recforest (version 1.0.0)

predict.recforest: Predict using a recforest model

Description

This function generates predictions from a recforest model given a set of input features.

Usage

# S3 method for recforest
predict(
  object,
  newdata,
  id_var,
  covariates,
  time_vars = c("t.start", "t.stop"),
  death_var = NULL,
  ...
)

Value

A vector of expected mean cumulative number of recurrent events per individual at the end of follow-up.

Arguments

object

A recforest model object.

newdata

A data frame containing the input features.

id_var

The name of the column containing the unique identifier for each subject.

covariates

A character vector containing the names of the columns to be used as predictors in the model.

time_vars

A length-2 character vector containing the names of the columns representing the start and stop times (default "t.start" and "t.stop").

death_var

The name of the column containing the death indicator or other any terminal event (optional).

...

Optional parameters to be passed to the low level function

Details

The predict_recforest function utilizes the ensemble of trees in the recforest model to generate predictions for new data. For each observation in newdata, the function aggregates the predictions from all trees in the recforest to provide a robust estimate.

Depending on the method specified during the initial training of the recforest model, the algorithm employs different prediction strategies:

• For standard recurrent event data, the function outputs the Nelson-Aalen estimates of the mean cumulative function.

• In the presence of terminal events, the function outputs the Ghosh-Lin estimates of the mean cumulative function.

The predictions represent the expected mean number of recurrent events for each individual at the end of the follow-up period.

References

Cook, R. J., & Lawless, J. F. (1997). Marginal analysis of recurrent events and a terminating event. Statistics in medicine, 16(8), 911-924.

Ghosh, D., & Lin, D. Y. (2002). Marginal regression models for recurrent and terminal events. Statistica Sinica, 663-688.

Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests.

Examples

data("bladder1_recforest")
trained_forest <- train_forest(
  data = bladder1_recforest,
  id_var = "id",
  covariates = c("treatment", "number", "size"),
  time_vars = c("t.start", "t.stop"),
  death_var = "death",
  event = "event",
  n_trees = 2,
  n_bootstrap = 70,
  mtry = 2,
  minsplit = 3,
  nodesize = 15,
  method = "NAa",
  min_score = 5,
  max_nodes = 20,
  seed = 111,
  parallel = FALSE,
  verbose = FALSE
)
predictions <- predict(
 trained_forest,
 newdata = bladder1_recforest,
 id_var = "id",
 covariates = c("treatment", "number", "size"),
 time_vars = c("t.start", "t.stop"),
 death_var = "death"
)