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/ ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help