In your case, it may not be sensible to simply fill missing values by mean or mode as multiple imputation becomes the norm this day. For your specific question, na.roughfix in randomForest package would do the work.
Weidong Gu On Tue, Oct 11, 2011 at 8:11 AM, francesca casalino <francy.casal...@gmail.com> wrote: > Dear R experts, > > I have a large database made up of mixed data types (numeric, > character, factor, ordinal factor) with missing values, and I am > looking for a package that would help me impute the missing values > using either the mean if numerical or the mode if character/factor. > > I maybe could use replace like this: > df$var[is.na(df$var)] <- mean(df$var, na.rm = TRUE) > And go through all the many different variables of the datasets using > mean or mode for each, but I was wondering if there was a faster way, > or if a package existed to automate this (by doing 'mode' if it is a > factor or character or 'mean' if it is numeric)? > > I have tried the package "dprep" because I wanted to use the function > "ce.mimp", btu unfortunately it is not available anymore. > > Thank you for your help, > -francy > > ______________________________________________ > 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. > ______________________________________________ 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.
