"constrain covariate xmiss(unobserved variable) that influences c"
Also, I don't see the xmiss variable you refer to.
Ahn,Taeyong posted on Saturday, February 16, 2019 - 4:18 am
Sorry.I would like to know the net effect of the explanatory variables (covariance, Xs) on the latent variable (C), except for the time-invariant effect (individual-variant effect). Can I get it by entering an additional command in the following syntax? If so, which command should I use?
" the net effect of the explanatory variables (covariance, Xs) on the latent variable (C), except for the time-invariant effect (individual-variant effect)."
This leads me to the question: Which of your explanatory variables are not time-invariant?
Ahn,Taeyong posted on Sunday, February 17, 2019 - 6:04 pm
I am analyzing the general growth mixture model with longitudinal data. I treated all covariates as time-invariant variables in my GMM and performed the multinomial logistic regression of latent class on the covariates. But I have heard that covariates can have individual-specific effects and this is unobservable covariate effects. And I understand that net effects of independent variable (covariates / explanatory variable) can be obtained while unobservable individual (exogenous variable) effects are constrained. To do this, I first need to confirm whether the individual-specific effects model is a fixed effect model or a random effect model. For this, it is said that it is necessary to compare the fitness of models that allow correlation between independent variable and unobservable individual effects and those that do not. Is this my idea right? And in my case, is it necessary to perform the fixed / random effect constraint process discussed above and can it be done? And if it is necessary and possible, what I want to know is the specific mplus syntax. (I used the 3-step approach in my analysis.) I am very embarrassed because I can not tell what I want. Do not you still understand me? Then I am really sorry. But if you understand me, please give me some help. thank.
There is the so called FE versus RE topic of longitudinal data where the random intercept (an unobserved variable) of the RE approach should be correlated with time-varying covariates.
In growth mixture modeling you have random intercept and a random slope describing growth. And it doesn't sound like you have time-varying covariates.
Ahn,Taeyong posted on Monday, February 18, 2019 - 6:49 pm
Are you saying that there are no random effects of covariates because I don't have time-varying covariates in my model. But I think that random intercept and random slope are correlated with time-invarient covariate too, because some of time-invarient covariate may have individual-specific effect as individual-varient covariate. Is this idea right?
On the other hand, Are you saying that there is no fixed effect of covariate in GMM because we have random intercept and random slope in GMM? Then, is it possible to say that we can take fixed effect of covariate from LCGA?
I am very sorry if you do not understand what I mean or if you feel my idea is nonsence. Thank.
Ahn,Taeyong posted on Tuesday, February 19, 2019 - 4:52 pm
I've read the Fixed effect regression models written by Allison(2009), And I've got the answer what I want. Thanks.