I can't see from what you have posted unless it is that you are labelling a fixed parameter. Please send your input, data, output, and license number to email@example.com.
Jill McClain posted on Thursday, September 10, 2009 - 5:20 pm
Hi Drs. Muthen. I am trying to use the model constraint command to generate predicted values for a dependent variable for set values of my independent variables (from a linear regression). I can do this by hand, of course, but I'm hoping that using labeled parameters in Mplus will properly propagate the errors so that my predicted values will have appropriate confidence intervals (please let me know if this is not the case). However, I cannot figure out how to label or otherwise include the intercept (other than simply entering the intercept value from the output, in which case the error won't be accounted for). Is it possible to label the intercept in the model command or otherwise indicate that I want to use the estimated intercept and its standard error in the model constraint calculation? Thanks.
Thanks very much. That worked perfectly. Oddly, though, the SE for yhatxi is substantially smaller when I use the labeled parameter "a" than when I simply insert the numerical value of a (which has no error as far as Mplus knows) into my constraint equation. Is that correct?
Bootstrapped CIs are unlikely to disagree for raw and standardized indirect effects. I typically decide on significance based on the raw and if I want to describe the effect size I compare it to the SDs of the X and Y (that is, I consider the standardized effect) - but without further discussing significance for the standardized.
I am trying to recover the regression coefficients from two covariates within a mixture model after accounting for classification error. "c" represents 5 latent classes. I would like to get the coefficients to further compute predictive probabilities with model constraint. At this point, I'm just trying to get them into model constraint to use.
I keep getting: The number of equality/parameter labels in the following statement does not match the number of parameters.