FitTT: Fits a Bayesian concentration-response model for target-time
In morseDR: Bayesian Inference of Binary, Count and Continuous Data in Toxicology

FitTT

R Documentation

Fits a Bayesian concentration-response model for target-time

Description

binary data: This function estimates the parameters of a concentration-response model for target-time binary data analysis using Bayesian inference. In this model, the rate of binary effect (survival or immobility) individuals at a given time point (called target time) is modeled as a function of the chemical compound concentration. The actual number of surviving individuals is then modeled as a stochastic function of the survival rate. Details of the model are presented in the vignette accompanying the package.
count data: 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 event (like reproduction) over time, with potential failure (death) all along the experiment. Particularly dedicated to reproduction data, 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 fit 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 fit function on CountData 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.
continuous data: This function estimates the parameters of a concentration-response model for target-time of any continuous data analysis using Bayesian inference. This model is particularly well-suited for growth data. Details of the model are presented in the vignette accompanying the package. We can choose the stochastic part to be either "gamma" or "normal", with a default to "gamma".

Usage

## S3 method for class 'BinaryData'
fit(
  data,
  target.time = NULL,
  inits = NULL,
  n.chains = 3,
  n.adapt = 3000,
  quiet = FALSE,
  warning.print = TRUE,
  n.iter = NA,
  ...
)

## S3 method for class 'ContinuousData'
fit(
  data,
  stoc.part = "gamma",
  target.time = NULL,
  inits = NULL,
  n.chains = 3,
  n.adapt = 3000,
  quiet = FALSE,
  warning.print = TRUE,
  n.iter = NA,
  low.asympt = FALSE,
  ...
)

## S3 method for class 'CountData'
fit(
  data,
  stoc.part = "bestfit",
  target.time = NULL,
  inits = NULL,
  n.chains = 3,
  n.adapt = 3000,
  quiet = FALSE,
  warning.print = TRUE,
  n.iter = NA,
  ...
)

fit(data, ...)

Arguments

`data`	an object of class `BinaryData`, `CountData` or `CountinuousData`
`target.time`	the chosen endpoint to evaluate the effect of the chemical compound concentration, by default the last time point available for all concentrations
`inits`	See jags.model. Optional specification of initial values.
`n.chains`	number of MCMC chains, the minimum required number of chains is 2
`n.adapt`	The number of iterations for adaptation. See jags.model for further details.
`quiet`	if `TRUE`, does not print messages and progress bars from JAGS
`warning.print`	if `TRUE`, print the warnings in REPL
`n.iter`	if `NA`, default, the number of iteration is estimated from `raftery.diag` process, otherwise, set the n.iter provided.
`...`	Further arguments to be passed to generic methods
`stoc.part`	a string for stochastic part. For "" model, the `stoc.part` is `"gamma"` (default) but can be `"normal"`.
`low.asympt`	binary TRUE/FALSE. If TRUE, a parameter for the lower side of the assymptote is compute in case of Continuous Data. Default is FALSE.