Bagg_pred_Surv {iBST} | R Documentation |
Bagging survival tree prediction
Description
Use the Bagging improper survival tree to predict on new features and to evaluate the predictor using Out Of Bag Integrated Brier Scores with either the Nelson Aalen estimator or the Breslow estimator. A permutation importance score is also computed using OOB observations.
Usage
Bagg_pred_Surv(xdata, Y.names, P.names, resBag, args.parallel = list(numWorkers = 1),
new_data = data.frame(), OOB = FALSE)
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) |
resBag |
The result of the |
args.parallel |
a list containing the number of parallel computing arguments: The number of workers, the type of parallelization to achieve, ... see |
new_data |
An optional data frame to validate the bagging procedure (the test dataset) |
OOB |
A value of |
Value
PREDNA |
A matrix with Nelson Aalen predictions on all individuals of the learning sample |
PREDBRE |
A matrix with Breslow predictions on all individuals of the learning sample |
tabhazNAa |
A list of matrix with Nelson Aalen prediction of each tree of the bagging sequence with the leaf node prediction in each column |
tabhazBRe |
A list of matrix with Breslow prediction of each tree of the bagging sequence with the leaf node prediction in each column |
OOB |
A value of |
Timediff |
The execution time of the prediction procedure |
TEST |
A value of |
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))
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)
## End(Not run)