Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:46 PM

INPUT INSTRUCTIONS

  TITLE: this is an example of multiple imputation for a set of
  variables with missing values
  DATA: FILE = ex11.5.dat;
  VARIABLE: ! the following are all the variables in the data
  ! set:
  NAMES = x1 x2 y1-y4 v1-v50 z1-z5;
  ! the following variables will be used to create the
  ! imputed data sets:
  USEVARIABLES = x1 x2 y1-y4 z1-z5;
  ! the following variables are saved with the imputed
  ! data sets, but not used to create the imputed data
  ! sets:
  AUXILIARY = v1- v10;
  MISSING = ALL (999);

  DATA IMPUTATION:
  ! the following are the variables for which missing
  ! data will be imputed:
  IMPUTE = y1-y4 x1 (c) x2;
  NDATASETS = 10;
  ! the following data sets will contain data for the
  ! variables x1 x2 y1-y4 z1-z5 v1-v10:
  SAVE = missimp*.dat;
  ANALYSIS: TYPE = BASIC;
  OUTPUT: TECH8;



INPUT READING TERMINATED NORMALLY



this is an example of multiple imputation for a set of
variables with missing values

SUMMARY OF ANALYSIS

Number of groups                                                 1
Average number of observations                                 500

Number of replications
    Requested                                                   10
    Completed                                                   10

Number of dependent variables                                   11
Number of independent variables                                  0
Number of continuous latent variables                            0

Observed dependent variables

  Continuous
   X1          X2          Y1          Y2          Y3          Y4
   Z1          Z2          Z3          Z4          Z5

Observed auxiliary variables
   V1          V2          V3          V4          V5          V6
   V7          V8          V9          V10


Variables used for imputation

  Variables imputed as continuous
   Y1          Y2          Y3          Y4          X2

  Variables imputed as categorical
   X1


Estimator                                                       ML
Information matrix                                        OBSERVED
Maximum number of iterations                                  1000
Convergence criterion                                    0.500D-04
Maximum number of steepest descent iterations                   20
Maximum number of iterations for H1                           2000
Convergence criterion for H1                             0.100D-03
Specifications for Bayesian Estimation
  Point estimate                                            MEDIAN
  Number of Markov chain Monte Carlo (MCMC) chains               2
  Random seed for the first chain                                0
  Starting value information                           UNPERTURBED
  Treatment of categorical mediator                       OBSERVED
  Algorithm used for Markov chain Monte Carlo           GIBBS(PX1)
  Convergence criterion                                  0.500D-01
  Maximum number of iterations                               50000
  K-th iteration used for thinning                               1
Specifications for Data Imputation
  Number of imputed data sets                                   10
  H1 imputation model type                              COVARIANCE
  Iteration intervals for thinning                             100

Input data file(s)
  ex11.5.dat

Input data format  FREE


SUMMARY OF DATA FOR THE FIRST DATA SET

     Number of missing data patterns             1


SUMMARY OF MISSING DATA PATTERNS FOR THE FIRST DATA SET


     MISSING DATA PATTERNS (x = not missing)

           1
 X1        x
 X2        x
 Y1        x
 Y2        x
 Y3        x
 Y4        x
 Z1        x
 Z2        x
 Z3        x
 Z4        x
 Z5        x


     MISSING DATA PATTERN FREQUENCIES

    Pattern   Frequency
          1         500


COVARIANCE COVERAGE OF DATA FOR THE FIRST DATA SET

Minimum covariance coverage value   0.100


     PROPORTION OF DATA PRESENT


           Covariance Coverage
              X1            X2            Y1            Y2            Y3
              ________      ________      ________      ________      ________
 X1             1.000
 X2             1.000         1.000
 Y1             1.000         1.000         1.000
 Y2             1.000         1.000         1.000         1.000
 Y3             1.000         1.000         1.000         1.000         1.000
 Y4             1.000         1.000         1.000         1.000         1.000
 Z1             1.000         1.000         1.000         1.000         1.000
 Z2             1.000         1.000         1.000         1.000         1.000
 Z3             1.000         1.000         1.000         1.000         1.000
 Z4             1.000         1.000         1.000         1.000         1.000
 Z5             1.000         1.000         1.000         1.000         1.000


           Covariance Coverage
              Y4            Z1            Z2            Z3            Z4
              ________      ________      ________      ________      ________
 Y4             1.000
 Z1             1.000         1.000
 Z2             1.000         1.000         1.000
 Z3             1.000         1.000         1.000         1.000
 Z4             1.000         1.000         1.000         1.000         1.000
 Z5             1.000         1.000         1.000         1.000         1.000


           Covariance Coverage
              Z5
              ________
 Z5             1.000



RESULTS FOR BASIC ANALYSIS

NOTE:  These are average results over 10 data sets.


     ESTIMATED SAMPLE STATISTICS


           Means
              X1            X2            Y1            Y2            Y3
              ________      ________      ________      ________      ________
 1              0.526        -0.004        -0.042        -0.109        -0.037


           Means
              Y4            Z1            Z2            Z3            Z4
              ________      ________      ________      ________      ________
 1             -0.023         0.035        -0.028        -0.107         0.031


           Means
              Z5
              ________
 1             -0.035


           Covariances
              X1            X2            Y1            Y2            Y3
              ________      ________      ________      ________      ________
 X1             0.249
 X2             0.266         2.193
 Y1             0.181         1.151         2.205
 Y2             0.220         1.132         1.153         2.222
 Y3             0.230         1.070         1.070         1.098         2.006
 Y4             0.238         1.101         1.083         1.219         1.112
 Z1             0.067         0.429         0.367         0.370         0.427
 Z2             0.108         0.541         0.491         0.502         0.357
 Z3             0.078         0.343         0.400         0.442         0.320
 Z4             0.115         0.420         0.492         0.535         0.471
 Z5             0.069         0.502         0.557         0.439         0.521


           Covariances
              Y4            Z1            Z2            Z3            Z4
              ________      ________      ________      ________      ________
 Y4             2.279
 Z1             0.473         0.936
 Z2             0.521         0.021         1.031
 Z3             0.428        -0.039        -0.049         0.980
 Z4             0.410        -0.025        -0.057        -0.007         0.986
 Z5             0.431        -0.084         0.007        -0.001         0.091


           Covariances
              Z5
              ________
 Z5             0.949


           Correlations
              X1            X2            Y1            Y2            Y3
              ________      ________      ________      ________      ________
 X1             1.000
 X2             0.359         1.000
 Y1             0.244         0.523         1.000
 Y2             0.296         0.513         0.521         1.000
 Y3             0.325         0.510         0.509         0.520         1.000
 Y4             0.316         0.493         0.483         0.542         0.520
 Z1             0.139         0.299         0.256         0.256         0.311
 Z2             0.212         0.360         0.326         0.332         0.248
 Z3             0.157         0.234         0.272         0.300         0.228
 Z4             0.233         0.285         0.334         0.361         0.335
 Z5             0.142         0.348         0.385         0.303         0.377


           Correlations
              Y4            Z1            Z2            Z3            Z4
              ________      ________      ________      ________      ________
 Y4             1.000
 Z1             0.324         1.000
 Z2             0.340         0.022         1.000
 Z3             0.287        -0.041        -0.049         1.000
 Z4             0.274        -0.026        -0.056        -0.007         1.000
 Z5             0.293        -0.089         0.007        -0.001         0.094


           Correlations
              Z5
              ________
 Z5             1.000


TECHNICAL 8 OUTPUT

   TECHNICAL 8 OUTPUT FOR BAYES ESTIMATION

     CHAIN    BSEED
     1        0
     2        285380

                     POTENTIAL       PARAMETER WITH
     ITERATION    SCALE REDUCTION      HIGHEST PSR
     100              1.087               16


SAVEDATA INFORMATION


  Save file
    missimp*.dat

  Order of variables

    X1
    X2
    Y1
    Y2
    Y3
    Y4
    Z1
    Z2
    Z3
    Z4
    Z5
    V1
    V2
    V3
    V4
    V5
    V6
    V7
    V8
    V9
    V10

  Save file format           Free

  Save file record length    10000


     Beginning Time:  22:46:09
        Ending Time:  22:46:10
       Elapsed Time:  00:00:01



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