Can you clarify: when fitting a zero-inflated binomial or poisson model, how is the outcome coded for the categorical part of the model? Does a "1" indicate that the outcome is zero or non-zero? In other words, if the same covariate is included in both parts of the model and has the same direction of effect in both, is the coefficient in both parts of the model positive or is it negative in the categorical model?
In the binary part Mplus does logistic regression for Prob(u#=1), where u# is a binary latent inflation variable and u#=1 indicates that the individual is unable to assume any value except 0. This is called the zero class.
For the non-zero class, any count value can be assumed and is modeled via regular Poisson.
So I guess one can say that the direction is not the same in the two parts.
Dear Linda, I am trying to run growth models for two parallel processes allowing each of the processes to have zero inflated distributions, probably negative binomial. Each process (offending and victimization) is measured annually during 5 years. Can Mplus do this type of analysis? If yes, could you please direct me to where I can find examples of syntax, etc. Thanks, Arina.
When running a negative binomial model with a single predictor I continually get a stdyx estimate of 1. In these models the p-value for the undstandardized coefficient is highly significant, while the standardize p is not. This problem does not seem to occur when multiple predictors are entered into the model. I am having a hard time understanding why this is occurring.
It probably does not make sense to use StdYX with a count outcome. I would use the raw coefficient or at most StdX which you would have to compute yourself.
Daniel Lee posted on Thursday, March 26, 2020 - 9:13 am
Hi Dr. Muthen,
I am reading the Regression and Mediation Analysis Using Mplus, and I wonder if there is any way to get predicted counts for a zero-truncated negative binomial model. That is, if the mean of all covariates are set at their respective mean, a unit increase in X would predict Y expected counts.
I'm guessing that Model Constraint is involved. If so, can you point me to syntax or provide maybe a simple example (2 covariates) to get me started.