Thanks for the quick reply! One more question, below. On 07/27/03 22:20, Frank E Harrell Jr wrote: >On Sun, 27 Jul 2003 14:47:30 -0400 >Jonathan Baron <[EMAIL PROTECTED]> 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$var
Then it seems to me that summary(fmp) is also giving incorrect std err.r, t, and p. Right? It seems to use Varcof(fmp) and not fmp$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. That gives me what I get if I use lm and then recalculate the t values "by hand" from fmp$var. Thus, ols seems like the way to go for now, if only to avoid additional calculations. Jon ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help