Re: [R] Is there a good package for multiple imputation of missing values in R?

From: Robert A LaBudde <>
Date: Mon, 30 Jun 2008 13:55:26 -0400

At 03:02 AM 6/30/2008, Robert A. LaBudde wrote:
>I'm looking for a package that has a start-of-the-art method of
>imputation of missing values in a data frame with both continuous
>and factor columns.
>I've found transcan() in 'Hmisc', which appears to be possibly
>suited to my needs, but I haven't been able to figure out how to get
>a new data frame with the imputed values replaced (I don't have
>Herrell's book).
>Any pointers would be appreciated.

Thanks to "paulandpen", Frank and Shige for suggestions.

I looked at the packages 'Hmisc', 'mice', 'Amelia' and 'norm'.

I still haven't mastered the methodology for using aregImpute() in 'Hmisc' based on the help information. I think I'll have to get hold of Frank's book to see how it's used in a complete example.

'Amelia' and 'norm' appear to be focused solely on continuous, multivariate normal variables, but my needs typically involve datasets with both factors and continuous variables.

The function mice() in 'mice' appears to best suit my needs, and the help file was intelligible, and it works on both factors and continuous variables.

For those in the audience with similar issues, here is a code snippet showing how some of these functions work ('felon' is a data frame with categorical and continuous predictors of the binary variable 'hired'):

library('mice') #missing data imputation library for md.pattern(), mice(), complete()
names(felon) #show variable names
md.pattern(felon[,1:4]) #show patterns for missing data in 1st 4 vars

library('Hmisc') #package for na.pattern() and impute() na.pattern(felon[,1:4]) #show patterns for missing data in 1st 4 vars

#simple imputation can be done by
felon2<- felon #make copy

felon2$felony<- impute(felon2$felony) #impute NAs (most frequent)
felon2$gender<- impute(felon2$gender) #impute NAs
felon2$natamer<- impute(felon2$natamer) #impute NAs
na.pattern(felon2[,1:4]) #show no NAs left in these vars fit2<- glm(hired ~ felony + gender + natamer, data=felon2, family=binomial) summary(fit2)

#better, multiple imputation can be done via mice(): imp<- mice(felon[,1:4]) #do multiple imputation (default is 5 realizations) for (iSet in 1:5) { #show results for the 5 imputation datasets

   fit<- glm(hired ~ felony + gender + natamer,      data=complete(imp, iSet), family=binomial) #fit to iSet-th realization    print(summary(fit))

Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail:
Least Cost Formulations, Ltd.            URL:
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire" mailing list PLEASE do read the posting guide and provide commented, minimal, self-contained, reproducible code. Received on Mon 30 Jun 2008 - 17:58:52 GMT

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