iBST-package: Improper Bagging Survival Tree

Description Details Author(s) References See Also Examples

Description

Fit a bagging survival tree on a mixture of population (susceptible and nonsusceptible) using either a pseudo R2 criterion or an adjusted Logrank criterion. The predictor is evaluated using the Out Of Bag Integrated Brier Score (IBS) and several scores of importance are computed for variable selection. The thressholds values for variable selection are computed using a nonparametric permutation test.

Details

Package: iBST
Type: Package
Version: 1.0
Date: 2017-01-30
License: GPL(>=2.0)

Author(s)

Cyprien Mbogning and Philippe Broet

Maintainer: Cyprien Mbogning <[email protected]>

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 improper_tree

Examples

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## Not run: 
 data(burn)
 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)
 
 ## fit an improper survival tree
 burn.tree <- improper_tree(burn, Y.names, P.names, T.names, method = "R2", args.rpart = myarg)
 plot(burn.tree)
 text(burn.tree, cex = .7, xpd = TRUE)
 
 ## fit an improper Bagging survival tree with the adjusted Logrank criterion
 burn.BagEssai0 <- Bagg_Surv(burn, Y.names, P.names, T.names, method = "LR", args.rpart = myarg, 
                           args.parallel = list(numWorkers = 1), Bag = mybag)
 
 ## fit an improper Bagging survival tree with the pseudo R2 criterion
 burn.BagEssai1 <- Bagg_Surv(burn, Y.names, P.names, T.names, method = "R2", args.rpart = myarg, 
                           args.parallel = list(numWorkers = 1), Bag = mybag)

 ## Plot the variable importance scores
 par(mfrow=c(1,3))
barplot(burn.BagEssai1$IIS, main = 'IIS', horiz = TRUE, las = 1,
        cex.names = .8, col = 'lightblue')
barplot(burn.BagEssai1$DIIS, main = 'DIIS', horiz = TRUE, las = 1,
        cex.names = .8, col = 'grey') 
barplot(burn.BagEssai1$DEPTH, main = 'MinDepth', horiz = TRUE, las = 1,
        cex.names = .8, col = 'purple')


 ## evaluation of the Bagging predictors 
pred0 <- Bagg_pred_Surv(burn, Y.names, P.names, burn.BagEssai0, 
                        args.parallel = list(numWorkers = 1), OOB = TRUE) 
 
 
 pred1 <- Bagg_pred_Surv(burn, Y.names, P.names, burn.BagEssai1, 
                         args.parallel = list(numWorkers = 1), OOB = TRUE) 
  
 ## The OOB integrated Brier score using the Breslow estimator
 pred1$OOB$IBSBREOOB
 
 ## The permutation importance score using the Breslow estimator
 pred1$OOB$vimpoobpbpbre
 
## End(Not run)

iBST documentation built on May 30, 2017, 3:31 a.m.