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Evgenia posted on Wednesday, June 13, 2012 - 5:38 am
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I want to do Latent Variable Mixture Modeling My data have missing (MNAR) BUT I want to do analysis ignoring missing values to see differences at estimates. What changes I have to make at the following input? DATA: FILE = DatasetMisSc1.dat; VARIABLE: NAMES = u1-u12;!Var u7-u12 are indicators of missingness MISSING ARE all (9); !Missing CATEGORICAL = u1-u12; CLASSES = c (2); 2 classes ANALYSIS: TYPE = MIXTURE ; ALGORITHM = INTEGRATION; STARTS =30 10; MODEL: %overall%! f by u1-u12@0;! [f@0]; f@1; %C#1% f by u7-u12*1; [u1$1-u12$1*-1]; %C#2% f by u7-u12*1; [u1$1-u12$1*+1]; Thanks in advance |
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Add LISTWISE=ON; to the DATA command. |
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Evgenia posted on Thursday, June 14, 2012 - 2:46 am
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Folowing your suggestion I add LISTWISE=ON; to the DATA command. But then I take error message *** ERROR Categorical variable U7 contains less than 2 categories which is true since U7-U12 are missing indicators and deleting listewise missing at vars U1-U6 make U7-U12 being constant equal to 1. Thanks again |
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You should not use listwise deletion with missing value indicators. |
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Evgenia posted on Wednesday, June 20, 2012 - 10:30 pm
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I want one more clarrification. Having both data and missing value indicators if I use only MISSING ARE all (9); and no listwise deletion (previous post and reply), what is the analysis MPLUS do? It ignores missing in each case? Could you explain to me more? Thanks alot Evgenia |
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Mplus uses all available data to estimate the model according to Little and Rubin. See the user's guide for the reference. |
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