# Variable selection with elastic net with R (glmnet, carret) [closed]

I have a matrix of 40 observations and 747 variables, which are frequencies with a certain number of zeros. My observations are divided into two groups, resume in the vector "rep_pdrp".

I want to select variables with good discriminant and predictive performance. So I decided to use caret and glmnet R packages as described here : https://quantmacro.wordpress.com/2016/04/26/fitting-elastic-net-model-in-r/

lambda.grid <- seq(0, 100)
alpha.grid <- seq(0, 0.5, length = 6)

trnCtrl = trainControl(
method = "repeatedCV",
number = 10,
repeats = 5)

srchGrd = expand.grid(.alpha = alpha.grid, .lambda = lambda.grid)

my.train <- train(x = data.matrix(my.data),
y = rep_pdrp,
method = "glmnet",
tuneGrid = srchGrd,
trControl = trnCtrl,
standardize = FALSE,
maxit = 1000000)

plot(my.train)

my.glmnet.model <- my.train$finalModel sum(coef(my.glmnet.model, s = my.train$bestTune\$lambda)!=0)


The "bestTune" parameter are alpha = 0.1 and lambda = 100. When I run again with a larger lambda.grid, it's always the max of lambda which is given in the output... Do you know what is the issue ?

Moreover, all coefficients equal zero, except the intercept. I don't understand why. Have you good an idea of the problem?

## closed as unclear what you're asking by gung♦Dec 17 '18 at 17:20

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• Did you use set.seed() function before running feature selection? Without it, you will get different results each time. Also, it is advised to use standardization in the penalized regression. – Malgorzata Maciukiewicz Dec 17 '18 at 16:30