sparse.model.matrix loses rows in R

Question

I am working on regular data.frame that looks to be to big for glm function so I've decided I'll work on a sparse represantation of a model matrix so I could put this sparse matrix into glmnet function. But sparse.model.matrix looks like to drops some rows from original matrix. Any idea why that happens and any solution how to avoid that? Code below:

> mm <- sparse.model.matrix(~clicks01+kl_tomek*bc1+hours+plec+1, 
data = daneOst)
> dim(mm)
[1] 1253223     292
> dim(daneOst)
[1] 1258836       6

Answer 1

I've had some success with changing the na.action to na.pass, this includes all the rows in my matrix:

options(na.action='na.pass')

Just note that this is a global option, so you probably want to set it back to it original value after, to not mess with the rest of your code.

previous_na_action <- options('na.action')
options(na.action='na.pass')
# Do your stuff...

options(na.action=previous_na_action$na.action)

Solution from this answer.

Answer 2

It's due to the NA's !

Run sum(complete.cases(mm)). I bet it also gives you 1253223.

So replace the NA's in your dataframe by a value (eg. 'IMPUTED_NA' or -99999), and then try again.