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Message/Author
 Paul Spin posted on Friday, December 01, 2017 - 3:19 pm
A relative newbie to Mplus...

I am attempting to model

Y= B0 + B1*LAS + B2*Z + error

where Y is a count variable, LAS is a latent class variable w/ 3 categories, and Z is a vector of covariates.

I am following Web Notes 21 [Section 3.2], which returns class-specific intercepts and slope for each Z. Instead, I would like to get class-independent main effects for Z while still allowing for class-specific intercepts.

Here is my code:

TITLE: STAGE 1: Estimate latent class model;
DATA: File = data.csv ;
VARIABLE:
NAMES = y a1-a8 z;
USEVARIABLES = a1-a8 ;
CATEGORICAL = a1-a8;
CLASSES = c(3);
AUXILIARY = y z;
ANALYSIS: TYPE = MIXTURE;
Savedata: File = lcaoutput.csv ;
Save = bchweights;


TITLE: Y on C and Z ;
DATA: File = lcaoutput.csv:
VARIABLE:
NAMES = y a1-a8 z W1-W3 MLC;
USEVARIABLES = y z W1-W3 ;
CLASSES = c(3);
Training = W1-W3(bch);
ANALYSIS: TYPE = MIXTURE;
MODEL:
%Overall%
C y on z;
%c#1%
y on z;
%c#2%
y on z;
%c#3%
y on z;


Q1: How to I modify the second input file to obtain what I described above?
Q2: How do I test for statistically significant differences in the class-specific intercepts?

Thank you!
 Bengt O. Muthen posted on Friday, December 01, 2017 - 5:18 pm
Q1: Try dropping the class-specific y on z statements. The intercept varies by class as the default.

Q2: Give parameter labels to the class-specific intercepts in the Model command, like:

%c#1%
[y] (p1);
%c#2%
[y] (p2);
%c#3%
[y] (p3);

and then use Model Test to do Wald testing like testing if all 3 are the same:

0 = p1-p2;
0 = p3-p1;
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