

Obtaining TIF/ICCs from an SPFA 

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Jon Heron posted on Monday, October 19, 2015  7:04 am



Hi Bengt/Linda i'm investigating the nonnormal nature of a trait through the use of a semiparametric FA. I have eight 3category items and am estimating a 2class model. My first attempt yielded 25 parameters (7 invariant loadings, 16 invariant thresholds, a single nonzero factor mean in class 1 and a mean for my latentclass split). However, plot3 gave me precious little other than a scatterplot or two. I then added "algorithm = integration". This gives me many more plots (ICC/TIF etc) and an additional parameter  a withinclass factor variance. However my model is now struggling. My likelihood has changed markedly and I am unable to replicate it. I have tried to impose a constraint to regain my 25parameter model e.g. fix my withinclass variance to one, or my withinclass means to zero and one, but no success. I've also boosted the random starts and also the integration points. Any thoughts? has something rather fundamental change now I've applied integration? many thanks, Jon 


It may be a function of the data  perhaps that withinclass variance isn't needed. The UG examples 7.17 and 7.27 are close to what you are doing, so in principle this can work fine. 

Jon Heron posted on Tuesday, October 20, 2015  4:05 am



ace, thanks Bengt bw, Jon 

Jon Heron posted on Tuesday, October 20, 2015  10:32 am



Ahh, I have achieved enlightenment. I'm sharing, with no additional questions: My first model (without integration) is actually NPFA. This explains the lack of a quoted factor variance and also the lack of ICC/TIF. Constraining the withinclass variance to zero in the second model (the one with integration) does indeed yield the first model. So with categorical data and no integration only means are estimated whereas with continuous indicators (as in 7.17) one also obtains a withinclass factor variance. To model factor variances with categorical indicators one needs integration. This sounds just the same as the situation for LCGA and GMM. best, Jon 


Right on. 

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