Tagloss_mcmc {phenology}

Bayesian model of tag loss using a CMR database.

Description

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

Usage

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
)

Arguments

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

Details

Tagloss_mcmc Bayesian model of tag loss using a CMR database.

Value

Return a list object with the Bayesian model describing tag loss.

Author(s)

Marc Girondot

See Also

Other Model of Tag-loss: Tagloss_LengthObs(), Tagloss_L(), Tagloss_cumul(), Tagloss_daymax(), Tagloss_fit(), Tagloss_format(), Tagloss_mcmc_p(), Tagloss_model(), Tagloss_simulate(), logLik.Tagloss(), o_4p_p1p2, plot.TaglossData(), plot.Tagloss()

Examples

## 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)