All, For my understanding, I wanted to see if I can get glmnet predictions using both the predict function and also by multiplying coefficients by the variable matrix. This is not worked out. Could anyone suggest where I am going wrong? I understand that I may not have the mean/intercept correct, but the scaling is also off, which suggests a bigger mistake.
Thanks for your help. Juliet Hannah library(ElemStatLearn) library(glmnet) data(prostate) # training data data.train <- prostate[prostate$train,] y <- data.train$lpsa # isolate predictors data.train <- as.matrix(data.train[,-c(9,10)]) # test data data.test <- prostate[!prostate$train,] data.test <- as.matrix(data.test[,-c(9,10)]) # scale test data by using means and sd from training data trainMeans <- apply(data.train,2,mean) trainSDs <- apply(data.train,2,sd) # create standardized test data data.test.std <- sweep(data.test, 2, trainMeans) data.test.std <- sweep(data.test.std, 2, trainSDs, "/") # fit training model myglmnet =cv.glmnet(data.train,y) # predictions by using predict function yhat_enet <- predict(myglmnet,newx=data.test, s="lambda.min") # attempting to get predictions by using coefficients beta <- as.vector( t(coef(myglmnet,s="lambda.min"))) testX <- cbind(1,data.test.std) yhat2 <- testX %*% beta # does not match plot(yhat2,yhat_enet) ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
