iBST (version 1.2)

improper_tree: imprper survival tree

Description

Fit an improper survival tree for the mixed population (susceptible and nonsusceptible) using either the proposed pseudo R2 criterion or an adjusted Logrank criterion

Usage

improper_tree(xdata, 
     Y.names, 
     P.names, 
     T.names, 
     method = "R2", 
     args.rpart)

Value

An unprunned improper survival tree

Arguments

xdata

The learning data frame

Y.names

A vector of the names of the two variables of interest (the time-to-event is follow by the event indicator)

P.names

The names of independant variables acting on the non-susceptible population (the plateau)

T.names

The names of independant variables acting on the survival of the susceptible population

method

The choosen method (either "LR" for the Logrank or "R2" for the proposed pseudo-R2 criterion)

args.rpart

The improper survival tree parameters: a list of options that control details of the rpart algorithm. minbucket: the minimum number of observations in any terminal <leaf> node; cp: complexity parameter (Any split that does not decrease the overall lack of fit by a factor of cp is not attempted); maxdepth: the maximum depth of any node of the final tree, with the root node counted as depth 0. ... See rpart.control for further details

Author

Cyprien Mbogning and Philippe Broet

References

Mbogning, C. and Broet, P. (2016). Bagging survival tree procedure for variable selection and prediction in the presence of nonsusceptible patients. BMC bioinformatics, 17(1), 1.

See Also

Bagg_Surv Bagg_pred_Surv

Examples

if (FALSE) {
 data(burn)
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 3)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 burn.tree <- suppressWarnings(improper_tree(burn, 
    Y.names, 
    P.names, 
    T.names, 
    method = "R2", 
    args.rpart = myarg))
    
 plot(burn.tree)
 text(burn.tree, cex = .7, xpd = TRUE)
 }