Unexpected result from cross validation - r

Question

I would like to perform 10-fold cross validation manually using prostate data to learn how to do it manually. I utilise the elasticnet package for code. I estimated the parameters by glmnet package (of course, it can perform cross validation too, but I would like to do that manually). After the analysis, It seems to me that I need a different criterion to choose tuning parameter other than minimum of cv.error because this gives the almost null model, if not so "where is my mistake?". (According to the original paper of Tibshirani, optimum model has three variables)
Here is the code
library(ElemStatLearn)
library(glmnet)
x <- scale(prostate[,1:8],T,T)
y <- scale(prostate[,9],T,F)
lambda = seq(0,1,0.02)
cv.folds <- function(n, folds = 10){
split(sample(1:n), rep(1:folds, length = n))
}
c.val <- function(x, y, K = 10, lambda, plot.it = TRUE){
n <- nrow(x)
all.folds <- cv.folds(length(y), K)
residmat <- matrix(0, length(lambda), K)
for(i in seq(K)) {
omit <- all.folds[[i]]
xk <- as.matrix(x[-omit, ])
yk <- as.vector(y[-omit])
xg <- x[omit, ]
yg <- y[omit]
fit <- glmnet(xk, yk, family="gaussian",
alpha=1, lambda=lambda,standardize = FALSE, intercept = FALSE)
fit <- predict(fit,newx=xg,lambda=lambda)
if(length(omit)==1){fit<-matrix(fit,nrow=1)}
residmat[, i] <- apply((yg - fit)^2, 2, mean)
}
cv <- apply(residmat, 1, mean)
cv.error <- sqrt(apply(residmat, 1, var)/K)
object<-list(lambda = lambda, cv = cv, cv.error = cv.error)
if(plot.it) {
plot(lambda, cv, type = "b", xlab="lambda", ylim = range(cv, cv + cv.error, cv - cv.error))
invisible(object)
}
}
result <- c.val(x,y,K = 10,lambda = lambda)
lambda.opt <- lambda[which.min(result$cv.error)]
fit <- glmnet(x, y, family="gaussian",
alpha=1, lambda=lambda.opt,standardize = FALSE, intercept = FALSE)
coef(fit)
Result:
> coef(fit)
9 x 1 sparse Matrix of class "dgCMatrix"
s0
(Intercept) .
lcavol 0.01926724
lweight .
age .
lbph .
svi .
lcp .
Edit:
Model generated directly from glmnet.
fit.lasso <- glmnet(x, y, family="gaussian", alpha=1,
standardize = FALSE, intercept = FALSE)
fit.lasso.cv <- cv.glmnet(x, y, type.measure="mse", alpha=1,
family="gaussian",standardize = FALSE, intercept = FALSE)
coef.lambda.min <- coef(fit.lasso.cv,s=fit.lasso.cv$lambda.min)
coef.lambda.1se <- coef(fit.lasso.cv,s=fit.lasso.cv$lambda.1se)
cbind(coef.lambda.min,coef.lambda.1se)
Result:
9 x 2 sparse Matrix of class "dgCMatrix"
1 1
(Intercept) . .
lcavol 0.59892674 0.5286355
lweight 0.23669159 0.1201279
age -0.06979581 .
lbph 0.09392021 .
svi 0.24620007 0.1400748
lcp . .
gleason 0.00346421 .
pgg45 0.06631013 .
The second column shows the correct (lambda.1se) result.

Answer 1

Your "mistake" is very hard to spot: it comes from the fact that glmnet will not use the order of your own lambda vector to sort the vector of results.
Example with the data you used:
res <- glmnet(x, y, lambda=lambda)
res$lambda
So when you call the command lambda[which.min(result$cv.error)] at the end of your procedure, you will not get the value corresponding to the minimum of the cross-validated error. Also, it explains why your graph looks strange.
An easy fix would be to declare lambda at the beginning of the script as a decreasing vector:
lambda = seq(1, 0, 0.02)
Final remark: be careful when using a single lambda.