morse: reproFitTT – R documentation

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reproFitTT

Fits a Bayesian concentration-effect model for target-time reproduction analysis

Description

This function estimates the parameters of a concentration-effect model for target-time reproduction analysis using Bayesian inference. In this model the endpoint is the cumulated number of reproduction outputs over time, with potential mortality all along the experiment.

Usage

reproFitTT(
  data,
  stoc.part = "bestfit",
  target.time = NULL,
  ecx = c(5, 10, 20, 50),
  n.chains = 3,
  quiet = FALSE
)

Arguments

`data`	an object of class `reproData`
`stoc.part`	stochastic part of the model. Possible values are `"bestfit"`, `"poisson"` and `"gammapoisson"`
`target.time`	defines the target time point at which to analyse the repro data. By default the last time point
`ecx`	desired values of x (in percent) for which to compute ECx
`n.chains`	number of MCMC chains. The minimum required number of chains is 2
`quiet`	if `TRUE`, does not print messages and progress bars from JAGS

Details

Because some individuals may die during the observation period, the reproduction rate alone is not sufficient to account for the observed number of offspring at a given time point. In addition, we need the time individuals have stayed alive during this observation period. The reproFitTT function estimates the number of individual-days in an experiment between its start and the target time. This covariable is then used to estimate a relation between the chemical compound concentration and the reproduction rate per individual-day.

The reproFitTT function fits two models, one where inter-individual variability is neglected ("Poisson" model) and one where it is taken into account ("gamma-Poisson" model). When setting stoc.part to "bestfit", a model comparison procedure is used to choose between both. More details are presented in the vignette accompanying the package.

Value

The function returns an object of class reproFitTT which is a list of the following objects:

`DIC`	DIC value of the selected model
`estim.ECx`	a table of the estimated 5, 10, 20 and 50 % effective concentrations (by default) and their 95 % credible intervals
`estim.par`	a table of the estimated parameters as medians and 95 % credible intervals
`mcmc`	an object of class `mcmc.list` with the posterior distribution
`model`	a JAGS model object
`warnings`	a data.frame with warning messages
`model.label`	a character string, `"P"` if the Poisson model is used, `"GP"` if the gamma-Poisson is used
`parameters`	a list of the parameter names used in the model
`n.chains`	an integer value corresponding to the number of chains used for the MCMC computation
`n.iter`	a list of two indices indicating the beginning and the end of monitored iterations
`n.thin`	a numerical value corresponding to the thinning interval
`jags.data`	a list of the data passed to the jags model
`transformed.data`	the `survData` object passed to the function
`dataTT`	the dataset with which the parameters are estimated

Examples

# (1) Load the data
data(cadmium1)

# (2) Create an object of class "reproData"
dataset <- reproData(cadmium1)

## Not run: 
# (3) Run the reproFitTT function with the log-logistic gamma-Poisson model
out <- reproFitTT(dataset, stoc.part = "gammapoisson",
                  ecx = c(5, 10, 15, 20, 30, 50, 80), quiet = TRUE)

## End(Not run)

morse

Modelling Tools for Reproduction and Survival Data in Ecotoxicology

v3.3.1

GPL (>= 2)

Authors

Virgile Baudrot [aut], Sandrine Charles [aut], Marie Laure Delignette-Muller [aut], Wandrille Duchemin [ctb], Benoit Goussen [ctb], Nils Kehrein [ctb], Guillaume Kon-Kam-King [ctb], Christelle Lopes [ctb], Philippe Ruiz [aut], Alexander Singer [ctb], Philippe Veber [aut]

Initial release