permute_select_surv: permutation variable selection

In iBST: Improper Bagging Survival Tree

permutation variable selection

Description

Variable selection using the permutation test on several scores of importance: IIS, DIIS and DEPTH.

Usage

permute_select_surv(xdata, 
    Y.names, 
    P.names, 
    T.names, 
    importance = "IIS", 
    method = "R2",
    Bag, 
    args.rpart, 
    args.parallel = list(numWorkers = 1), 
    nperm = 50)

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
importance	The importance score to consider: either IIS, DIIS or DEPTH
method	The splitting method: either "R2" for the proposed pseudo-R2 criterion or "LR" for the adjusted Logrank criterion
Bag	The number of Bagging samples to consider
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
args.parallel	a list containing the number of parallel computing arguments: The number of workers, the type of parallelization to achieve, ... see mclapply for further details.
nperm	The number of permutation samples to consider for the permutation test

Details

Testing weither the importance score is null or not.

Value

A list of five elements:

pvalperm1	The permutation test P-values ranking in decreasing order
pvalperm2	The permutation test P-values ranking in decreasing order considering an approximate gaussian distribution under the null hypothesis
pvalKS	The Kolmogorov-Smirnov P-values of the comparisons between the observed importance under the null hypothesis and a theoretical gaussian distribution
IMPH1	The observed importance score
PERMH0	A matrix with the importance scores for each permutation sample in each column

Author(s)

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

## Not run: 
 myarg = list(cp = 0, maxcompete = 0, maxsurrogate = 0, maxdepth = 2)
 Y.names = c("T3" ,"D3")
 P.names = 'Z2'
 T.names = c("Z1", paste("Z", 3:11, sep = ''))
 mybag = 40
 set.seed(5000)
 
 data(burn)
 resperm0 <- suppressWarnings(permute_select_surv(xdata = burn, 
       Y.names, 
       P.names, 
       T.names, 
       method = "LR", 
       Bag = mybag, 
       args.rpart = myarg, 
       args.parallel = list(numWorkers = 1), 
       nperm = 150))
 
## End(Not run)

iBST documentation built on Jan. 12, 2023, 5:15 p.m.