Tagloss_mcmc: Bayesian model of tag loss using a CMR database.

In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

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)

phenology documentation built on Oct. 16, 2023, 9:06 a.m.