Hi! I'm trying to conduct multiple imputation in a multilevel dataset and I have some questions: 1) I have missing values in the dependent variable and in the covariates. Mplus can make imputations in such case?
2) I am using negative binomial regression in my multilevel model, Mplus can make imputations?
3) Can I include interactions in my multilevel model and Mplus still can do imputations?
1. Yes. 2. All variables are treated as continuous unless they are on the CATEGORICAL list. No other types of variables can be imputed. 3. I would include the two variables and create the interaction after the imputation.
Hi, Dr. Muthen, I have two questions. 1) Following Ex. 9.1, I specify a two-level random intercept model with a categorical DV, which has five categories (1-5) with missing value (.). The output showed that I have 7 categories: Category 1 0.098 1573.000 Category 2 0.327 5235.000 Category 3 0.265 4240.000 Category 4 0.163 2619.000 Category 5 0.101 1621.000 Category 6 0.046 730.000 Category 7 0.000 1.000
I have no idea why Mplus presents 7 categories. Any thought?
2) What is the syntax to save the predicted Y values for both levels?
Hi, Dr. Muthen, I have missing values in the two level variable OCC. In the imputed data from Mplus, I got variations for OCC within clusters, which should not be. For instance, for cluster progidx=5, progidx OCC 5 45.7 5 65.8 5 43.3 5 12.6 5 46.4
It should be constant within this cluster. Thanks,
my input file:
DATA: file = Missing.csv; VARIABLE:
names=q98days progidx q25_lic occ female homeless usevariables=q98days q25_lic occ female homeless; count is q98days (nb); categorical is q25_lic female homeless; cluster = progidx; within = female homeless; between = q25_lic occ;
missing = all (-99);
ANALYSIS: TYPE = TWOLEVEL RANDOM; bseed = 2828; bconvergence = 0.01; PROCESSORS = 2; INTEGRATION =MONTECARLO (10); MODEL: %within% q98days on female homeless; %between% q98days on occ q25_lic;
DATA IMPUTATION: impute=occ; ndatasets = 20; save =Missing*.dat; thin = 500;