Hello list.
For some reason, the makers of glmnet do not accept a dataframe as input.
They expect the input to be a matrix, where the dummies are already
precoded.
Now I have created a sample dataset with
. 11 factor columns with two levels
. 4 factor columns with three levels
. 135 continuous columns (from a standard normal)
. 100 observations (rows)
Say this dataframe is in dfrPredictors.
What I do now, is use the following code:
form<-paste("~",paste(colnames(dfrPredictors), collapse="+"), sep="")
dfrTmp<-model.frame(dfrPredictors, na.action=na.pass)
result<- as.matrix(model.matrix(as.formula(form), data=dfrTmp))[,-1]
This works (although admittedly, I don't understand everything of it).
However, I notice that for this rather limited dataset, this conversion
takes around 0.1 seconds user/elapsed time (on a relatively speedy laptop).
For my current work, I need to do this a lot of times on very similar
dataframes (in fact, they are multiply imputed from the same 'original'
dataframe), so I need all the speed I can get.
Does anybody know of a way that is quicker than the above? Note: because of
other uses of the dataframe, I don't have the option to do this conversion
before the imputation, so I really need the conversion itself to work
quickly.
Thanks,
Nick Sabbe
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