[R] glmnet: obtain predictions using predict and also by extracting coefficients

[Juliet Hannah]

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)