A good place to start if you have convergence problems is by looking in the Index of the Mplus User's Guide under Convergence Problems and following the suggestions given there. If you continue to have problems, send the full output and data to email@example.com.
Anonymous posted on Monday, January 03, 2005 - 1:41 pm
Even when following the suggestions on pp 160-62 of the Mplus Guide i continue running in convergence problems. Could it be because of the relatively small sample size (n=150) or is due to low factor variance?
Hi, this is my first time joining the dicussion group. I am a PhD student running my first SEM in Mplus.
I have a model, which is well grounded in theory and should be ok. When running the model in Mplus, I got the following WARNING which I do not know quite how to either interpret or fix. Can you please help me with what to do?
Mplus WARNING: "THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS -0.544D-16. PROBLEM INVOLVING PARAMETER 52."
Hello, I have a data set of two variables measured at two time points. I tried to do a SEM analysis according to the example 6.13 on your guide: GROWTH MODEL FOR TWO PARALLEL PROCESSES FOR CONTINUOUS OUTCOMES WITH REGRESSIONS AMONG THE RANDOM EFFECTS.
I got the following message: THE DEGREES OF FREEDOM FOR THIS MODEL ARE NEGATIVE. THE MODEL IS NOT IDENTIFIED. NO CHI-SQUARE TEST IS AVAILABLE. CHECK YOUR MODEL.
Could the problem be that I only have two time points for each variable? How many measurements of each variable are required for this kind of analysis?
Hi! I'm running a longitudinal latent path analysis. I keep getting the following message.
*** WARNING Data set contains cases with missing on all variables except x-variables. These cases were not included in the analysis. Number of cases with missing on all variables except x-variables: 110
There is no solution to this. Observations with all missing data on the set of dependent variables in the model cannot contribute to model estimation. Depending on your sample size, coverage may still be high.
If you think you are reading your data incorrectly, send the output, data set, and your license number to firstname.lastname@example.org.