot(o, t=1:300, model="1", scale=1000, add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with fixed.parameters data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- structure(c(49.5658922243074, 5.22150592456511, 8.00608716525864, 50.612916258503, 6, 9), .Names = c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")) pfixed <- c(D2D1_2=10000, D3D2_2=10000, C_2=0, D2D1_1=10000, D3D2_1=10000, C_1=0) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, fixed.parameters=pfixed) # Here it is the result of the previous function o <- structure(list(par = structure(c(55.2184044121564, 5.2630294044259, 8.13359029885985, 14269.9757684677, 21.8702023948044, 6.46586480967269 ), .Names = c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")), value = 5853.64634357369, counts = structure(c(757L, NA), .Names = c("function", "gradient" )), convergence = 0L, message = NULL, hessian = structure(c(0.036636720324168, -2.26385645873961, -1.2330608569755, -2.95585778076202e-06, -2.27373675443232e-07, -0.0399197688238928, -2.26385645873961, 232.345637869003, 47.1904784262733, 0.000118689058581367, 7.50333128962666e-06, 1.69928603099834, -1.2330608569755, 47.1904784262733, 304.432723851278, 0.000196678229258396, 1.36424205265939e-06, 2.8553522497532, -2.95585778076202e-06, 0.000118689058581367, 0.000196678229258396, 4.54747350886464e-07, 0, 0.00741636085876962, -2.27373675443232e-07, 7.50333128962666e-06, 1.36424205265939e-06, 0, 4.00177668780088e-05, 8.79936123965308e-05, -0.0399197688238928, 1.69928603099834, 2.8553522497532, 0.00741636085876962, 8.79936123965308e-05, 107.941018768543), .Dim = c(6L, 6L), .Dimnames = list( c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1"), c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1500:3000, model="1", scale=1000, add=TRUE) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) plot(o, t=1:300, model="2", scale=1000, ylim=c(0, 3), col="red", hessian=o$hessian) plot(o, t=1:300, model="1", scale=1000, add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with delta data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- structure(c(45.8764973711504, 5.22489974562498, 8.07602162728874, -0.865444694177429), .Names = c("D1_2", "A_2", "B_2", "delta" )) pfixed <- c(D2D1_2=10000, D3D2_2=10000, C_2=0) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, fixed.parameters=pfixed) # Here it is the result of the previous function o <- structure(list(par = structure(c(45.9035484983855, 5.22576211343279, 8.07585745169786, -0.865706100004634), .Names = c("D1_2", "A_2", "B_2", "delta")), value = 5913.716964613, counts = structure(c(91L, NA), .Names = c("function", "gradient")), convergence = 0L, message = NULL, hessian = structure(c(0.0644593001197791, -2.88983483187621, -1.49161280660337, -0.0875163550517755, -2.88983483187621, 221.02317802819, 45.3729608125286, 3.73816044429987, -1.49161280660337, 45.3729608125286, 440.129730122862, 30.4781699469459, -0.0875163550517755, 3.73816044429987, 30.4781699469459, 9.47964940678503), .Dim = c(4L, 4L), .Dimnames = list(c("D1_2", "A_2", "B_2", "delta"), c("D1_2", "A_2", "B_2", "delta")))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1:3000, model="1", scale=1000, col="blue", add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with model_after data_f_LR <- Tagloss_format(outLR, model="LR") par <- structure(c(72.0399239978454, 58.1034231071992, 645.068735669251, 5.10791337470247, 3538.47220045768, 7.83358940767931), .Names = c("D1_L2", "D2D1_L2", "D3D2_L2", "A_L2", "B_L2", "C_L2")) pfixed <- NULL # A progress bar can be shown when one core is used system.time( print(Tagloss_L(individuals=data_f_LR, par=par, days.maximum=Tagloss_daymax(data_f_LR), fixed.parameters=pfixed, mc.cores=1, model_after="pR2=pL2;pR1=pL2;pL1=pL2", progressbar = TRUE)) ) # When parallel computing is done, no progress bar can be shown system.time( print(Tagloss_L(individuals=data_f_LR, par=par, days.maximum=Tagloss_daymax(data_f_LR), fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2")) ) # The NLR_00 are very long to calculate data_f_LR_fast <- subset(data_f_LR, subset=(is.na(data_f_LR$NLR_00))) system.time( print(Tagloss_L(individuals=data_f_LR_fast, par=par, days.maximum=Tagloss_daymax(data_f_LR_fast), fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2")) ) o <- Tagloss_fit(data=data_f_LR_fast, fitted.parameters=par, fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2") par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") ## End(Not run)## Not run: library(phenology) # Example with 21 format of data data_f_21 <- Tagloss_format(outLR, model="21") par <- structure(c(49.5658922243074, 808.136085362158, 106.283783786853, 5.22150592456511, 8.00608716525864, 8.32718202233396, 150.612916258503, 715.865805125223, 2242.06574225966, 119.212383120678, 10.1860735529433, 7.14231725937626), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")) pfixed <- NULL # All the data are analyzed; the N20 are very long to compute Tagloss_L(individuals=data_f_21, par=par, days.maximum=Tagloss_daymax(data_f_21), fixed.parameters=pfixed, mc.cores=1, progressbar=TRUE) # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) Tagloss_L(individuals=data_f_21_fast, par=par, days.maximum=Tagloss_daymax(data_f_21_fast), fixed.par=pfixed, mc.cores=1, progressbar=TRUE) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par) # Here it is the result of the previous function o <- structure(list(par = structure(c(49.5658922243074, 808.136085362158, 106.283783786853, 5.22150592456511, 8.00608716525864, 8.32718202233396, 150.612916258503, 715.865805125223, 2242.06574225966, 119.212383120678, 10.1860735529433, 7.14231725937626), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")), value = 5841.93084262461, counts = structure(c(1093L, NA), .Names = c("function", "gradient")), convergence = 0L, message = NULL, hessian = structure(c(0.0469808583147824, 0.000133240973809734, 6.68478605803102e-05, -2.53581288234273, -1.25931342154217, -0.124650568977813, -2.46700437855907e-05, -1.11413100967184e-05, -3.18323145620525e-06, 0, -0.0182945996130002, -0.00510601694259094, 0.000133240973809734, 1.45519152283669e-05, 7.50333128962666e-06, -0.00452587300969753, -0.0191316757991444, -0.0255117811320815, -1.13686837721616e-06, -1.36424205265939e-06, -2.27373675443232e-07, 0, 0.000335830918629654, -0.000448608261649497, 6.68478605803102e-05, 7.50333128962666e-06, 4.32009983342141e-06, -0.00226373231271282, -0.00954059942159802, -0.0127809016703395, -4.54747350886464e-07, -4.54747350886464e-07, -2.27373675443232e-07, 0, 0.000176896719494835, -0.000224190443987027, -2.53581288234273, -0.00452587300969753, -0.00226373231271282, 223.422489398217, 41.4073996353181, 3.77875949197914, 0.000986460690910462, 0.000398813426727429, 0.000117665877041873, 0, 0.727547330825473, 0.194675862985605, -1.25931342154217, -0.0191316757991444, -0.00954059942159802, 41.4073996353181, 189.534394394286, 28.3386068531399, 0.00216437001654413, 0.00241834641201422, 0.000652562448522076, 0, 0.841939595375152, 1.0472297162778, -0.124650568977813, -0.0255117811320815, -0.0127809016703395, 3.77875949197914, 28.3386068531399, 70.250493081403, -0.00022441781766247, -0.000161662683240138, 0.000257614374277182, 0, -0.578908839088399, 1.08917492980254, -2.46700437855907e-05, -1.13686837721616e-06, -4.54747350886464e-07, 0.000986460690910462, 0.00216437001654413, -0.00022441781766247, 0.000148247636388987, 0.000145519152283669, 3.97903932025656e-05, 0, 0.0156976511789253, 0.0678746800986119, -1.11413100967184e-05, -1.36424205265939e-06, -4.54747350886464e-07, 0.000398813426727429, 0.00241834641201422, -0.000161662683240138, 0.000145519152283669, 0.000145519152283669, 3.9676706364844e-05, 0, 0.0138438736030366, 0.0678776359563926, -3.18323145620525e-06, -2.27373675443232e-07, -2.27373675443232e-07, 0.000117665877041873, 0.000652562448522076, 0.000257614374277182, 3.97903932025656e-05, 3.9676706364844e-05, 1.77351466845721e-05, 0, 0.00317095327773131, 0.0316927071253303, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.0182945996130002, 0.000335830918629654, 0.000176896719494835, 0.727547330825473, 0.841939595375152, -0.578908839088399, 0.0156976511789253, 0.0138438736030366, 0.00317095327773131, 0, 8.85630879565724, 4.44044781033881, -0.00510601694259094, -0.000448608261649497, -0.000224190443987027, 0.194675862985605, 1.0472297162778, 1.08917492980254, 0.0678746800986119, 0.0678776359563926, 0.0316927071253303, 0, 4.44044781033881, 88.8524673428037 ), .Dim = c(12L, 12L), .Dimnames = list(c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1"), c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1" )))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1500:3000, model="1", scale=1000, add=TRUE) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) plot(o, t=1:300, model="2", scale=1000, ylim=c(0, 3), col="red", hessian=o$hessian) plot(o, t=1:300, model="1", scale=1000, add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with fixed.parameters data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- structure(c(49.5658922243074, 5.22150592456511, 8.00608716525864, 50.612916258503, 6, 9), .Names = c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")) pfixed <- c(D2D1_2=10000, D3D2_2=10000, C_2=0, D2D1_1=10000, D3D2_1=10000, C_1=0) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, fixed.parameters=pfixed) # Here it is the result of the previous function o <- structure(list(par = structure(c(55.2184044121564, 5.2630294044259, 8.13359029885985, 14269.9757684677, 21.8702023948044, 6.46586480967269 ), .Names = c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")), value = 5853.64634357369, counts = structure(c(757L, NA), .Names = c("function", "gradient" )), convergence = 0L, message = NULL, hessian = structure(c(0.036636720324168, -2.26385645873961, -1.2330608569755, -2.95585778076202e-06, -2.27373675443232e-07, -0.0399197688238928, -2.26385645873961, 232.345637869003, 47.1904784262733, 0.000118689058581367, 7.50333128962666e-06, 1.69928603099834, -1.2330608569755, 47.1904784262733, 304.432723851278, 0.000196678229258396, 1.36424205265939e-06, 2.8553522497532, -2.95585778076202e-06, 0.000118689058581367, 0.000196678229258396, 4.54747350886464e-07, 0, 0.00741636085876962, -2.27373675443232e-07, 7.50333128962666e-06, 1.36424205265939e-06, 0, 4.00177668780088e-05, 8.79936123965308e-05, -0.0399197688238928, 1.69928603099834, 2.8553522497532, 0.00741636085876962, 8.79936123965308e-05, 107.941018768543), .Dim = c(6L, 6L), .Dimnames = list( c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1"), c("D1_2", "A_2", "B_2", "D1_1", "A_1", "B_1")))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1500:3000, model="1", scale=1000, add=TRUE) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) plot(o, t=1:300, model="2", scale=1000, ylim=c(0, 3), col="red", hessian=o$hessian) plot(o, t=1:300, model="1", scale=1000, add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with delta data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- structure(c(45.8764973711504, 5.22489974562498, 8.07602162728874, -0.865444694177429), .Names = c("D1_2", "A_2", "B_2", "delta" )) pfixed <- c(D2D1_2=10000, D3D2_2=10000, C_2=0) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, fixed.parameters=pfixed) # Here it is the result of the previous function o <- structure(list(par = structure(c(45.9035484983855, 5.22576211343279, 8.07585745169786, -0.865706100004634), .Names = c("D1_2", "A_2", "B_2", "delta")), value = 5913.716964613, counts = structure(c(91L, NA), .Names = c("function", "gradient")), convergence = 0L, message = NULL, hessian = structure(c(0.0644593001197791, -2.88983483187621, -1.49161280660337, -0.0875163550517755, -2.88983483187621, 221.02317802819, 45.3729608125286, 3.73816044429987, -1.49161280660337, 45.3729608125286, 440.129730122862, 30.4781699469459, -0.0875163550517755, 3.73816044429987, 30.4781699469459, 9.47964940678503), .Dim = c(4L, 4L), .Dimnames = list(c("D1_2", "A_2", "B_2", "delta"), c("D1_2", "A_2", "B_2", "delta")))), .Names = c("par", "value", "counts", "convergence", "message", "hessian"), class = c("list", "Tagloss")) par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") plot(o, t=1:3000, model="1", scale=1000, col="blue", add=TRUE, hessian=o$hessian) legend("topright", legend=c("2 -> 1", "1 -> 0"), col=c("red", "black"), lty=1) ###### Example with model_after data_f_LR <- Tagloss_format(outLR, model="LR") par <- structure(c(72.0399239978454, 58.1034231071992, 645.068735669251, 5.10791337470247, 3538.47220045768, 7.83358940767931), .Names = c("D1_L2", "D2D1_L2", "D3D2_L2", "A_L2", "B_L2", "C_L2")) pfixed <- NULL # A progress bar can be shown when one core is used system.time( print(Tagloss_L(individuals=data_f_LR, par=par, days.maximum=Tagloss_daymax(data_f_LR), fixed.parameters=pfixed, mc.cores=1, model_after="pR2=pL2;pR1=pL2;pL1=pL2", progressbar = TRUE)) ) # When parallel computing is done, no progress bar can be shown system.time( print(Tagloss_L(individuals=data_f_LR, par=par, days.maximum=Tagloss_daymax(data_f_LR), fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2")) ) # The NLR_00 are very long to calculate data_f_LR_fast <- subset(data_f_LR, subset=(is.na(data_f_LR$NLR_00))) system.time( print(Tagloss_L(individuals=data_f_LR_fast, par=par, days.maximum=Tagloss_daymax(data_f_LR_fast), fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2")) ) o <- Tagloss_fit(data=data_f_LR_fast, fitted.parameters=par, fixed.parameters=pfixed, model_after="pR2=pL2;pR1=pL2;pL1=pL2") par(mar=c(4, 4, 1, 1)) plot(o, t=1:3000, model="2", scale=1000, ylim=c(0, 3), col="red") ## End(Not run)
Usefull to summarize data
Tagloss_LengthObs(data, progressbar = TRUE)Tagloss_LengthObs(data, progressbar = TRUE)
data |
Set of indivuals |
progressbar |
Is shown a progressbar? |
Tagloss_LengthObs returns a list with the number of days for different kinds of individuals are seen.
Return a list with the number of days for different kinds of individuals are seen.
Marc Girondot [email protected]
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") Tagloss_LengthObs(data_f_21) ## End(Not run)## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") Tagloss_LengthObs(data_f_21) ## End(Not run)
This function fits a model of tag loss using a CMR database using Bayesian mcmc.
The parameters must be stored in a data.frame with named rows for each parameter with the following columns:
Density. The density function name, example dnorm, dlnorm, dunif
Prior1. The first parameter to send to the Density function
Prior2. The second parameter to send to the Density function
SDProp. The standard error from new proposition value of this parameter
Min. The minimum value for this parameter
Max. The maximum value for this parameter
Init. The initial value for this parameter
Tagloss_mcmc( data = stop("A database formated using Tagloss_format() must be used"), parameters = stop("Priors must be supplied"), fixed.parameters = NULL, model_before = NULL, model_after = NULL, mc.cores = detectCores(all.tests = FALSE, logical = TRUE), groups = detectCores(all.tests = FALSE, logical = TRUE), n.iter = 10000, n.chains = 1, n.adapt = 100, thin = 30, trace = FALSE, traceML = FALSE, adaptive = FALSE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )Tagloss_mcmc( data = stop("A database formated using Tagloss_format() must be used"), parameters = stop("Priors must be supplied"), fixed.parameters = NULL, model_before = NULL, model_after = NULL, mc.cores = detectCores(all.tests = FALSE, logical = TRUE), groups = detectCores(all.tests = FALSE, logical = TRUE), n.iter = 10000, n.chains = 1, n.adapt = 100, thin = 30, trace = FALSE, traceML = FALSE, adaptive = FALSE, adaptive.lag = 500, adaptive.fun = function(x) { ifelse(x > 0.234, 1.3, 0.7) }, intermediate = NULL, filename = "intermediate.Rdata", previous = NULL )
data |
An object formated using Tagloss_format |
parameters |
A data.frame with priors; see description and examples |
fixed.parameters |
Set of fixed parameters |
model_before |
Transformation of parameters before to use Tagloss_model() |
model_after |
Transformation of parameters after to use Tagloss_model() |
mc.cores |
Number of cores to use for parallel computing |
groups |
Number of groups for parallel computing |
n.iter |
Number of iterations for each chain |
n.chains |
Number of chains |
n.adapt |
Number of iteration to stabilize likelihood |
thin |
Interval for thinning Markov chain |
trace |
Or FALSE or period to show progress |
traceML |
TRUE or FALSE to show ML |
adaptive |
Should an adaptive process for SDProp be used |
adaptive.lag |
Lag to analyze the SDProp value in an adaptive context |
adaptive.fun |
Function used to change the SDProp |
intermediate |
Or NULL of period to save intermediate result |
filename |
Name of file in which intermediate results are saved |
previous |
The content of the file in which intermediate results are saved |
Tagloss_mcmc Bayesian model of tag loss using a CMR database.
Return a list object with the Bayesian model describing tag loss.
Marc Girondot
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc_p(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # model fitted by Rivalan et al. 2005 par <- c(a0_2=-5.43E-2, a1_2=-103.52, a4_2=5.62E-4, delta_1=3.2E-4) pfixed <- c(a2_2=0, a3_2=0, a2_1=0, a3_1=0) model_before <- "par['a0_1']=par['a0_2'];par['a1_1']=par['a1_2'];par['a4_1']=par['a4_2']" o <- Tagloss_fit(data=data_f_21, fitted.parameters=par, fixed.parameters=pfixed, model_before=model_before) pMCMC <- Tagloss_mcmc_p(o, accept=TRUE) o_MCMC <- Tagloss_mcmc(data=data_f_21, parameters=pMCMC, fixed.parameters=pfixed, model_before=model_before, n.iter=10000, n.chains = 1, n.adapt = 100, thin=30) ## End(Not run)## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # model fitted by Rivalan et al. 2005 par <- c(a0_2=-5.43E-2, a1_2=-103.52, a4_2=5.62E-4, delta_1=3.2E-4) pfixed <- c(a2_2=0, a3_2=0, a2_1=0, a3_1=0) model_before <- "par['a0_1']=par['a0_2'];par['a1_1']=par['a1_2'];par['a4_1']=par['a4_2']" o <- Tagloss_fit(data=data_f_21, fitted.parameters=par, fixed.parameters=pfixed, model_before=model_before) pMCMC <- Tagloss_mcmc_p(o, accept=TRUE) o_MCMC <- Tagloss_mcmc(data=data_f_21, parameters=pMCMC, fixed.parameters=pfixed, model_before=model_before, n.iter=10000, n.chains = 1, n.adapt = 100, thin=30) ## End(Not run)
Interactive (or not!) script used to generate set of parameters to be used with Tagloss_mcmc().
Tagloss_mcmc_p( result = stop("An output from Tagloss_fit() must be provided"), default.density = "dunif", accept = FALSE )Tagloss_mcmc_p( result = stop("An output from Tagloss_fit() must be provided"), default.density = "dunif", accept = FALSE )
result |
An object obtained after a Tagloss_fit() fit |
default.density |
The default density, "dnorm" or "dunif' |
accept |
If TRUE, does not wait for use interaction |
Tagloss_mcmc_p generates set of parameters to be used with Tagloss_mcmc()
A matrix with the parameters
Marc Girondot
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_model(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # model fitted by Rivalan et al. 2005 par <- c(a0_2=-5.43E-2, a1_2=-103.52, a4_2=5.62E-4, delta_1=3.2E-4) pfixed <- c(a2_2=0, a3_2=0, a2_1=0, a3_1=0) model_before <- "par['a0_1']=par['a0_2'];par['a1_1']=par['a1_2'];par['a4_1']=par['a4_2']" o <- Tagloss_fit(data=data_f_21, fitted.parameters=par, fixed.parameters=pfixed, model_before=model_before) pMCMC <- Tagloss_mcmc_p(o, accept=TRUE) o_MCMC <- Tagloss_mcmc(data=data_f_21, parameters=pMCMC, fixed.parameters=pfixed, model_before=model_before, n.iter=10000, n.chains = 1, n.adapt = 100, thin=30) ## End(Not run)## Not run: library(phenology) # Example data_f_21 <- Tagloss_format(outLR, model="21") # model fitted by Rivalan et al. 2005 par <- c(a0_2=-5.43E-2, a1_2=-103.52, a4_2=5.62E-4, delta_1=3.2E-4) pfixed <- c(a2_2=0, a3_2=0, a2_1=0, a3_1=0) model_before <- "par['a0_1']=par['a0_2'];par['a1_1']=par['a1_2'];par['a4_1']=par['a4_2']" o <- Tagloss_fit(data=data_f_21, fitted.parameters=par, fixed.parameters=pfixed, model_before=model_before) pMCMC <- Tagloss_mcmc_p(o, accept=TRUE) o_MCMC <- Tagloss_mcmc(data=data_f_21, parameters=pMCMC, fixed.parameters=pfixed, model_before=model_before, n.iter=10000, n.chains = 1, n.adapt = 100, thin=30) ## End(Not run)
This function compute a model of daily tag loss rate for days t
based on a set of parameters, par or a fitted tag loss model in x.
Parameters are described in Tagloss_fit.
Tagloss_model( t = NULL, par = NULL, Hessian = NULL, mcmc = NULL, model_before = NULL, model_after = NULL, model = stop("You must specify which tag loss rate you want."), method = NULL, replicates = NULL, x = NULL )Tagloss_model( t = NULL, par = NULL, Hessian = NULL, mcmc = NULL, model_before = NULL, model_after = NULL, model = stop("You must specify which tag loss rate you want."), method = NULL, replicates = NULL, x = NULL )
t |
Time for which values of model must be estimated |
par |
Parameters |
Hessian |
Hessian matrix of parameters |
mcmc |
A mcmc result |
model_before |
Function to be used before estimation of daily tagloss rate |
model_after |
Function to be used after estimation of daily tagloss rate |
model |
The model of parameter to be used, can be 1, 2, L1, L2, R1 or R2 |
method |
Can be NULL, "delta", "SE", "Hessian", "MCMC", or "PseudoHessianFromMCMC" |
replicates |
Number of replicates to estimate se of output |
x |
A Tagloss fitted model |
Tagloss_model returns the daily rate of tag loss.
Return the daily rate of tag loss if hessian is null or a data.frame with distribution of daily rate of tag loss if hessian is not null.
Marc Girondot [email protected]
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_simulate(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
## Not run: library(phenology) # Example t <- 1:1000 par <- c(D1=200, D2D1=100, D3D2=200, A=-logit(0.02), B=-logit(0.05), C=-logit(0.07)) y <- Tagloss_model(t, par, model="1") plot(x=t, y, type="l") par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, A_1=-logit(0.02), B_1=-logit(0.05), C_1=-logit(0.07)) y <- Tagloss_model(t, par, model="1") phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="1") # Fig1A in Rivalan et al. 2005 (note an error for a0; a0 must be negative) par <- c(a0=-1E5, a1=-2000, a2=0, a3=2*max(t), a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1B in Rivalan et al. 2005 par <- c(a0=-0.5, a1=-2000, a2=-0.001, a3=0, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1C in Rivalan et al. 2005 par <- c(a0=-1, a1=-6, a2=0, a3=0, a4=0) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1D in Rivalan et al. 2005 par <- c(a0=-1, a1=-6, a2=0, a3=0, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1E in Rivalan et al. 2005 par <- c(a0=-0.1, a1=-10, a2=-0.2, a3=60, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1F in Rivalan et al. 2005 par <- c(a0=-0.1, a1=-10, a2=0.2, a3=60, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Example with fitted data data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- c('D1_2' = 49.086835072129126, 'D2D1_2' = 1065.0992647723231, 'D3D2_2' = 6.15531475922079, 'A_2' = 5.2179675647973758, 'B_2' = 8.0045560376751386, 'C_2' = 8.4082505219581876, 'D1_1' = 177.23337287498103, 'D2D1_1' = 615.42690323741033, 'D3D2_1' = 2829.0806609455867, 'A_1' = 28.500118091731551, 'B_1' = 10.175426055942701, 'C_1' = 6.9616630417169398) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par) t <- 1:10 y <- Tagloss_model(t, o$par, model="1") y <- Tagloss_model(t, x=o, method=NULL, model="1") y <- Tagloss_model(t, x=o, method="Hessian", model="1", replicates=1000) ## End(Not run)## Not run: library(phenology) # Example t <- 1:1000 par <- c(D1=200, D2D1=100, D3D2=200, A=-logit(0.02), B=-logit(0.05), C=-logit(0.07)) y <- Tagloss_model(t, par, model="1") plot(x=t, y, type="l") par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, A_1=-logit(0.02), B_1=-logit(0.05), C_1=-logit(0.07)) y <- Tagloss_model(t, par, model="1") phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="1") # Fig1A in Rivalan et al. 2005 (note an error for a0; a0 must be negative) par <- c(a0=-1E5, a1=-2000, a2=0, a3=2*max(t), a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1B in Rivalan et al. 2005 par <- c(a0=-0.5, a1=-2000, a2=-0.001, a3=0, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1C in Rivalan et al. 2005 par <- c(a0=-1, a1=-6, a2=0, a3=0, a4=0) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1D in Rivalan et al. 2005 par <- c(a0=-1, a1=-6, a2=0, a3=0, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1E in Rivalan et al. 2005 par <- c(a0=-0.1, a1=-10, a2=-0.2, a3=60, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Fig1F in Rivalan et al. 2005 par <- c(a0=-0.1, a1=-10, a2=0.2, a3=60, a4=0.1) y <- Tagloss_model(t, par) plot(x=t, y, type="l") # Example with fitted data data_f_21 <- Tagloss_format(outLR, model="21") # Without the N20 the computing is much faster data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20))) par <- c('D1_2' = 49.086835072129126, 'D2D1_2' = 1065.0992647723231, 'D3D2_2' = 6.15531475922079, 'A_2' = 5.2179675647973758, 'B_2' = 8.0045560376751386, 'C_2' = 8.4082505219581876, 'D1_1' = 177.23337287498103, 'D2D1_1' = 615.42690323741033, 'D3D2_1' = 2829.0806609455867, 'A_1' = 28.500118091731551, 'B_1' = 10.175426055942701, 'C_1' = 6.9616630417169398) o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par) t <- 1:10 y <- Tagloss_model(t, o$par, model="1") y <- Tagloss_model(t, x=o, method=NULL, model="1") y <- Tagloss_model(t, x=o, method="Hessian", model="1", replicates=1000) ## End(Not run)
Generate data with known features.
model_before is applied to par parameter.
model_after is applied after par is separated in p1, p2, pL1, pL2, pR1 and pR2 parameters.
pobservation can be a vector of daily probabilities to be captured. The last value is repeated
if necessary.
The maximum number of days of observation is exp(LengthObservation["max"]).
If model="12" then par must have _1 and _2 parameters.
if model="LR" then par must have _L2, _L1, _R2, R1 parameters.
Tagloss_simulate( n = 500, par, pobservation = c(rep(0.05, 70), 0.01), LengthObservation = c(min = 0, max = 9), dailysurvival = 0.999, model = "12", model_before = NULL, model_after = NULL, progressbar = TRUE )Tagloss_simulate( n = 500, par, pobservation = c(rep(0.05, 70), 0.01), LengthObservation = c(min = 0, max = 9), dailysurvival = 0.999, model = "12", model_before = NULL, model_after = NULL, progressbar = TRUE )
n |
Number of individuals to simulate |
par |
Set of parameters |
pobservation |
Probability of daily observation |
LengthObservation |
The log of number of days of observations is a random number between min and max |
dailysurvival |
Daily probability of survival |
model |
Must be "12" or "LR" |
model_before |
Transformation of parameters before to use Tagloss_model() |
model_after |
Transformation of parameters after to use Tagloss_model() |
progressbar |
Is a progressbar should be shown? |
Tagloss_simulate returns a list with the number of days different kinds of individuals are seen.
Return a list with the number of days different kinds of individuals are seen.
Marc Girondot [email protected]
Other Model of Tag-loss:
Tagloss_L(),
Tagloss_LengthObs(),
Tagloss_cumul(),
Tagloss_daymax(),
Tagloss_fit(),
Tagloss_format(),
Tagloss_mcmc(),
Tagloss_mcmc_p(),
Tagloss_model(),
logLik.Tagloss(),
o_4p_p1p2,
plot.Tagloss(),
plot.TaglossData()
library(phenology) ## Not run: # Example par <- structure(c(49.5658922243074, 808.136085362158, 106.283783786853, 5.22150592456511, 8.00608716525864, 8.32718202233396, 150.612916258503, 715.865805125223, 2242.06574225966, 119.212383120678, 10.1860735529433, 7.14231725937626), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")) cmr <- Tagloss_simulate(n=500, par=par, model="12") cmr_f <- Tagloss_format(cmr, model="12") ## End(Not run)library(phenology) ## Not run: # Example par <- structure(c(49.5658922243074, 808.136085362158, 106.283783786853, 5.22150592456511, 8.00608716525864, 8.32718202233396, 150.612916258503, 715.865805125223, 2242.06574225966, 119.212383120678, 10.1860735529433, 7.14231725937626), .Names = c("D1_2", "D2D1_2", "D3D2_2", "A_2", "B_2", "C_2", "D1_1", "D2D1_1", "D3D2_1", "A_1", "B_1", "C_1")) cmr <- Tagloss_simulate(n=500, par=par, model="12") cmr_f <- Tagloss_format(cmr, model="12") ## End(Not run)
This function is used to transform a set of parameters that uses Min, MinB or MinE to a set of parameters that uses PMin, PminB or PminE, or reverse.
toggle_Min_PMin(parameters = stop("A set of parameters must be indicated"))toggle_Min_PMin(parameters = stop("A set of parameters must be indicated"))
parameters |
Set of current parameters |
toggle_Min_PMin transforms a set of parameters from Min, MinB or MinE to PMin, PminB or PminE, or reverse
Return a set of modified parameters
Marc Girondot [email protected]
# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot) # Change the parameters to PMinB and PMinE parg1<-toggle_Min_PMin(parameters=parg) # And change back to MinB and MinE parg2<-toggle_Min_PMin(parameters=parg1)# Read a file with data data(Gratiot) # Generate a formatted list nammed data_Gratiot refdate <- as.Date("2001-01-01") data_Gratiot<-add_phenology(Gratiot, name="Complete", reference=refdate, format="%d/%m/%Y") # Generate initial points for the optimisation parg<-par_init(data_Gratiot) # Change the parameters to PMinB and PMinE parg1<-toggle_Min_PMin(parameters=parg) # And change back to MinB and MinE parg2<-toggle_Min_PMin(parameters=parg1)