[R] multiple imputation with fit.mult.impute in Hmisc

[Jonathan Baron]

I have always avoided missing data by keeping my distance from
the real world.  But I have a student who is doing a study of
real patients.  We're trying to test regression models using
multiple imputation.  We did the following (roughly):

f <- aregImpute(~ [list of 32 variables, separated by + signs],
 n.impute=20, defaultLinear=T, data=t1)
# I read that 20 is better than the default of 5.
# defaultLinear makes sense for our data.

fmp <- fit.mult.impute(Y ~ X1 + X2 ... [for the model of interest],
 xtrans=f, fitter=lm, data=t1)

and all goes well (usually) except that we get the following
message at the end of the last step:

 Warning message: Not using a Design fitting function;
 summary(fit) will use standard errors, t, P from last imputation
 only.  Use Varcov(fit) to get the correct covariance matrix,
 sqrt(diag(Varcov(fit))) to get s.e.

I did try using sqrt(diag(Varcov(fmp))), as it suggested, and it
didn't seem to change anything from when I did summary(fmp).

But this Warning message sounds scary.  It sounds like the whole
process of multiple imputation is being ignored, if only the last
one is being used.

So I discovered I could get rid of this warning by loading the
Design library and then using ols instead of lm as the fitter in
fit.mult.imput.  It seems that ols provides a variance/covariance
matrix (or something) that fit.mult.impute can use.

But here I am beyond my (very recently acquired) understanding of
what this is all about.

Should I worry about that warning message?  Or am I maybe off the
track in some larger way?

-- 
Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page:            http://www.sas.upenn.edu/~baron
R page:               http://finzi.psych.upenn.edu/