Dear Terrence,
thanks a lot for the speedy reply and the information.
I fitted it accordingly, but it seems there are problems with the predictor gender.
Age 2 48 numeric 1 0 47.5416667 1.670195e+02 0
18 Gender 3 48 factor 1 0 NA NA 2 0|1
19 Duration 4 48 numeric 1 0 113.7916667 1.631349e+04 0
20 class:Age 317 48 numeric 1 0 61.8333333 1.412652e+03 0
21*** class:Gender 318 0 logical 1 0 NA NA 0
22 class:Duration 319 48 numeric 1 0 128.2916667 1.635310e+04 0
Further, I do fit different so called "predictor families" to avoid the problem of multicollinearity. I am doing this, by including all predictors at once setting them at 0 (variance and covariance structure is already present). Then in several models I regress each predictor family solely on the intercept and the slope.
Unfortunately, it seems that some predictor families show too little variance, or do I interpret the error message the wrong way?
Error in chol.default(S) :
the leading minor of order 34 is not positive definite
In addition: Warning message:
In computeOmega(Sigma.hat = Sigma.hat, Mu.hat = Mu.hat, lavsamplestats = lavsamplestats, :
lav_model_gradient: Sigma.hat is not positive definite
Thanks again, and all the best,
Constanze