View source: R/ensemble_model.R
ensemble_model | R Documentation |
Method for training a stacking ensemble model for Methylation Correlation Block.
ensemble_model(single_res,training_set,Surv_training,testing_set, Surv_testing,ensemble_type)
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. |
ensemble_type |
Secondary model use for ensemble, one of "Cox", "C-index" and "feature weighted linear regression". "feature weighted linear regression" only uses two meta-features namely kurtosis and S.D. |
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. |
mboost | Model object for the mboost model at first level. |
stacking | Model object for the stacking model. |
Xin Yu
Xin Yu et al. 2019 Predicting disease progression in lung adenocarcinoma patients based on methylation correlated blocks using ensemble machine learning classifiers (under review)
#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])
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