Description Usage Arguments Details Value Note Author(s) References See Also Examples
Bagging procedure to aggregate several improper trees using either the pseudoR2 procedure or the adjusted Logrank procedure. Several scores for variables importance are computed.
1 2 
xdata 
The learning data frame 
Y.names 
A vector of the names of the two variables of interest (the timetoevent is follow by the event indicator) 
P.names 
The names of independant variables acting on the nonsusceptible population (the plateau) 
T.names 
The names of independant variables acting on the survival of the susceptible population 
method 
The choosen method (either 
args.rpart 
The improper survival tree parameters: a list of options that control details of the rpart algorithm.

args.parallel 
a list containing the number of parallel computing arguments: The number of workers, the type of parallelization to achieve, ... see 
Bag 
The number of Bagging samples to consider 
For the Bagging procedure, it is mendatory to set maxcompete = 0
and maxsurrogate = 0
within the args.rpart
arguments. This will ensured the correct calculation of the importance of variables and also a better computation time.
A list of ten elements
MaxTreeList 
The list of improper survival trees computed during the bagging procedure 
IIS 
The Index Importance Score 
DIIS 
The Depth Index Importance Score 
DEPTH 
The minimum depth importance Score 
IND_OOB 
A list of length 
IIND_SAMP 
The final list of length 
IIND_SAMP 
The initial list of sample individuals used for each improper survival tree at teh beginning 
Bag 
The number of bagging samples retained at the end of the procedure after removing the trees without leaves 
indrpart 
a vector of 
Timediff 
The ellapsed time of the Bagging procedure 
This version of the code allows for the moment only one variable to have an impact on the cured population.The next version will allow more than one variable.
Cyprien Mbogning and Philippe Broet
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## 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)
burn.BagEssai0 < Bagg_Surv(burn, Y.names, P.names, T.names, method = "LR", args.rpart = myarg,
args.parallel = list(numWorkers = 1), Bag = mybag)
burn.BagEssai1 < Bagg_Surv(burn, Y.names, P.names, T.names, method = "R2", args.rpart = myarg,
args.parallel = list(numWorkers = 1), Bag = mybag)
## End(Not run)

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