Description Usage Arguments Details Value See Also Examples
An implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners for intervalcensored survival data.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  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

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. 
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 intervalcensored 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.
1 2 3 4 5 6 7 8 9  #### 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 itervalcensored conditional inference forest
Cforest < ICcforest(Surv(l, u, type = "interval2") ~ grp, data = miceData)

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