This function estimates the parameters of an concentration-response model for target-time survival analysis using Bayesian inference. In this model, the survival rate of 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.
# S3 method for survDataCstExp
survFitTT(
data,
target.time = NULL,
lcx = c(5, 10, 20, 50),
n.chains = 3,
quiet = FALSE,
...
)The function returns an object of class survFitTT, which is a
list with the following information:
a table of the estimated \(LC_x\) along with their 95% credible intervals
a table of the estimated parameters (medians) and 95% credible intervals
the name of the deterministic part of the used model
an object of class mcmc.list with the posterior
distribution
a table with warning messages
a JAGS model object
a list of 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 survData
the chosen endpoint to evaluate the effect of the chemical compound concentration, by default the last time point available for all concentrations
desired values of \(x\) (in percent) for which to compute \(LC_x\).
number of MCMC chains, the minimum required number of chains is 2
if TRUE, does not print messages and progress bars from
JAGS
Further arguments to be passed to generic methods
The function returns parameter estimates of the concentration-response model and estimates of the so-called \(LC_x\), that is the concentration of chemical compound required to get an \((1 - x/100)\) survival rate.