#training and fitting a lasso model using the function glmnet::cv.glmnet
#function expects dataframe x with one column named "RT" which gets predicted
fit.glmnet<- function(x,alpha=1){
print("Computing model glmnet ... Please wait ...")
cv_glmnet<- glmnet::cv.glmnet(x= data.matrix(x[,-which(colnames(x) =="RT")]),
y = x[,which(colnames(x) =="RT")],
alpha=alpha,
type.measure = "mse", nfolds = 10,
nlambda =200,
standardize = FALSE,
family = "gaussian")
plot(cv_glmnet)
model_glmnet<-glmnet::glmnet(x= data.matrix(x[,-which(colnames(x) =="RT")]),
y = x[,which(colnames(x) =="RT")],
family = "gaussian",
alpha=alpha,
standardize = FALSE,
lambda = cv_glmnet$lambda.min)
print("End training")
return(model_glmnet)
}
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