morse: predict_ode.survFit – R documentation

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predict_ode.survFit

Predict method for survFit objects

Description

This is the generic predict S3 method for the survFit class. It provides predicted survival rate for "SD" or "IT" models under constant or time-variable exposure.

Usage

## S3 method for class 'survFit'
predict_ode(
  object,
  data_predict = NULL,
  spaghetti = FALSE,
  mcmc_size = 1000,
  hb_value = TRUE,
  interpolate_length = 100,
  interpolate_method = "linear",
  hb_valueFORCED = NA,
  ...
)

Arguments

`object`	An object of class `survFit`.
`data_predict`	A dataframe with three columns `time`, `conc` and `replicate` used for prediction. If `NULL`, prediction is based on `x` object of class `survFit` used for fitting.
`spaghetti`	If `TRUE`, return a set of survival curves using parameters drawn from the posterior distribution.
`mcmc_size`	Can be used to reduce the number of mcmc samples in order to speed up the computation. `mcmc_size` is the number of selected iterations for one chain. Default is 1000. If all MCMC is wanted, set argument to `NULL`.
`hb_value`	If `TRUE`, the background mortality `hb` is taken into account from the posterior. If `FALSE`, parameter `hb` is set to a fixed value. The default is `TRUE`.
`interpolate_length`	Length of the time sequence for which output is wanted.
`interpolate_method`	The interpolation method for concentration. See package `deSolve` for details. Default is `linear`.
`hb_valueFORCED`	If `hb_value` is `FALSE`, it fix `hb`.
`...`	Further arguments to be passed to generic methods

Examples

# (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
out <- survFit(dataset , model_type = "SD")

# (4) Create a new data table for prediction
data_4prediction <- data.frame(time = 1:10,
                               conc = c(0,5,30,30,0,0,5,30,15,0),
                               replicate= rep("predict", 10))

# (5) Predict on a new data set
predict_out <- predict_ode(object = out, data_predict = data_4prediction,
                           mcmc_size = 1000, 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