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I have variables w/large BVRs in my LCA. I am trying to create WITH statements to model the associations w/in each class. please advise what's wrong with the model? Variable: NAMES are wmt_lca msvt_lca nvms_lca rds_lca f_lca fp_lca fs_lca fbs_lca rbs_lca; USEVARIABLES wmt_lca msvt_lca nvms_lca rds_lca f_lca fp_lca fs_lca fbs_lca rbs_lca; CLASSES = c (3); CATEGORICAL = wmt_lca msvt_lca nvms_lca rds_lca f_lca fp_lca fs_lca fbs_lca rbs_lca; Analysis: TYPE = MIXTURE; MODEL: %OVERALL% %c#1% wmt_lca WITH msvt_lca; f_lca WITH fp_lca; fs_lca WITH fbs_lca; %c#2% wmt_lca WITH msvt_lca; f_lca WITH fp_lca; fs_lca WITH fbs_lca; %c#3% wmt_lca WITH msvt_lca; f_lca WITH fp_lca; fs_lca WITH fbs_lca; *** ERROR The following MODEL statements are ignored: * Statements in Class 1: WMT_LCA WITH MSVT_LCA F_LCA WITH FP_LCA FS_LCA WITH FBS_LCA * Statements in Class 2: WMT_LCA WITH MSVT_LCA F_LCA WITH FP_LCA FS_LCA WITH FBS_LCA * Statements in Class 3: WMT_LCA WITH MSVT_LCA F_LCA WITH FP_LCA FS_LCA WITH FBS_LCA *** ERROR One or more MODEL statements were ignored. These statements may be incorrect or are only supported by ALGORITHM=INTEGRATION. 


WITH statements for LCA with categorical outcomes needs Parameterization = Rescov. See the paper and scripts on our web site: Asparouhov, T. & Muthen, B. (2015). Residual associations in latent class and latent transition analysis. Structural Equation Modeling: A Multidisciplinary Journal, 22:2, 169177, DOI: 10.1080/10705511.2014.935844. (Download scripts). 


Thank you, after running it with the script I am getting this error and am not sure how to interpret these findings THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NONPOSITIVE DEFINITE FIRSTORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE CONDITION NUMBER IS 0.529D15. PROBLEM INVOLVING THE FOLLOWING PARAMETER: Parameter 5, %C#1%: [ MSVT_LCA$2 ] ONE OR MORE MULTINOMIAL LOGIT PARAMETERS WERE FIXED TO AVOID SINGULARITY OF THE INFORMATION MATRIX. THE SINGULARITY IS MOST LIKELY BECAUSE THE MODEL IS NOT IDENTIFIED, OR BECAUSE OF EMPTY CELLS IN THE JOINT DISTRIBUTION OF THE CATEGORICAL LATENT VARIABLES AND ANY INDEPENDENT VARIABLES. THE FOLLOWING PARAMETERS WERE FIXED: Parameter 104, [ C#3 ] 


My syntax was Title: Entering data from .csv file Data: FILE IS /Users/wfmcbr01/Desktop/Dissertation/Dissertation Work/dissertation_LCA_1126.csv; Variable: NAMES are wmt_lca msvt_lca nvms_lca rds_lca f_lca fp_lca fs_lca fbs_lca rbs_lca; USEVARIABLES wmt_lca msvt_lca nvms_lca rds_lca f_lca fp_lca fs_lca fbs_lca rbs_lca; CLASSES = c (5); CATEGORICAL = wmt_lca msvt_lca nvms_lca rds_lca f_lca fp_lca fs_lca fbs_lca rbs_lca; Analysis: TYPE = MIXTURE; algo=int; param=rescov; STARTS = 300 10; LRTSTARTS = 2 1 50 15; LRTBOOTSTRAP = 100; MODEL: %OVERALL% f_lca with fp_lca; OUTPUT: TECH1 svalues TECH10 TECH11 TECH14; 


We need to see your full output  send to Support along with your license number. Also, we ask that postings be limited to one window. Note also that you needlessly slow down computations by using STARTS together with TECH11 and TECH14  see our web note Asparouhov, T. & Muthén, B. (2012). Using Mplus TECH11 and TECH14 to test the number of latent classes. Paper can be downloaded from here. Mplus Web Notes: No. 14. May 22, 2012. 


Apologies and thank you for the recommendation. I will send it now. 

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