On Thu, 12 Jun 2003 23:57:45 +0200 Jonck van der Kogel <[EMAIL PROTECTED]> wrote:
> Hi all, > I'm currently working with a dataset that has quite a few missing > values and after some investigation I figured that multiple imputation > is probably the best solution to handle the missing data in my case. I > found several references to functions in S-Plus that perform multiple > imputation (NORM, CAT, MIX, PAN). Does R have corresponding functions? > I searched the archives but was not able to find anything conclusive > there. > Any help on this subject is much appreciated. > Thanks, Jonck > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help Look at the aregImpute function in the Hmisc package (http://hesweb1.med.virginia.edu/biostat/s/Hmisc.html). aregImpute uses the bootstrap, predictive mean matching, and flexible additive regression models to do multiple imputation. In one simulation study it performs as well as MICE but it runs much faster and does not assume linearity in the imputation models. I hope that someday we'll have simulation studies comparing aregImpute with NORM. --- Frank E Harrell Jr Prof. of Biostatistics & Statistics Div. of Biostatistics & Epidem. Dept. of Health Evaluation Sciences U. Virginia School of Medicine http://hesweb1.med.virginia.edu/biostat ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
