ensemble_model: Trainging stacking ensemble model for Methylation Correlation...

In EnMCB: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models

Description

Method for training a stacking ensemble model for Methylation Correlation Block.

Usage

ensemble_model(single_res,training_set,Surv_training,testing_set,Surv_testing)

Arguments

single_res	Methylation Correlation Block information returned by the IndentifyMCB function.
training_set	methylation matrix used for training the model in the analysis.
Surv_training	Survival function contain the survival information for training.
testing_set	methylation matrix used for testing the model in the analysis.
Surv_testing	Survival function contain the survival information for testing.

Value

Object of class list with elements (XXX repesents the model you choose):

cox	Model object for the cox model at first level.
svm	Model object for the svm model at first level.
enet	Model object for the enet model at first level.
stacking	Model object for the stacking model.

Author(s)

Xin Yu

References

Xin Yu et al. 2019 Predicting disease progression in lung adenocarcinoma patients based on methylation correlated blocks using ensemble machine learning classifiers (under review)

Examples

#import datasets
library(survival)
data(demo_survival_data)
datamatrix<-create_demo()
data(demo_MCBinformation)
#select MCB with at least 3 CpGs.
demo_MCBinformation<-demo_MCBinformation[demo_MCBinformation[,"CpGs_num"]>2,]
trainingset<-colnames(datamatrix) %in% sample(colnames(datamatrix),0.6*length(colnames(datamatrix)))
select_single_one=1
em<-ensemble_model(t(demo_MCBinformation[select_single_one,]),
    training_set=datamatrix[,trainingset],
    Surv_training=demo_survival_data[trainingset])

EnMCB documentation built on Dec. 22, 2020, 2 a.m.