morse: survFit – R documentation

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survFit

Fits a TKTD model for survival analysis using Bayesian inference

Description

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.

This function estimates the parameters of a TKTD ('SD' or 'IT') 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.

Usage

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 class '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 class '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,
  ...
)

Arguments

`data`	An object of class `survDataVarExp`.
`model_type`	can be `"SD"` or `"IT"` to choose between "Stochastic Death" or "Individual Tolerance" models (resp.). See the modeling vignette for details.
`quiet`	If `FALSE`, prints logs and progress bar from JAGS.
`n.chains`	A positive integer specifying the number of MCMC chains. The minimum required number of chains is 2.
`n.adapt`	A positive integer specifying the number of iterations for adaptation. If `n.adapt` = 0 then no adaptation takes place.
`n.iter`	A positive integer specifying the number of iterations to monitor for each chain.
`n.warmup`	A positive integer specifying the number of warmup (aka burnin) iterations per chain.
`thin.interval`	A positive integer specifying the period to monitor.
`limit.sampling`	if `FALSE` (default is `TRUE`), there is no limit to the number of iterations in MCMC imposed by the `raftery.diag` test.
`dic.compute`	if `TRUE` (default is `FALSE`), it generates penalized deviance samples to compute the Deviance Information Criterion (DIC) with the `rjags` package
`dic.type`	type of penalty to use. A string identifying the type of penalty: `pD` or `popt` (see function `dic.samples`)
`hb_value`	If `TRUE`, the background mortality `hb` is taken into account. If `FALSE`, parameter `hb` is set to 0. The default is `TRUE`.
`hb_valueFIXED`	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
`extend_time`	Number of for each replicate used for linear interpolation (comprise between time to compute and fitting accuracy)

Details

The function survFit return 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.

Value

The function returns an object of class survFitCstExp, 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 `mcmc.list` with the posterior distribution
`model`	a JAGS model object
`dic`	return the Deviance Information Criterion (DIC) if `dic.compute` is `TRUE`
`warnings`	a table with warning messages
`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
`mcmcInfo`	a table with the number of iterations, chains, adaptation, warmup and the thinning interval.
`jags.data`	a list of the data passed to the JAGS model
`model_type`	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:

`estim.par`	a table of the estimated parameters as medians and 95% credible intervals
`mcmc`	an object of class `mcmc.list` with the posterior distribution
`model`	a JAGS model object
`dic`	return the Deviance Information Criterion (DIC) if `dic.compute` is `TRUE`
`warnings`	a table with warning messages
`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
`mcmcInfo`	a table with the number of iterations, chains, adaptation, warmup and the thinning interval
`jags.data`	a list of the data passed to the JAGS model
`model_type`	the type of TKTD model used: `SD` or `IT`

References

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.

Examples

# (1) Load the survival data
data(propiconazole)

# (2) Create an object of class "survData"
dataset  <- survData(propiconazole)

## Not run: 
# (3) Run the survFit function with TKTD model 'SD' or 'IT' 
out <- survFit(dataset , model_type = "SD")

# (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)

## End(Not run)

# When the data set include variable exposure profile, time for inference is longer

# (1) Load the survival data with variable exposure profile
data("propiconazole_pulse_exposure")

# (2) Create an object of class "survData"
dataset <- survData(propiconazole_pulse_exposure)

## Not run: 
# (3) Run the survFit function with TKTD model 'SD' or 'IT' 
out <- survFit(dataset , model_type = "SD")

# (4) Summarize look the estimated parameters
summary(out)

# (5) Plot the fitted curve
plot(out, adddata = FALSE)

# (6) Plot the fitted curve with ggplot style and CI as spaghetti
plot(out, spaghetti = TRUE)

## End(Not run)



# (1) Load the survival data
data(propiconazole)

# (2) Create an object of class "survData"
dataset  <- survData(propiconazole)

## Not run: 
# (3) Run the survFit function with TKTD model 'SD' or 'IT' 
out <- survFit(dataset , model_type = "SD")

# (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)

## End(Not run)


# (1) Load the survival data
data("propiconazole_pulse_exposure")

# (2) Create an object of class "survData"
dataset <- survData(propiconazole_pulse_exposure)

## Not run: 
# (3) Run the survFit function with TKTD model 'SD' or 'IT' 
out <- survFit(dataset , model_type = "SD")

# (4) Summarize look the estimated parameters
summary(out)

# (5) Plot the fitted curve
plot(out, adddata = FALSE)

# (6) Plot the fitted curve with ggplot style and CI as spaghetti
plot(out, spaghetti = TRUE)

## End(Not run)

morse

Modelling Tools for Reproduction and Survival Data in Ecotoxicology

v3.3.1

GPL (>= 2)

Authors

Virgile Baudrot [aut], Sandrine Charles [aut], Marie Laure Delignette-Muller [aut], Wandrille Duchemin [ctb], Benoit Goussen [ctb], Nils Kehrein [ctb], Guillaume Kon-Kam-King [ctb], Christelle Lopes [ctb], Philippe Ruiz [aut], Alexander Singer [ctb], Philippe Veber [aut]

Initial release