ICcforest {ICcforest} | R Documentation |
Fit a conditional inference forest for interval-censored survival data
Description
An implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners for interval-censored survival data.
Usage
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),
...
)
Arguments
formula |
a formula object, with the response being a
|
data |
a data frame containing the variables named in |
mtry |
number of input variables randomly sampled as candidates at each node for
random forest like algorithms. The default |
ntree |
an integer, the number of the trees to grow for the forest. |
applyfun |
an optional |
cores |
numeric. If set to an integer the |
na.action |
a function which indicates what should happen when the data contain missing values. |
suppress |
a logical specifying whether the messages from |
trace |
whether to print the progress of the search of the optimal value of |
perturb |
a list with arguments |
control |
a list of control parameters, see |
... |
additional arguments. |
Details
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.
Value
An object of class ICcforest
, as a subclass of cforest
.
See Also
predict.ICcforest
for prediction, gettree.ICcforest
for individual tree extraction, and tuneICRF
for mtry
tuning.
Examples
#### 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)