permute_select_surv {iBST}

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)