Good afternoon. I'm running what I thought was a straightforward imputation problem in SAS PROC MI. The dataset includes age, sex, and dichotomous race (none missing), 6 predictors with some missing (4 dichotomies, not grossly unbalanced, two reasonably distributed continua), unemployment at 6 waves (dichotomies, some missing), and occupational prestige at 6 waves (continuous, well-distributed, missing wherever unemployment is a yes).
Everything looks fine except the plots for the occupational prestige variables -- all the plots for the dichotomies look good. This is with a large number of burn-in iterations (10000) and iterations between imputations (5000). I'm sure most people here know more about what I'm doing than I do, so I'd appreciate any advice. Thanks, Pat Malone -- Patrick S. Malone, Ph.D., Research Scientist Duke University Center for Child and Family Policy http://fds.duke.edu/db/aas/PublicPolicy/faculty/malone http://childandfamilypolicy.duke.edu/ Yahoo Messenger: patricksmalone AOL Instant Messenger: pat2048 -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.utsouthwestern.edu/pipermail/impute/attachments/20061005/6b684171/attachment.htm From N.Smits <@t> psy.vu.nl Fri Oct 6 10:33:00 2006 From: N.Smits <@t> psy.vu.nl (Niels Smits) Date: Fri Oct 6 10:33:17 2006 Subject: [Impute] Bad ACF and time series plots In-Reply-To: <[email protected]> References: <[email protected]> Message-ID: <[email protected]> Hi, I think the problem is that your unemployment variable is in the imputation model. It is in fact a missing data indicator of occupational prestige. The unemployment variables add no new information to your data because whenever it has value 1, some variables are missing. Moreover it messes up the estimation procedure. Removing them would solve the problem. In general if all the `right' variables are in the imputation model , if there are still convergence problems try setting a hyperparameter (e.g., schafer [1997] p. 156)), which will smooth the covariance matrix and speed up the algorithm. Cheers Niels Niels Smits Research Methodology, Statistics and Data-analysis Faculty of Psychology and Education Free University Amsterdam Van der Boechorststraat 1 1081 BT Amsterdam The Netherlands Tel: +31 (0)20 5988713 Secr: +31 (0)20 5988757 Fax: +31 (0)20 5988758 Patrick Malone wrote: > Good afternoon. > > I'm running what I thought was a straightforward imputation problem in > SAS PROC MI. The dataset includes age, sex, and dichotomous race > (none missing), 6 predictors with some missing (4 dichotomies, not > grossly unbalanced, two reasonably distributed continua), unemployment > at 6 waves (dichotomies, some missing), and occupational prestige at 6 > waves (continuous, well-distributed, missing wherever unemployment is > a yes). > > Everything looks fine except the plots for the occupational prestige > variables -- all the plots for the dichotomies look good. This is > with a large number of burn-in iterations (10000) and iterations > between imputations (5000). > > I'm sure most people here know more about what I'm doing than I do, so > I'd appreciate any advice. > > Thanks, > Pat Malone > > -- > Patrick S. Malone, Ph.D., Research Scientist > Duke University Center for Child and Family Policy > http://fds.duke.edu/db/aas/PublicPolicy/faculty/malone > http://childandfamilypolicy.duke.edu/ > Yahoo Messenger: patricksmalone AOL Instant Messenger: pat2048 > >------------------------------------------------------------------------ > >_______________________________________________ >Impute mailing list >[email protected] >http://lists.utsouthwestern.edu/mailman/listinfo/impute > >
