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# Extract CpGs needed for prediction of GA (GAprediction package)
# type="se" (default) designates model with penalty term
# lambda within less one standard error of minimum
# (se="min", gives CpGs for minimum lambda model, se="all" gives all CpGs
# require by prediction model, even those not estimated)
# Returns a vector with CpG sites
extractSites<-function( type="se" ){
# data(glmnetPredictor)
tempMat<-NULL
allCpGs<-NULL
all<-FALSE
if (type=="se"){
# lambda's within one std. error of minimum retains fewer components
# and model performance is comparable to minimum lambda, therefore it is default
tempMat<-as.matrix(coef(UL.mod.cv, s="lambda.1se"))
}
# Minimum lambdas, slightly better performance, but more CpGs retained
else if (type=="min") {
tempMat<-as.matrix(coef(UL.mod.cv, s="lambda.min"))
}
else if (type=="all") {
tempMat=as.matrix(coef(UL.mod.cv))
all<-TRUE
}
else{
message("Unknown type, please choose \"se\", \"min\" or \"all\" for the appropriate set of CpGs\n")
stop("Exiting...")
}
# Extract all CpGs from trained model
tempMat=data.frame(tempMat)
names(tempMat)="est"
# Extract only coeffcients that are used
if(all==TRUE){
allCpGs=rownames(tempMat)
return(allCpGs[-1])
}
else{
predictorSites=rownames(tempMat)[which(tempMat$est!=0)]
return(predictorSites[-1])
}
}
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