Oops. Coefficients are returned on the scale of the original data. testX <- cbind(1,data.test) yhat2 <- testX %*% beta
# works plot(yhat2,yhat_enet) On Wed, Mar 21, 2012 at 2:35 PM, Juliet Hannah <juliet.han...@gmail.com> wrote: > 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.
