tune.ltrcrrf  R Documentation 
mtry
to the optimal value with respect to outofbag error for a LTRCRRF modelStarting with the default value of mtry
, search for the optimal value
(with respect to outofbag error estimate) of mtry
for ltrcrrf
.
tune.ltrcrrf(
formula,
data,
id,
mtryStart = NULL,
stepFactor = 2,
time.eval = NULL,
time.tau = NULL,
ntreeTry = 100L,
bootstrap = c("by.sub", "by.root", "by.node", "by.user", "none"),
samptype = c("swor", "swr"),
sampfrac = 0.632,
samp = NULL,
na.action = "na.omit",
trace = TRUE,
doBest = FALSE,
plot = FALSE,
ntime,
nsplit = 10L,
nodesizeTry = max(ceiling(sqrt(nrow(data))), 15),
nodedepth = NULL
)
formula 
a formula object, with the response being a

data 
a a data frame containing 
id 
variable name of subject identifiers. If this is present, it will be
searched for in the 
mtryStart 
starting value of 
stepFactor 
at each iteration, 
time.eval 
a vector of time points, at which the estimated survival probabilities are evaluated. 
time.tau 
an optional vector, with the ith entry giving the upper time limit for the
computed survival probabilities for the ith data (i.e., only computes
survival probabilies at 
ntreeTry 
number of trees used at the tuning step. 
bootstrap 
bootstrap protocol.
(1) If 
samptype 
choices are 
sampfrac 
a fraction, determining the proportion of subjects to draw
without replacement when 
samp 
Bootstrap specification when 
na.action 
action taken if the data contains 
trace 
whether to print the progress of the search. 
doBest 
whether to run a 
plot 
whether to plot the outofbag error as a function of 
ntime 
an integer value used for survival to constrain ensemble calculations
to a grid of 
nsplit 
an nonnegative integer value for number of random splits to consider
for each candidate splitting variable. This significantly increases speed.
When zero or 
nodesizeTry 
forest average terminal node size used at the tuning step. 
nodedepth 
maximum depth to which a tree should be grown. The default behaviour is that this parameter is ignored. 
If doBest = FALSE
(default), this returns the optimal mtry value of those searched.
If doBest = TRUE
, this returns the ltrcrrf
object produced with the optimal mtry
.
sbrier_ltrc
for evaluation of model fit for the optimal value of mtry
.
### Example with data pbcsample
library(survival)
Formula = Surv(Start, Stop, Event) ~ age + alk.phos + ast + chol + edema
## mtry tuned by the OOB procedure with stepFactor 3, number of trees built 10.
mtryT = tune.ltrcrrf(formula = Formula, data = pbcsample, stepFactor = 3,
ntreeTry = 10L)
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