There are several examples of mixture applications at www.statmodel.com under Examples, Mixture Applications. Seeing how results are reported in these papers may be helpful.
Monica posted on Wednesday, April 03, 2002 - 10:24 am
Reporting results. For a latent growth mixture model are the estimated factor mean test statistics a "t" or a "z"; for the multinomial logistic regression results it appears from Muthen & Muthen 2000 that the test statistic is a "t"--Yes?? Thanks. Monica
The test statistic is a z statistic. Actually, the cutoffs for z and t are the same for samples of any size, so this shouldn't be an issue.
Anonymous posted on Sunday, September 29, 2002 - 3:51 pm
I'm constructing a mixture model in Mplus and would like to know how to interpret the "Condition Number for the Information Matrix" Mplus provides (under the heading "QUALITY OF NUMERICAL RESULTS").
bmuthen posted on Sunday, September 29, 2002 - 5:38 pm
A very small condition number, say of a magnitude less than 1 to the power of -10 or -12, is an indication that the model is not identified. The condition number in Mplus is defined as the ratio between the smallest and largest eigenvalue of an estimator of the ML information matrix.
Yi-fu Chen posted on Thursday, March 20, 2003 - 11:56 am
Hi, Dr. Muthen,
I am running a latent class model with covariates. There are 7 binary indicators and four classes are found. I also find a covariate (say, x) has significant relationship with one of binary indicators (say, u4). I read the user guide, the example 25.10 and it says that "for each class, the probability of u4 varies as a function of x". Suppose the coefficient (x predicts u4) I find is -.429. I don't know how to interpret this coefficient in plain language? Could you provide more explanation about the interpretation of this relationship?
This coefficent is a logistic regression coefficient. This means that when x increases one unit, the logit for u4 goes down -.429 conditional on class membership. The important interpretation is that if this value is significant, it means that the probability of the u4 item given class is not constant for the values of x. Therefore, you don't have measurement invariance.