GMM, Cont distal outcome, time varyin... PreviousNext
Mplus Discussion > Growth Modeling of Longitudinal Data >
Message/Author
 shrihari sridhar posted on Monday, April 23, 2007 - 11:54 am
Hi,

I am trying to estimate this model currently

a) a vector of 3 observed varaibles measured at 5 time points
b) a vector of time invariant covariates influenceing the intercept and slope of the vector in a)
c) a scalar time varying covariate influencing the slope and intercept of of a)
d) a continuous distal outcome measured at time 6
e) a, b and c varying across classes with mixture indicator d)

my questions are

a) i saw an example of the moel with a distal outcome and no time varying covariate in Hix-Small et al 2004. Is it possible to build the model i am suggesting
b) what would be a good reference to a growth mixture model of this nature with a time varying covariate?
c) does the distal continuous outcome pose a problem?
thanks!
hari
 Linda K. Muthen posted on Tuesday, April 24, 2007 - 8:21 am
a. Yes.
b. I don't know of any such paper.
c. No.
 Jungeun Lee posted on Wednesday, April 16, 2008 - 4:07 pm
Hi,

I am hoping to run a growth mixture model which is very similar to the example 8.6 in the Mplus manual. In my model, I'd like to add another distal outcome so that a latent variable 'c' predicts my first distal outcome and then my first distal outcome predicts my final outcome. All observed variables in my model are continuous. Is it possible to run such model in Mplus? If yes, could you point me to examples that I can refer to for a Mplus syntax?
 Linda K. Muthen posted on Wednesday, April 16, 2008 - 5:07 pm
Just remove the CATEGORICAL option from Example 8.6, add the y variable to the USEVARIABLES list, and add y ON u to the MODEL command.
 Anne Chan  posted on Friday, January 22, 2010 - 6:10 am
Hello, I am planning to run a analysis which is exactly like example 8.6 in the Mplus guide, only the distal outcome in my analysis is a continous variable, but not a binary one. May I ask how to do it?
 Linda K. Muthen posted on Friday, January 22, 2010 - 6:14 am
The setup would be identical to Example 8.6 without the CATEGORICAL option.
 Anne Chan  posted on Sunday, January 24, 2010 - 5:46 pm
Thanks. I followed the instruction and got the means for each class. I would like to check if there are any significant differences of the distal outcomes between each pair of classes (not comparing all the classes altogether). May I ask is there a way to do so?
 Linda K. Muthen posted on Monday, January 25, 2010 - 9:04 am
I would use MODEL TEST. See the user's guide for further information.
 Anne Chan  posted on Tuesday, January 26, 2010 - 2:31 am
Thanks, may I ask 2 follow-up questions:

1)Is the model test command used to request Wald chi-square test?

2)Is the Model Test command both applicable to continuous distal outcomes (by comparing the means of the classes) as well as categorical distal outcomes (by comparing the thresholds)?

Thanks a lot!
 Linda K. Muthen posted on Tuesday, January 26, 2010 - 7:37 am
1. Yes.
2. Yes.
 JBP posted on Tuesday, August 31, 2010 - 11:09 am
Hello,
I also want to use the 8.6 example. First question, as my variables are censored, and i don't put Algorithm=integration it's more a LCGA than a GMM that is estimated?
In my model, x has a different status. My aim is to estimate the prediction of U (my distal categorical outcome) by C but controlling for the effect of x to estimate if the prediction of U by C still remains significant after controlling for x. I think i must introduce a regression path: u on x; and a covariance between C and x but I'm not sure how to do that. Would that be a proper specification for the model if there is three latent classes ?
MODEL:
%OVERALL%
i s | y1@0 y2@1 y3@2 y4@3;
u ON x;
c#1 with x;
c#2 with x;

I also want to estimate the same model but with the distal outcome being a count variable. Is it ok to specify: COUNT is U; instead of categorical, will the model run properly.
Many Thanks!
JB
 Bengt O. Muthen posted on Tuesday, August 31, 2010 - 4:31 pm
It is an LCGA if you don't see variances estimated for the growth factors.

I would instead say

c on x.

You can use a count variable u without changing anything else in the model.
 JBP posted on Tuesday, August 31, 2010 - 5:52 pm
Just to confirm the code would be:
u on x;
c on x;
so that the estimate for the path c->u would be controled for the confounding effect of x ?
Thanks for your quick answer!
 Linda K. Muthen posted on Wednesday, September 01, 2010 - 10:13 am
Yes.
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