I have another question with regard to Monte Carlo simulation study for a two-part growth model for a continuous outcome.
I plan to study the impact of alternative distribution assumption to parameter estimation. So, can I use Mplus v4 to generate a longitudinal data set with alternative assumption, say, gamma distribution for the continuous part? If not, would you give me some suggestions on how to do it?
Mplus does not offer the gamma distribution. But using mixtures you can obtain almost any distribution you like. For example, using Mplus to generate data with 2 latent classes, each with a normal distribution, the mixture has a skewed distribution. We used that approach in our 2002 article which you find on our web site:
Muthén, L.K. & Muthén, B.O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 4, 599-620.
Nicki Bush posted on Monday, March 12, 2007 - 8:06 am
I am examining interactions (between 2 observed variables) within a 2-part model. Are there special considerations on how to probe significant interactions in this context--I mean, given that there are significant interactions predicting both the binary and continuous parts, do I do anything other than calculate log odds for the binary outcome ones and plot 1SD below/above mean on the continuous outcome ones (ala Aiken & West)?
Also, I am unaware of any papers that have examined interactions in a 2-part model. Can you refer me to any writings or examples of this?
Hello, I want to run a two-part model with covariates on cross-sectional data (re: analyzing health care costs data with a preponderance on zeros). I already created my binary and continuous outcomes (but I know it is not necessary since V4)
Is e.g 6.16 the best starting point? If yes, how do I specify the model?
Or should I treat my problem as a censored-inflated regression WITH correlation between the two regression (e.g. 3.3)? If so, how can I make sure the censoring limit is zero? For the correlation, do I just add y1 with y1#1 in the model?
The residual covariance between the two parts of a regression model is not identified so it doesn't matter if you run the two parts separately or together. You might was to see work at the Rand Corporation by Naihua Duan on two-part regression modeling.
In a two-part growth approach, how would you label the growth curve of the y-part, when "type = missing" was applied? Normally, one labels this curve "frequency of behavior for those engaging in the behavior in question". However, since I'm using type=missing random "nonusers" are not excluded from the estimation of growth means of the y-curve. If a person was nonuser at t1 and then user at t2, according to FIML (type=missing), a sort of "imputation" is done with the missing y-value at t1 (is that correct), despite the fact that this person is a nonuser!?
I am trying to setup a twopart growth mixture model with two categorical latent variables, i.e., where c1 depicts developmental heterogeneity in the probability and where c2 depicts this for the conditional mean of y. While the model converges, it seems that my model set up is incorrect because for both c1 and c2 both measurement models (i.e., u and y) are used to define the classes. How can I keep these two part of the model separate and allowing for different number of classes for u and y. Thank you for your feedback. Below is my abreviated syntax:
%overall% iu su qu cu | etc. iy sy qy cy | etc.
c2 on c1;
Model c1: %c1#1% [ iu su qu cu]; %c1#2% [ iu su qu cu]; %c1#3% [ iu su qu cu]; Model c2: %c2#1% [ iy sy qy cy]; %c2#2% [ iy sy qy cy]; %c2#3% [ iy sy qy cy];
Hello! Is it possible to run a two-part longitudinal model with individually varying times of observation using Mplus? e.g., MODEL: iu su | b1 b2 b3 b4 AT a1 a2 a3 a4; iy sy | c1 c2 c3 c4 AT a1 a2 a3 a4;
Hello! I have done two-part growth modeling for 3 ethnic groups separately. Is it admissible to test for differences in intercepts and slopes between ethnic groups from these separate models using simple t-Tests or ANOVAs? I know it's theoretically possible to use Mixture Modeling with known classes to compare groups. But I don't want to go down that road unless absolutely necessary.
No, but it follows from first principles. You have independent samples in the different groups and you have no across-group parameter equalities which means that the estimates are uncorrelated.
To check that you get the right results you can run the 3 groups in one multi-groups run with no equalities and use MODEL Constraint to express whatever difference you are interested in - it gives you SEs and z-scores.