ICcforest: Fit a conditional inference forest for interval-censored...
In ICcforest: An Ensemble Method for Interval-Censored Survival Data

Description Usage Arguments Details Value See Also Examples

View source: R/ICcforest.R

An implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners for interval-censored survival data.

ICcforest(
  formula,
  data,
  mtry = NULL,
  ntree = 100L,
  applyfun = NULL,
  cores = NULL,
  na.action = na.pass,
  suppress = TRUE,
  trace = TRUE,
  perturb = list(replace = FALSE, fraction = 0.632),
  control = partykit::ctree_control(teststat = "quad", testtype = "Univ", mincriterion =
    0, saveinfo = FALSE, minsplit = nrow(data) * 0.15, minbucket = nrow(data) * 0.06),
  ...
)

`formula`	a formula object, with the response being a `Surv` object, with form `Surv(time1, time2, type="interval2")`.
`data`	a data frame containing the variables named in `formula`.
`mtry`	number of input variables randomly sampled as candidates at each node for random forest like algorithms. The default `mtry` is tuned by `tuneICRF`.
`ntree`	an integer, the number of the trees to grow for the forest. `ntree = 100L` is set by default.
`applyfun`	an optional `lapply`-style function with arguments `function(X, FUN, ...)`. It is used for computing the variable selection criterion. The default is to use the basic `lapply` function unless the `cores` argument is specified (see below). See `ctree_control`.
`cores`	numeric. If set to an integer the `applyfun` is set to `mclapply` with the desired number of cores. See `ctree_control`.
`na.action`	a function which indicates what should happen when the data contain missing values.
`suppress`	a logical specifying whether the messages from `getFitEsts` are suppressed. If `FALSE`, the messages are printed. `suppress = TRUE` is set by default.
`trace`	whether to print the progress of the search of the optimal value of `mtry` when `mtry` is not specified (see `tuneICRF`). `trace = TRUE` is set by default.
`perturb`	a list with arguments `replace` and `fraction` determining which type of resampling, with `replace = TRUE` referring to the n-out-of-n bootstrap and `replace = FALSE` referring to sample splitting. `fraction` is the proportion of observations to draw without replacement.
`control`	a list of control parameters, see `ctree_control`. `control` parameters `minsplit`, `minbucket` have been adjusted from the `cforest` defaults. Other default values correspond to those of the default values used by `ctree_control`.
`...`	additional arguments.

ICcforest returns an ICcforest object. The object belongs to the class ICcforest, as a subclass of cforest. This function extends the conditional inference survival forest algorithm in cforest to fit interval-censored survival data.

An object of class ICcforest, as a subclass of cforest.

predict.ICcforest for prediction, gettree.ICcforest for individual tree extraction, and tuneICRF for mtry tuning.

#### Example with miceData
library(icenReg)
data(miceData)

## For ICcforest to run, Inf should be set to be a large number, for example, 9999999.
miceData$u[miceData$u == Inf] <- 9999999.

## Fit an iterval-censored conditional inference forest
Cforest <- ICcforest(Surv(l, u, type = "interval2") ~ grp, data = miceData)