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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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