[R] Logistic regression with multiple imputation

[Chuck Cleland]

On 6/30/2010 1:14 AM, Daniel Chen wrote:
> Hi,
> 
> I am a long time SPSS user but new to R, so please bear with me if my
> questions seem to be too basic for you guys.
> 
> I am trying to figure out how to analyze survey data using logistic
> regression with multiple imputation.
> 
> I have a survey data of about 200,000 cases and I am trying to predict the
> odds ratio of a dependent variable using 6 categorical independent variables
> (dummy-coded). Approximatively 10% of the cases (~20,000) have missing data
> in one or more of the independent variables. The percentage of missing
> ranges from 0.01% to 10% for the independent variables.
> 
> My current thinking is to conduct a logistic regression with multiple
> imputation, but I don't know how to do it in R. I searched the web but
> couldn't find instructions or examples on how to do this. Since SPSS is
> hopeless with missing data, I have to learn to do this in R. I am new to R,
> so I would really appreciate if someone can show me some examples or tell me
> where to find resources.

  Here is an example using the Amelia package to generate imputations
and the mitools and mix packages to make the pooled inferences.

titanic <-
read.table("http://lib.stat.cmu.edu/S/Harrell/data/ascii/titanic.txt",
sep=',', header=TRUE)

set.seed(4321)

titanic$sex[sample(nrow(titanic), 10)] <- NA
titanic$pclass[sample(nrow(titanic), 10)] <- NA
titanic$survived[sample(nrow(titanic), 10)] <- NA

library(Amelia) # generate multiple imputations
library(mitools) # for MIextract()
library(mix) # for mi.inference()

titanic.amelia <- amelia(subset(titanic,
select=c('survived','pclass','sex','age')),
                         m=10, noms=c('survived','pclass','sex'),
emburn=c(500,500))

allimplogreg <- lapply(titanic.amelia$imputations,
function(x){glm(survived ~ pclass + sex + age, family=binomial, data = x)})

mice.betas.glm <- MIextract(allimplogreg, fun=function(x){coef(x)})
mice.se.glm <- MIextract(allimplogreg, fun=function(x){sqrt(diag(vcov(x)))})

as.data.frame(mi.inference(mice.betas.glm, mice.se.glm))

# Or using only mitools for pooled inference

betas <- MIextract(allimplogreg, fun=coef)
vars <- MIextract(allimplogreg, fun=vcov)
summary(MIcombine(betas,vars))

> Thank you!
> 
> Daniel
> 
> 	[[alternative HTML version deleted]]
> 
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-- 
Chuck Cleland, Ph.D.
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