Fits a TKTD for survival analysis using Bayesian inference for survDataTKTD object
This function estimates the parameters of a TKTD model 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.
survFitTKTD(data, n.chains = 3, quiet = FALSE)
data |
An object of class |
n.chains |
Number of MCMC chains. The minimum required number of chains is 2. |
quiet |
If |
The function returns an object of class survFitTKTD
, which is
a list with the following information:
estim.par |
a table of the estimated parameters as medians and 95% credible intervals |
mcmc |
an object of class |
warnings |
a table with warning messages |
model |
a JAGS model object |
parameters |
a list of 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 data passed to the JAGS model |
Delignette-Muller ML, Ruiz P and Veber P (2017). Robust fit of toxicokinetic-toxicodynamic models using prior knowledge contained in the design of survival toxicity tests.
Bedaux, J., Kooijman, SALM (1994) Statistical analysis of toxicity tests, based on hazard modeling, Environmental and Ecological Statistics, 1, 303-314.
# (1) Load the survival data data(propiconazole) # (2) Create an object of class "survData" dataset <- survData(propiconazole) ## Not run: # (3) Run the survFitTKTD function out <- survFitTKTD(dataset) # (4) Summarize look the estimated parameters summary(out) # (5) Plot the fitted curve plot(out, adddata = TRUE) # (6) Plot the fitted curve with ggplot style and CI as spaghetti plot(out, spaghetti = TRUE , adddata = TRUE, style = "ggplot") ## End(Not run)
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