Message/Author 


Dear Linda and Bengt, I am using BSEM to estimate a model with latent interactions. It seems the builtin method for latent interactions (LMS) described in the Mplus manual (Example 5.13) will not work with ESTIMATOR= Bayes. Please correct me if I'm wrong but I get the following error: *** ERROR in MODEL command Interaction variables are not allowed with ESTIMATOR=BAYES. Can you recommend another way to do latent interactions in BSEM? Would you recommend the unconstrained method by Marsh, Wen & Hau (2004, 2006)? 


Yes, we haven't gotten to XWITH for Bayes yet. I am not in a position to make a recommendation on alternatives. 


Hello Bengt & Linda, I tries an approach to estimating a latent interaction using Bayesian estimation. This approach requires specifying a single nonlinear constraint, following this example (code snippet is from Coenders, Batista & Saris 2008): model: eta1 by y1@1; eta1 by y2* (p1); eta2 by y3@1; eta2 by y4* (p2); eta3 by y1y3@1; eta3 by y2y4* (p3); model constraint: p3=p2*p1; I get the following error: *** FATAL ERROR THIS MODEL CONSTRAINT IS NOT AVAILABLE WITH BAYES ESTIMATION. However, I was able to do the following in Bayesian estimation to test an indirect effect following an example in the manual: NEW(indirect1 indirect2); indirect1 = a*b1; indirect2 = a*b2; Am I doing something wrong? or are nonlinear constraints not supported with Bayesian estimation? If not, is there another approach? Another posts mentions phantom variables but I don't know what that is and can't find anything about this term in the manual. Thanks fore your help, 'Alim 


Only NEW parameters are available with BAYES. 


Thanks. can you explain about phantom variables please or suggest where to read about this? mentioned here: http://www.statmodel.com/discussion/messages/11/7736.html?1310007137 


Google a Psychometrika article by Rindskopf. 

Tibor Zin posted on Thursday, December 06, 2018  4:19 am



Dear Dr. Muthen, I would like to ask a question about how to inspect the effect of IV on DV on different levels of moderating variable(JohnsonNeyman technique) using Bayesian estimator. Would it be correct approach to consider the value 1 as a minimum, 0 as a medium, and 1 as a maximum value? Thank you! 


Typically, you use 1 SD below and 1 SD above the mean for the moderator. Or, the 20th and 80th percentiles. 

Tibor Zin posted on Saturday, December 08, 2018  11:04 pm



Thank you for the answer, Dr. Muthen. Please, could you tell me how to obtain SD and mean of a latent variable using Bayesian estimator or 20th and 80th percentiles? I know that the question may be trivial but I do not know how to proceed. 


TECH4 gives you the mean and variance of a latent variable. The latent variables are assume to be normally distributed, so you can get the percentiles from a standard normal distribution table (subtracting the mean and dividing by the SD). 

Tibor Zin posted on Monday, December 10, 2018  1:20 am



Thank you for the advice. The problem is that when I use ML estimator, the model is not correctly estimated. When I use Bayesian estimator, TECH4 output is unavailable. My goal was to estimate an interaction between two latent variables, type = random and algorithm = integration. Please, do you know what could be a problem or whether there is another way how to approach this problem? 


Send your ML and Bayes output that you have questions about to Mplus Support along with your license number. 


Dear Dr Muthen, I am having some difficulty using BAYES estimator in a latent moderation analysis. Of course I cannot use the DEFINE command to create a latent interaction of the X and W factors... Could you please point me in the right direction to specify such a model... Many thanks in advance 


Bayes XWITH was introduced in Mplus Version 8.2 last November. 


Hello, I am trying to use BAYES XWITH but I am unsure how to define the interaction because the intearction variable e.g., inter1 is not availabe. Thanks in advance, 


If you say f1f2  f1 XWITH f2; you can then regress a variable on f1f2: y on f1 f2 f1f2; 

Back to top 