Message/Author 

Phil Wood posted on Thursday, February 15, 2007  1:03 pm



Hi folks, For the general education of my students, I did a small twopredictor regression in SAS and then in Mplus: analysis: type=meanstructure Model: gpa on hsrank act; hsrank with act; [hsrank* act* gpa*] output: residual tech1 standardized; savedata: file=admissioninf.txt; save is mahalanobis loglikelihood influence cooks; I notice that Cooks D from Mplus doesn't match that reported by SAS(As a matter of fact, the values only correlate .86) Any ideas as to why? Thanks! thanks! 


I would need to see the SAS output and the Mplus input, data, and output to explain this. Please send these files to support@statmodel.com. 


I'm running alternative CFA models and want to identify individuals to whom specific models do/don't apply. 1. Is there a way to get Mahalanobis distances and loglikelihood contributions that are model specific (e.g., mahalanobis d using the implied covariance matrix)? 2. Where can I find more information on how the influence variable is computed exactly? The distribution of the influence scores is somewhat awkward (e.g., 8 highly negative values, then a major cluster of respondents close to but above zero, and then a group of positive values). Can I detect if a value is not valid because of model nonconvergence? I suspect the models may converge to different local optima (sample sizes vary around 50). 


See pages 606607 of the Mplus User's Guide and the references mentioned there. 


I presume that the INFLUENCE command reports the difference in the value of the optimized criterion when a case is deleted. The question I still have is: which criterion? (I checked the reference by Cook & Weisberg (1982) (free download at http://conservancy.umn.edu/handle/37076). I compared individual influence values to model fit results including/excluding the case I'm looking at, but I can't find the link. According to what I see it's not the change in LL, 2LL, chiČ, unless I'm doing something wrong.) 


The criterion is the loglikelihood. You exclude case i and compute the estimates. Then you look at the L1L2(i) where L1 is the usual ML likelihood based on all the data while L2(i) is the likelihood based on all the data evaluated at the ith parameter estimates. 

Phil Wood posted on Wednesday, August 03, 2011  5:36 pm



Would it be possible to get influence diagnostics (such as mahalanobis or influence) for Bayesian models? I was just wondering, given that they're an option for ML. Do you recommend that we calculate these in ML and then use that as a basis for excluding observations in Bayes? thanks! 


These types of diagnostics are not currently available with Bayes. Looking at ML would be one alternative. 

Back to top 