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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? Many thanks! |
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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. |
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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? Thank you so much! |
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Please send input, output, and data to support along with your license number. |
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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; |
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We need the data and your input and output to help. Send to support along with your license number. |
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I am using negative binomial regression in my multilevel model, this variable does not have missing values. MPlus can make imputations in other continuous variables? I got the following error: *** ERROR in DATA IMPUTATION command The DATA IMPUTATION command is not available for analysis with count, continuous-time survival, censored or nominal variables. |
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You must be saying you want to impute for the negative binomial. Example 11.5 goes over the various options that can be used with DATA IMPUTATION. |
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