Chelsea Jin posted on Thursday, March 01, 2012 - 9:19 pm
I've recently worked on a project involving correlated errors between a count and a continuous variables. For example, I have equations like:
y1 ON x1 - x5; y2 ON x1 - x6;
Say, y1 is a count variable. It could be negative binomial or zero-inflated. y2 is approximately normally distributed. I want to do "y1 WITH y2", however, the statement doesn't apply to a count variable with a continuous one. So, I'm looking for a solution.
Oh, it's another question... I mean there's no residual covariance between y2 and y1 this time. It's just a continuous variable regressing on a count one. Maybe the count one is a outcome of another regression,like "y1 ON x1 - x5; y2 ON x1 - x6 y1;", so y1 is a mediator.
I think I read some notes saying in Mplus, if a count variable is a predictor, then it's being considered as a continuous variable. Even it's a mediator, it's still a continuous variable. Am I right? Is there any other situation to deal with the count variable as a mediator?
Then for the first one, "f BY y1@1 y2 y3", I can get factor loadings on y2 and y3, but how can I know the correlation coefficient of the residuals between y2 and y3? The same question for the second situation.
Hello, This is an interesting problem that my apply to a parallel process model I am running. If one growth process is continuous and the other process is specified with a Poisson distribution through the COUNT command, are the covariances between the latent intercepts of each process (would also apply to the slope) specified correctly by a simple WITH statement, or would I need to specify with the BY command as described above? Thank you for your advice.