Evgenia posted on Wednesday, June 13, 2012 - 5:38 am
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;
You should not use listwise deletion with missing value indicators.
Evgenia posted on Wednesday, June 20, 2012 - 10:30 pm
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?