Description Usage Arguments Value See Also Examples
Starting with the default value of mtry, search for the optimal value (with respect to OutofBag error estimate) of mtry for ICcforest.
1 2 3 4 5 6 7 8 9 10 11 12 13  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

data 
a data frame containing the variables named in 
mtryStart 
starting value of 
stepFactor 
at each iteration, 
ntreeTry 
number of trees used at the tuning step. 
control 
a list with control parameters, see 
suppress 
a logical specifying whether the messages from 
trace 
whether to print the progress of the search. 
plot 
whether to plot the outofbag error as a function of 
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 intervalcensored data
when searching for the optimal value of mtry
.
1 2 3 4 5 6 7 8 9 10 11 12  ### 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)

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