David Rein posted on Friday, April 26, 2002 - 9:09 am
What method does Mplus use to estimate continuous regression models within classes?
If it is maximum likelihood, is the distributional assumption one of normality? Gamma, whatever?
If it is quasi-liklihood, how is the link and the variance parameters defined?
Getting more to the point, for the model I am examining, the best estimates for a non-class model are derived from a quasi-likelihood approach in which the distribution is not specificed, the and the link and variance functions are specified based on a prior exploaration of the data.
Given that I find a link function = mean**.03 and variance function equal to mean**2.4, is there any way to specify this in Mplus to get model estimates?
One alternative I see is to use the FMM to find classes. Then use Mplus, or another program to estimate each regression seperately by class using GLM and the link and variance function.
bmuthen posted on Friday, April 26, 2002 - 4:33 pm
Mplus uses ML, assuming within each class conditional normality given covariates. This implies that the overall (mixture, not class-specific) distribution is very non-normal.
I don't know if the link fcn mean and the variance fcn values can be used somewhow.
David Rein posted on Friday, July 11, 2003 - 1:10 pm
Is there a standard way to test if the coefficients on the same exogneous variable are statistically different between classes? For example to test the null that the difference between age on wealth is different between class one (the middle class) and class two (the wealthy) is equal to zero?
The quick and dirty way seems to be to calculate the confidence intervals for each variable and just see ifthey overlap, but I'm thinking there must be another way to do this.
Just do two runs, one where they are equal and one where they are not. Take 2 times the loglikelihood difference and test by chi-square using as df the difference in number of parameters.
David Rein posted on Monday, July 14, 2003 - 8:55 am
Sounds like a good test, except that I have around a 40 variables, and 4 classes - If I understand you correctly, I'll need to run 40 sets of comparison models to identify statistical differences - and thats just between no class differences and any class differences, not mentioning differences between class 1 and 2, and class 1 and 3, 1 and 4, 2 and 3, 2 and 4, etc.
I'm following your logic, just wish there was an easier or automated way.
Anonymous posted on Thursday, July 17, 2003 - 1:13 pm
I'm attempting to estimate a SEM of the below form in Mplus:
X ----> L ----> Y X ------------> Y.
Is it possible to specify that one of the X's is correlated with Y, but not the intervening latent class measurement model L ?
Whenever I attempt to exclude one of the X's from the first set of equations (i.e., where L is in intervening variable), Mplus returns the warning that the "excluded" X will be treated as a Y variable, and then produces error msgs for the regressions of the "excluded" X on Y.
bmuthen posted on Thursday, July 17, 2003 - 1:30 pm
Yes, you can handle the model you show in the figure. The second line is given as "y on x" and this x need not be part of "l on x". If this doesn't clear it up, please send your input, output, and data to email@example.com.
David Rein posted on Wednesday, August 06, 2003 - 10:03 am
Is there a way to specify a Poisson or NB distribution for a regression of count data within a mixture model?
You can look at Tech1 and see which parameters vary across classes - those are the model features that describe the class differences. The Mplus default is that means vary across classes, except for factor indicators in BY statements. So in your case the factor means vary across classes by default. In addition, your regression slope varies because you have specified that. If you don't want the factor means to vary across classes you say