?na.omit ??
But it is certainly true that omitting missing values first and analyzing the remaining data can: 1. Leave you with no data to analyze 2. Result in biased and misleading conclusions if the missingness mechanism is related to the covariates. In general, handling data with missing values properly can be a very difficult or even insoluble problem. You may wish to consult with a local statistician knowledgeable in such matters (I am not, for example). Cheers, Bert On Fri, Nov 29, 2013 at 6:12 AM, Laura Buzdugan <laubuzdu...@gmail.com> wrote: > Hi everyone, > > I have a large dataset with missing values. I tried using glmnet, but it > seems that it cannot handle NA values in the design matrix. I also tried > lars, but I get an error too. Does anyone know of any package for computing > the lasso solution which handles NA values? > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- Bert Gunter Genentech Nonclinical Biostatistics (650) 467-7374 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
