On Mon, 28 Jul 2003 08:18:09 -0400 Jonathan Baron <[EMAIL PROTECTED]> wrote:
> 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. summary is using the usual lm output, for the last fit, so it is not adjusted for multiple imputation. Varcov(fmp) is using what summary uses because I forgot to tell Varcov.lm to look for fmp$var first. Frank > > >> So I discovered I could get rid of this warning by loading the > >> Design library and then using ols instead of lm as the fitter in > >> fit.mult.imput. It seems that ols provides a variance/covariance > >> matrix (or something) that fit.mult.impute can use. > > > >That works too. > > That gives me what I get if I use lm and then recalculate the t > values "by hand" from fmp$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 --- 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 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help