R - Logistic Regression - Sparse Matrix -

i have dataset has 1000 features , 30,000 rows. of data 0's. storing information in sparse matrix. perform column wise logistic regression - each feature vs dependent variable.

my question how perform logistic regression on sparse matrices.i stumbled glmnet package requires minimum 2 columns. here sample code

require(glmnet) x = matrix(rnorm(100*1),100,1) y = rnorm(100) glmnet(x,y) 

this gives me error. wondering if there other package might have missed?

any appreciated. all

this more workaround solution. can add column 1s (cbind(1, x)) one-column matrix. new column used estimating intercept. therefore, have use argument intercept = false.

glmnet(cbind(1, x), y, intercept = false)