This function estimates the parameters of a TKTD model ('SD' or 'IT') for survival analysis using Bayesian inference. In this model, the survival rate of individuals is modeled as a function of the chemical compound concentration with a mechanistic description of the effects on survival over time.
survFit(
data,
model_type,
quiet,
n.chains,
n.adapt,
n.iter,
n.warmup,
thin.interval,
limit.sampling,
dic.compute,
dic.type,
hb_value,
hb_valueFIXED,
...
)# S3 method for survDataCstExp
survFit(
data,
model_type = NULL,
quiet = FALSE,
n.chains = 3,
n.adapt = 3000,
n.iter = NULL,
n.warmup = NULL,
thin.interval = NULL,
limit.sampling = TRUE,
dic.compute = FALSE,
dic.type = "pD",
hb_value = TRUE,
hb_valueFIXED = NA,
...
)
# S3 method for survDataVarExp
survFit(
data,
model_type = NULL,
quiet = FALSE,
n.chains = 3,
n.adapt = 1000,
n.iter = NULL,
n.warmup = NULL,
thin.interval = NULL,
limit.sampling = TRUE,
dic.compute = FALSE,
dic.type = "pD",
hb_value = TRUE,
hb_valueFIXED = NA,
extend_time = 100,
...
)
an object of class survFit
The function returns an object of class survFitCstExp, which is
a list with the following information:
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
return the Deviance Information Criterion (DIC) if dic.compute is TRUE
a table with warning messages
a list of parameter names used in the model
an integer value corresponding to the number of chains used for the MCMC computation
a table with the number of iterations, chains, adaptation, warmup and the thinning interval.
a list of the data passed to the JAGS model
the type of TKTD model used: SD or IT
The function returns an object of class survFitVarExp, which is
a list with the following information:
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
return the Deviance Information Criterion (DIC) if dic.compute is TRUE
a table with warning messages
a list of parameter names used in the model
an integer value corresponding to the number of chains used for the MCMC computation
a table with the number of iterations, chains, adaptation, warmup and the thinning interval
a list of the data passed to the JAGS model
the type of TKTD model used: SD or IT
An object of class survDataCstExp or survDataVarExp.
Can be "SD" or "IT" to choose
between "Stochastic Death" or "Individual Tolerance" models
(resp.). See the modeling vignette for details.
If FALSE, prints logs and progress bar from
JAGS.
A positive integer specifying the number of MCMC chains. The minimum required number of chains is 2.
A positive integer specifying the number of iterations for adaptation. If n.adapt = 0
then no adaptation takes place.
A positive integer specifying the number of iterations to monitor for each chain.
A positive integer specifying the number of warmup (aka burnin) iterations per chain.
A positive integer specifying the period to monitor.
if FALSE (default is TRUE), there is no limit to the number of iterations
in MCMC imposed by the raftery.diag test.
if TRUE (default is FALSE), it generates penalized deviance samples to compute
the Deviance Information Criterion (DIC) with the rjags package
type of penalty to use. A string identifying the type of penalty: pD or popt
(see function dic.samples)
If TRUE, the background mortality hb is taken into account.
If FALSE, parameter hb is set to 0. The default is TRUE.
If hb_value is FALSE, then hb_valueFiXED is the value to fix hb.
If hb_value is FALSE and hb_valueFiXED is NA, then hb is fixed to 0.
Further arguments to be passed to generic methods
Number of for each replicate used for linear interpolation (comprise between time to compute and fitting accuracy)
The function survFit returns the parameter estimates of Toxicokinetic-toxicodynamic (TKTD) models
SD for 'Stochastic Death' or IT fo 'Individual Tolerance'.
TKTD models, and particularly the General Unified Threshold model of
Survival (GUTS), provide a consistent process-based
framework to analyse both time and concentration dependent datasets.
In GUTS-SD, all organisms are assumed to have the same internal concentration
threshold (denoted \(z\)), and, once exceeded, the instantaneous probability
to die increases linearly with the internal concentration.
In GUTS-IT, the threshold concentration is distributed among all the organisms, and once
exceeded in one individual, this individual dies immediately.
When class of object is survDataCstExp, see survFit.survDataCstExp ;
and for a survDataVarExp, see survFit.survDataVarExp.
Jager, T., Albert, C., Preuss, T. G. and Ashauer, R. (2011) General unified threshold model of survival-a toxicokinetic-toxicodynamic framework for ecotoxicology, Environmental Science and Technology, 45, 2529-2540. 303-314.