Results in Probability Scale PreviousNext
Mplus Discussion > Categorical Data Modeling >
 James Algina posted on Saturday, March 12, 2016 - 8:33 am
Following is a simple model I estimated using both ML and Bayes (50000 biterations, PSR ~ 1.00 for last 45000). The estimates of the thresholds are quite different for the two estimation procedures and, as a consequence, so are the probabilities estimated from the threshold estimates. However with ML the estimates in the Results in Probability Scale (RPS) are equal to those computed from the threshold estimates. With Bayes
this is not true. Further, the RPS for ML and Bayes almost identical (one estimate differs in the 3rd place) and for RPS ML standard errors and the Bayes posterior SD are identical.

Are the RPS results for Bayes not computed from the threshold estimates or is the code incorrect for using BAYES?


[DV$1] (THRESH1);
[DV$2] (THRESH2);
model constraint:
new P_L P_M P_H ;
P_H = 1/(1 + EXP(Thresh2));
P_M= 1/(1 + EXP(Thresh1)) - 1/(1 + EXP(Thresh2));
P_L = 1- 1/(1 + EXP(Thresh1));
 Linda K. Muthen posted on Saturday, March 12, 2016 - 9:44 am
ML is logistic. Bayes is probit. The way to compute probabilities differs. See Chapter 14 of the user's guide.
Back to top
Add Your Message Here
Username: Posting Information:
This is a private posting area. Only registered users and moderators may post messages here.
Options: Enable HTML code in message
Automatically activate URLs in message