fitBeeGUTS {BeeGUTS}  R Documentation 
The function fitBeeGUTS
estimates the parameters of a GUTS model
for the stochastic death (SD) or individual tolerance (IT) death mechanisms for
survival analysis using Bayesian inference.
fitBeeGUTS(
data,
modelType = NULL,
distribution = "loglogistic",
priorsList = NULL,
parallel = TRUE,
nCores = parallel::detectCores()  1L,
nChains = 3,
nIter = 2000,
nWarmup = floor(nIter/2),
thin = 1,
adaptDelta = 0.95,
odeIntegrator = "rk45",
relTol = 1e08,
absTol = 1e08,
maxSteps = 1000,
...
)
data 
An object of class 
modelType 
A model type between 
distribution 
A distribution for the IT death mechanism. To be chosen between

priorsList 
A list containing the prior distribution for the parameter considered.
By default, when no priors are provided (default is 
parallel 
Logical indicating whether parallel computing should be used or not. Default is 
nCores 
A positive integer specifying the number of cores to use. Default is one core less than maximum number of cores available 
nChains 
A positive integer specifying the number of MCMC chains to run. Default is 3. 
nIter 
A positive integer specifying the number of iteration to monitor for each MCMC chain. Default is 2000 
nWarmup 
A positive integer specifying the number of warmup iteration per chain. Default is half the number of iteration 
thin 
A positive integer specifying the interval between the iterations to monitor. Default is 1 (all iterations are monitored) 
adaptDelta 
A double, bounded between 0 and 1 and controlling part of the sampling algorithms.
See the 
odeIntegrator 
A string specifying the integrator used to solve the system of
differential equations (ODE) in the 
relTol 
A double, bounded between 0 and 1 and controlling the relative tolerance of the accuracy of the solutions generated by the integrator. A smaller tolerance produces more accurate solution at the expanse of the computing time. Default is 1e8 
absTol 
A double, bounded between 0 and 1 and controlling the absolute tolerance of the accuracy of the solutions generated by the integrator. A smaller tolerance produces more accurate solution at the expanse of the computing time. Default is 1e8 
maxSteps 
A double controlling the maximum number of steps that can be taken before stopping a runaway simulation. Default is 1000 
... 
Additional parameters to be passed to 
The automated prior determination is modified from DelignetteMuller et al. by considering that the minimal concentration for the prior can be close to 0 (1e6) whereas the original paper considered the lowest nonzero concentration. Similarly, the minimal kd considered for the prior calculation was reduced to allow more chance to capture slow kinetics.
The function fitBeeGUTS
returns the parameter estimates
of the General Unified Threshold model of Survival (GUTS) in an object
of class beeSurvFit
. This object is a list composed of the following:
stanFit 
An object of S4 class 
data 
The data object provided as argument of the function 
dataFit 
A list of data passed to the Stan model object 
setupMCMC 
A list containing the setup used for the MCMC chains 
modelType 
A character vector specifying the type of GUTS model used between

distribution 
A character vector specifying the type of distribution used in case 
messages 
A character vector containing warning messages 
DelignetteMuller, M.L., Ruiz P. and Veber P. (2017). Robust fit of toxicokinetictoxicodynamic models using prior knowledge contained in the design of survival toxicity tests. doi:10.1021/acs.est.6b05326
data(betacyfluthrinChronic)
fit < fitBeeGUTS(betacyfluthrinChronic, modelType = "SD", nIter = 1000, nCores = 2)