Latent growth modeling
Message/Author
 Thierno Diallo posted on Tuesday, February 05, 2008 - 12:29 pm
Hi Dr Muthen,

I am reading the article :
Muthen, B. (1998). Second-generation structural equation
modeling with a combination of categorical and continuous latent
variables: New opportunities for latent class/latent growth modeling.

and at the same time I am learning from the imput and the output from Mplus web site. I would like to ask you few questions.
From penn8,
1)How to get Table 10.3: Probality of dependence Given Class?
For example, how to calculate
P(Dep| c=1) =.20
2)What is the meaning of :
a)
Intercepts
C#1 -2.461 0.311 -7.906 0.000
C#2 -3.379 0.595 -5.681 0.000

b)
What is the meaning of Category > 1 in

LATENT CLASS ODDS RATIO RESULTS

Latent Class 1 Compared to Latent Class 2

DEP94
Category > 1 0.548 0.218 2.510 0.012

 Bengt O. Muthen posted on Wednesday, February 06, 2008 - 6:24 pm
This is part of our Short Course material for the mixture days. Very briefly,

1) Probability = 1/ (1 + exp(-Logit)) where Logit = - threshold.
Odds = Probability/(1 - Probability)
Odds Ratio = Odds (class k)/Odds (class K) for K = 4

2a) Those are the intercepts in the latent class multinomial regression on covariates ("c on x") - see chapter 13 of the UG,

2b) Categories are 1, 2, 3... for polytomous outcomes. So 1 and 2 for binary outcomes. Therefore, with a binary outcome >1 means category 2.
 Thierno Diallo posted on Monday, February 11, 2008 - 8:01 am
Hi Dr Muthen,
Thank you for your answer it was very helpful. Today my question is about Local maxima in mixture modeling. What can we do with a local maximum problem. I have already incresed the number of starts (from 50 to 500), but I still have this message:

WARNING: THE BEST LOGLIKELIHOOD VALUE WAS NOT REPLICATED. THE SOLUTION MAY NOT BE TRUSTWORTHY DUE TO LOCAL MAXIMA. INCREASE THE NUMBER OF RANDOM STARTS.