PredictSurvFit: Prediction base on `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. prediction on constant exposure profile

Note: On constant exposure profiles, the results is explicit (exact), so you don't have to profile

Usage

predict_SurvFitCstExp(
  fit,
  display.exposure = NULL,
  hb_value = NULL,
  interpolate_length = NULL,
  ...
)
predict_cstSD(
  display.exposure = NULL,
  display.parameters = NULL,
  hb_value = NULL,
  interpolate_length = NULL
)
predict_cstIT(
  display.exposure = NULL,
  display.parameters = NULL,
  hb_value = NULL,
  interpolate_length = NULL
)
predict_SurvFitVarExp(
  fit,
  display.exposure = NULL,
  hb_value = NULL,
  interpolate_length = NULL,
  interpolate_method = "linear",
  ...
)
predict_varSD(
  display.exposure = NULL,
  display.parameters = NULL,
  hb_value = NULL,
  interpolate_length = NULL,
  interpolate_method = NULL
)
predict_varIT(
  display.exposure = NULL,
  display.parameters = NULL,
  hb_value = NULL,
  interpolate_length = NULL,
  interpolate_method = NULL
)
predict(fit, ...)
# S3 method for SurvFit
predict(
  fit,
  display.exposure = NULL,
  hb_value = NULL,
  interpolate_length = NULL,
  interpolate_method = "linear",
  ...
)

Value

a list of data.frame with the quantiles of outputs in df_quantiles or all the MCMC chains df_spaghetti

Arguments

fit: an object of class SurvFit
display.exposure: concentration points on which prediction is done
hb_value: a numeric used as hb_value (can be set to 0 to remove background mortality and take only effect parameters).
interpolate_length: if display.time is NULL, the argument interpolate_length can be used to provide a sequence from 0 to maximum of the time of exposure in original dataset (used for fitting).
...: Further arguments to be passed to generic methods
display.parameters: parameters of the specific model.
interpolate_method: The interpolation method for concentration. See package deSolve for details. Default is linear.