Bagg_Surv {iBST} | R Documentation |
Bagging improper survival trees
Description
Bagging sunbsampling procedure to aggregate several improper trees using either the pseudo-R2 procedure or the adjusted Logrank procedure. Several scores for variables importance are computed.
Usage
Bagg_Surv(xdata,
Y.names,
P.names,
T.names,
method = "R2",
args.rpart,
args.parallel = list(numWorkers = 1),
Bag = 100, feat = 5)
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 |
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 |
feat |
The size of features subsample. A full baging when feat is the total number of features. |
Details
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.
Value
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 |
Note
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.
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.
Duhaze Julianne et al. (2020). A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort. Frontiers in Immunology, 11.
See Also
Examples
## 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
feat_samp = length(T.names)
set.seed(5000)
burn.BagEssai0 <- suppressWarnings(Bagg_Surv(burn,
Y.names,
P.names,
T.names,
method = "LR",
args.rpart = myarg,
args.parallel = list(numWorkers = 1),
Bag = mybag, feat = feat_samp))
burn.BagEssai1 <- suppressWarnings(Bagg_Surv(burn,
Y.names,
P.names,
T.names,
method = "R2",
args.rpart = myarg,
args.parallel = list(numWorkers = 1),
Bag = mybag, feat = feat_samp))
## End(Not run)