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Author: Shimanto

Elastic Net Regression With Python & R/Elastic Net Regression with R.R

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+install.packages('Ecdat')
+library(Ecdat)
+library(corrplot)
+library(caret)
+library(glmnet)
+data("VietNamI")
+str(VietNamI)
+p.cor<-cor(VietNamI[,-4])
+corrplot.mixed(p.cor)
+VietNamI$commune<-NULL
+VietNamI_reduced<-VietNamI[1:1000,]
+ind<-sample(2,nrow(VietNamI_reduced),replace=T,prob = c(0.7,0.3))
+train<-VietNamI_reduced[ind==1,]
+test<-VietNamI_reduced[ind==2,]
+grid<-expand.grid(.alpha=seq(0,1,by=.5),.lambda=seq(0,0.2,by=.1))
+control<-trainControl(method = "LOOCV")
+enet.train<-train(illdays~.,train,method="glmnet",trControl=control,tuneGrid=grid)
+enet.train
+
+train$sex<-model.matrix( ~ sex - 1, data=train ) #convert to dummy variable 
+test$sex<-model.matrix( ~ sex - 1, data=test )
+predictor_variables<-as.matrix(train[,-9])
+days_ill<-as.matrix(train$illdays)
+enet<-glmnet(predictor_variables,days_ill,family = "gaussian",alpha = 0.5,lambda = .2)
+
+enet.coef<-coef(enet,lambda=.2,alpha=.5,exact=T)
+enet.coef
+
+test.matrix<-as.matrix(test[,-9])
+enet.y<-predict(enet, newx = test.matrix, type = "response", lambda=.2,alpha=.5)
+
+
+plot(enet.y)
+
+
+enet.resid<-enet.y-test$illdays
+mean(enet.resid^2)
+
+
+set.seed(317)
+enet.cv<-cv.glmnet(predictor_variables,days_ill,alpha=.5)
+plot(enet.cv)
+
+enet.cv$lambda.min
+enet.cv$lambda.1se
+
+coef(enet.cv,s="lambda.1se")
+
+
+enet.y.cv<-predict(enet.cv,newx = test.matrix,type='response',lambda="lambda.1se", alpha = .5)
+enet.cv.resid<-enet.y.cv-test$illdays
+mean(enet.cv.resid^2)