I have been interested in what people have been saying about Solas, as it fits in with my biases against standalone statistical software.
I have been running a lot of simulations in S-Plus comparing MICE (which has had excellent performance in my tests) with a new S-Plus and R function I've recently developed called aregImpute. So far, the much faster bootstrap-based aregImpute function seems to work as well as the approximate Bayesian predictive distribution approach implemented in MICE, in terms of bias and MSE of regression coefficients. I have attached documentation to aregImpute and would appreciate any reactions to or questions about the algorithm proposed there. Sincerely, Frank Harrell -- 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20020415/1f59972d/aregImpute.html From nmi13 <@t> it.canterbury.ac.nz Tue Apr 16 08:35:26 2002 From: nmi13 <@t> it.canterbury.ac.nz (nmi13) Date: Sun Jun 26 08:24:59 2005 Subject: IMPUTE: Re: Solas, and other possibilities Message-ID: <3cbb6...@webmail> Dear Dr. Frank, I tried to use the aregImpute, with the example given in your manual and when the whole program is run it given an error r.dll. Can you please suggest me why this is occurring. when I tried with a less number of iterations and the two variables there is no error. It gives the results. One more doubt with the program. I created a data as follows x1<-1:100 x2<-x1+rnorm(100) orgi.x2<-x2[1:30] x2[1:30]<-NA and now used the aregImpute to impute the missing values f<-(~I(x1)+I(x2),n.impute=5) now when I check the imputed value it is just the 31st value of teh data which is obtained as imputed value, 8 times and replaced in for the first 30 values missing. When the original values are compared to the imputed there is huge difference to them and the imputed ones. I thought I might be wrong and I did a very small example this time with the following program x<-1:10 y<-x+rnorm(10) orgi.y<-y[1:3] y[1:3]<-NA f<-aregImpute(~y+x,n.impute=5) now checked the values to the original ones and found the same results as above. Am I doing some thing wrong or the package is meant to give the results the same way I don't know. Can You please correct me if I am wrong and correct if I am doing the wrong procedures? If I am correct then can you please explain the reason for only coming up with the first observed value as the imputed value. Thank you very much for your time and help.Even I tried with the small example given in your package thinking that I might be worng in creating the data set in a wrong way, but for this too the vales are the same as the first observed value in the data set. Sincerely yours Murthy. M.N.Murthy Student Department of Mathematics and Statistics University of Canterbury, New Zealand. ph: 0064-3-3411500 extn 52193
