Owing to the computational demanding on the analysis I have to reduce the number of integration to 7, and it works. But, I would like to know if this has compromised the obtained results, i.e. if the results are well valid. Will there be any particular areas that I should pay attention to when I read the results? Thanks. Pat
Thanks for the advise. The coefficients obtained for the model (3 dimensions of integration) with default integration and with 7 integration are generally the same, although not identical. I will need 5-7 dimensions of integration in the full model.
Thanks, Linda. I have tried Integration = Montecarlo (5000) in the analysis with 5 X-variables or dimensions of integration. I suspect that this is the way to control the maximum number of total integration points in the analysis regardless the number of X-variables I put into the model. However, it turned out that there is a great significant difference in results obtained from the analysis with Integration = Montecarlo (5000); and those with Integration = 7. For the latter, the total number of integration points is 16807. What would you suggest? Thanks. Pat
I don't see any obvious problems. Your model has three dimensions of integration which can be computationally demanding. I would run one latent variable interaction at a time. It is likely that they are not all signficant.
Katerina Gk posted on Thursday, September 26, 2013 - 2:20 pm
Thank you for your message, I run only one interaction indpara1 | par_b XWITH a1_w ;
er1_w ON indpara1; er2_w ON indpara1; er3_w ON indpara1; er4_w ON indpara1 ; er5_w ON indpara1 ; but I get this error:
THE ESTIMATED COVARIANCE MATRIX COULD NOT BE INVERTED. COMPUTATION COULD NOT BE COMPLETED IN ITERATION 463.CHANGE YOUR MODEL AND/OR STARTING VALUES.
THE MODEL ESTIMATION DID NOT TERMINATE NORMALLY DUE TO AN ERROR IN THE COMPUTATION. CHANGE YOUR MODEL AND/OR STARTING VALUES.