predict: Predict method for 'survFit' objects

In morse: Modelling Reproduction and Survival Data in Ecotoxicology

Predict method for survFit objects

Description

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

It provides the simulated number of survivors for "SD" or "IT" models under constant or time-variable exposure.

It provides the simulated number of survivors for "SD" or "IT" models under constant or time-variable exposure.

This is a method to replace function predict_Nsurv used on survFit object when computing issues happen. predict_nsurv_ode uses the deSolve library to improve robustness. However, time to compute may be longer.

Usage

## S3 method for class 'survFit'
predict(
  object,
  data_predict = NULL,
  spaghetti = FALSE,
  mcmc_size = NULL,
  hb_value = TRUE,
  ratio_no.NA = 0.95,
  hb_valueFORCED = NA,
  extend_time = 100,
  ...
)

predict_Nsurv(object, ...)

## S3 method for class 'survFit'
predict_Nsurv(
  object,
  data_predict = NULL,
  spaghetti = FALSE,
  mcmc_size = NULL,
  hb_value = TRUE,
  hb_valueFORCED = NA,
  extend_time = 100,
  ...
)

predict_Nsurv_ode(
  object,
  data_predict,
  spaghetti,
  mcmc_size,
  hb_value,
  hb_valueFORCED,
  extend_time,
  interpolate_length,
  interpolate_method,
  ...
)

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 0. The default is TRUE.
ratio_no.NA	A numeric between 0 and 1 standing for the proportion of non-NA values required to compute quantile. The default is 0.95.
hb_valueFORCED	If hb_value is FALSE, it fix hb.
extend_time	Length of time points interpolated with variable exposure profiles.
...	Further arguments to be passed to generic methods
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.

Value

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

an object of class predict_Nsurv.

The function returns an object of class survFitPredict_Nsurv, which is a list with the two following data.frame:

df_quantile

A data.frame with 10 columns, time, conc, replicate, Nsurv (observed number of survivors) and other columns with median and 95% credible interval of the number of survivors computed with 2 different way refers as check and valid: Nsurv_q50_check, Nsurv_qinf95_check, Nsurv_qsup95_check, Nsurv_q50_valid, Nsurv_qinf95_valid, Nsurv_qsup95_valid. The _check refers to the number of survivors at time t predicted using the observed number of survivors at time t-1, while the _valid refers to the number of survivors predicted at time t based on the predicted number of survivors at time t-1.

df_spaghetti

NULL if arguement spaghetti = FALSE. With spaghetti = TRUE, it returns a dataframe with all simulations based on MCMC parameters from a survFit object.

an object of class predict_Nsurv_ode.

Examples

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

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


# (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 dataset
predict_out <- predict(object = out, data_predict = data_4prediction, spaghetti = TRUE)





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

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


# (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),
                               Nsurv = c(20,20,17,16,15,15,15,14,13,12))

# (5) Predict Nsurv on a new data set
predict_out <- predict_Nsurv(object = out, data_predict = data_4prediction, spaghetti = TRUE)

morse documentation built on Oct. 29, 2022, 1:14 a.m.