tuneICRF: Tune mtry to the optimal value with respect to out-of-bag...
In ICcforest: An Ensemble Method for Interval-Censored Survival Data

Description Usage Arguments Value See Also Examples

View source: R/tuneICRF.R

Starting with the default value of mtry, search for the optimal value (with respect to Out-of-Bag error estimate) of mtry for ICcforest.

tuneICRF(
  formula,
  data,
  mtryStart = NULL,
  stepFactor = 1.5,
  ntreeTry = 100L,
  control = partykit::ctree_control(teststat = "quad", testtype = "Univ", mincriterion =
    0, saveinfo = FALSE, minsplit = nrow(data) * 0.15, minbucket = nrow(data) * 0.06),
  suppress = TRUE,
  trace = TRUE,
  plot = FALSE,
  doBest = FALSE
)

`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`.
`mtryStart`	starting value of `mtry`; default is `sqrt(nvar)`.
`stepFactor`	at each iteration, `mtry` is inflated (or deflated) by this value.
`ntreeTry`	number of trees used at the tuning step.
`control`	a list with control parameters, see `cforest`. The default values correspond to those of the default values used by `ICcforest`.
`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. `trace = TRUE` is set by default.
`plot`	whether to plot the out-of-bag error as a function of `mtry`.
`doBest`	whether to run an ICcforest using the optimal mtry found.

If doBest=FALSE (default), this returns the optimal mtry value of those searched.

If doBest=TRUE, this returns the ICcforest object produced with the optimal mtry.

sbrier_IC for evaluation of model fit for interval-censored data when searching for the optimal value of mtry.

### Example with dataset tandmob2
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.

## Create a new variable to be selected from
miceData$new = rep(1:4)

## Tune mtry 
mtryTune <- tuneICRF(Surv(l, u, type = "interval2") ~ grp + new, data = miceData)