Dear Robin, You already have a literal answer to your question, which is to look at MIcombine.default, but this is just implements Rubin's rules for combining multiple imputations, which are described in most treatments of the subject.
What's curious is that with only one missing observation among 1409, you would have a fraction of missing information for the coefficient of u of 64%. Of course, the fraction of missing information for a coefficient isn't simply the fraction of observations missing for the corresponding variable, since the coefficient for that variable will be affected by missing data on other variables, but if the several multiple imputations produce very similar coefficients, as should be the case when only one observation is missing, then the fraction of missing information should be small for all coefficients. So something is out of whack here. In particular, you don't say how you got the multiple imputations in rt.imp, and to what extent the coefficients produced from them differ. I suspect that either some mistake was made, for example in preparing the data, or that the data must be extremely unusual in some respect. If I were you, I'd start by looking at how the results for the completed data sets in rt.imp differ (e.g., the coefficients of u must differ a lot), and if that doesn't reveal the problem, at the completed data sets themselves. I hope this helps, John -------------------------------- John Fox Senator William McMaster Professor of Social Statistics Department of Sociology McMaster University Hamilton, Ontario, Canada web: socserv.mcmaster.ca/jfox > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Robin Jeffries > Sent: November-07-10 1:24 PM > To: r-help@r-project.org > Subject: [R] How is MissInfo calculated? (mitools) > > What does missInfo compute and how is it computed? > There is only 1 observation missing the ethnic3 variable. There is no other > missing data. > N=1409 > > > summary(MIcombine(mod1)) > > Multiple imputation results: > with(rt.imp, glm(G1 ~ stdage + female + as.factor(ethnic3) + u, > family = binomial())) > > MIcombine.default(mod1) > results se > (lower upper) missInfo > (Intercept) -0.40895453 0.14743928 -0.70805544 -0.1098536 > 53 % > stdage 0.13991360 0.06046537 0.02140364 > 0.2584236 0 % > female -0.05587635 0.11083362 -0.27310639 > 0.1613537 0 % > as.factor(ethnic3)1 0.17297835 0.19556664 -0.21032531 0.5562820 0 > % > as.factor(ethnic3)2 0.63507020 0.18017975 0.28192410 0.9882163 0 > % > u -0.01322976 0.18896230 -0.40291914 > 0.3764596 64 % > > Thanks, > > > Robin Jeffries > MS, DrPH Candidate > Department of Biostatistics > UCLA > 530-624-0428 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
