Dear imputers I'm a Ph.D student and working on missing data, multiple imputation...I'm a little bit confused and have some questions : 1. When I estimate the fraction of missing information from a multiple imputation with Gibbs sampling , I obtained always higher the fraction of missing information than multiple imputation with stochastic EM. Is this normal? Do you have similar results? 2. Let's assume that I have data contains some complete and some incomplete variables, and I want to estimate the fraction of missing information. I expect that the fraction of missing information for complete variables should be 0. Is this idea wrong? Especially, if we impute data with Gibbs, they are not equal to zero.. 3. Let's say I want to estimate the fraction of missing information. I have two options to impute data: First, I can estimate parameters from Gibbs complete data(i.e. after each draw of Ymissing, I have a complete data) In this case, I obtained 0 fraction of missing information for variables that I have complete data. (Is this improper multiple imputations?). Second, I can use parameter draws from Gibbs(of course, after convergence) and estimate the fraction of missing information(I think this os proper imputation??). In this situation, I don't have 0 fraction of missing information for variables which are complete. Which method is correct? 4. I have two different designs(missing by design) for the same data set and I want to compare these two different designs(i.e. different missing data patterns) using the fraction of missing information of parameters. Does the fraction of missing information show only missing information after imputation?Let's say if the imputation works very well for both designs, then shall we expect the fraction of missing information be the same amount for both designs? Do you suggest me any other methods(statistics) to show which designs contain more information before imputation? I hope these are not stupid questions and I can get some reply. Thanks in advance for any help. Feray Adiguzel -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20040804/fbdba03f/attachment.htm From rlaforge <@t> uri.edu Tue Aug 17 13:01:47 2004 From: rlaforge <@t> uri.edu (Robert Laforge) Date: Sun Jun 26 08:25:02 2005 Subject: [Impute] Multiple Imputation: Can you obtain proc mixed Type III table results from PROC MIANALYZE? Message-ID: <007301c48484$46d0b910$6501a...@rgl>
Hi, I would like to use SAS Proc MiANALYZE to output and summarize the Type III table results for fixed effects that is part of the routine output ( default ) from SAS PROC MIXED. I have run 20 imputed datasets and fed the parameters to MIANALYZE and get the summarized output for the model parameters, but is there is a way to have the Type III table results also summarized. They are available for all 20 of the imputed data sets by default. I had no luck trying to figure it out from the SAS 9 manual. Anybody ever done this? Thanks , bob Laforge SAS code proc mixed NOCLPRINT NOINFO NOITPRINT; class id time group; model problem=time group time*group /solution; random intercept / subject=id; by _imputation_; format time tfmt.; ods output solutionf=mixparms; run; proc mianalyze parms=mixparms; class time group; modeleffects intercept time group time*group; title "MI analysis for Mixed random intercept 20 datasets"; run; -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20040817/80221caf/attachment.htm From JUDKIND1 <@t> westat.com Thu Aug 19 13:23:32 2004 From: JUDKIND1 <@t> westat.com (David Judkins) Date: Sun Jun 26 08:25:02 2005 Subject: [Impute] fractionofmissinginformation Message-ID: <[email protected]> This is just a guess, but it sounds to me like you might being using the draws from the Gibbs sampler for reported observations as well as missing observations. Assuming you have 5 multiple imputations, you want to simply make 5 copies of the reported data for the variable on a responding case. With this procedure, the post-imputation variance estimate will equal the complete-data variance estimate and so the fraction of missing information will be 0. --Dave Judkins -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Adiguzel, Feray Sent: Wednesday, August 04, 2004 5:26 PM To: [email protected] Subject: [Impute] fractionofmissinginformation Dear imputers I'm a Ph.D student and working on missing data, multiple imputation...I'm a little bit confused and have some questions : 1. When I estimate the fraction of missing information from a multiple imputation with Gibbs sampling , I obtained always higher the fraction of missing information than multiple imputation with stochastic EM. Is this normal? Do you have similar results? 2. Let's assume that I have data contains some complete and some incomplete variables, and I want to estimate the fraction of missing information. I expect that the fraction of missing information for complete variables should be 0. Is this idea wrong? Especially, if we impute data with Gibbs, they are not equal to zero.. 3. Let's say I want to estimate the fraction of missing information. I have two options to impute data: First, I can estimate parameters from Gibbs complete data(i.e. after each draw of Ymissing, I have a complete data) In this case, I obtained 0 fraction of missing information for variables that I have complete data. (Is this improper multiple imputations?). Second, I can use parameter draws from Gibbs(of course, after convergence) and estimate the fraction of missing information(I think this os proper imputation??). In this situation, I don't have 0 fraction of missing information for variables which are complete. Which method is correct? 4. I have two different designs(missing by design) for the same data set and I want to compare these two different designs(i.e. different missing data patterns) using the fraction of missing information of parameters. Does the fraction of missing information show only missing information after imputation?Let's say if the imputation works very well for both designs, then shall we expect the fraction of missing information be the same amount for both designs? Do you suggest me any other methods(statistics) to show which designs contain more information before imputation? I hope these are not stupid questions and I can get some reply. Thanks in advance for any help. Feray Adiguzel -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20040819/0eff5fc2/attachment.htm
