R help - Jul 2003 - multiple imputation with fit.mult.impute in Hmisc

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/

On Sun, 27 Jul 2003 14:47:30 -0400
Jonathan Baron <baron at psych.upenn.edu> wrote:
> 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.
The warning message may be ignored.  But the advice to use Varcov(fmp) is faulty
for lm fits - I will fix that in the next release of Hmisc.  You may get the
imputation-corrected covariance matrix for now using fmp$var

> 
> 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.
That works too.

Frank
> 
> 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/
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://www.stat.math.ethz.ch/mailman/listinfo/r-help

---
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