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.
reproFitTT(
data,
stoc.part = "bestfit",
target.time = NULL,
ecx = c(5, 10, 20, 50),
n.chains = 3,
quiet = FALSE
)The function returns an object of class reproFitTT which is a list
of the following objects:
DIC value of the selected model
a table of the estimated 5, 10, 20 and 50 % effective concentrations (by default) and their 95 % credible intervals
a table of the estimated parameters as medians and 95 % credible intervals
an object of class mcmc.list with the posterior distribution
a JAGS model object
a data.frame with warning messages
a character string, "P" if the Poisson model is used,
"GP" if the gamma-Poisson is used
a list of the parameter names used in the model
an integer value corresponding to the number of chains used for the MCMC computation
a list of two indices indicating the beginning and the end of monitored iterations
a numerical value corresponding to the thinning interval
a list of the data passed to the jags model
the survData object passed to the function
the dataset with which the parameters are estimated
an object of class reproData
stochastic part of the model. Possible values are "bestfit",
"poisson" and "gammapoisson"
defines the target time point at which to analyse the repro data. By default the last time point
desired values of \(x\) (in percent) for which to compute \(EC_{x}\)
number of MCMC chains. The minimum required number of chains is 2
if TRUE, does not print messages and progress bars from JAGS
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.