Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:05 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
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
________ ________ ________ ________ ________
0.526 -0.004 -0.042 -0.109 -0.037
Means
Y4 Z1 Z2 Z3 Z4
________ ________ ________ ________ ________
-0.023 0.035 -0.028 -0.107 0.031
Means
Z5
________
-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
UNIVARIATE SAMPLE STATISTICS
UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
X1 0.545 -0.181 0.000 45.51% 0.000 0.000 1.000
356.000 0.248 -1.967 1.000 54.49% 1.000 1.000
X2 -0.021 -0.057 -4.200 0.27% -1.219 -0.409 -0.059
375.000 2.167 -0.158 3.678 0.27% 0.309 1.288
Y1 -0.144 0.018 -4.778 0.29% -1.324 -0.470 -0.114
343.000 2.235 0.229 5.167 0.29% 0.194 1.053
Y2 -0.245 0.126 -4.142 0.30% -1.457 -0.604 -0.272
338.000 2.085 -0.028 4.738 0.30% 0.178 0.901
Y3 -0.303 -0.031 -4.660 0.31% -1.464 -0.774 -0.384
321.000 1.879 -0.012 3.858 0.31% 0.057 0.921
Y4 -0.402 -0.115 -5.694 0.32% -1.612 -0.813 -0.364
315.000 2.088 0.063 3.423 0.32% -0.043 0.911
Z1 0.035 0.058 -2.549 0.20% -0.774 -0.208 0.074
500.000 0.936 0.051 3.200 0.20% 0.287 0.813
Z2 -0.028 -0.003 -3.182 0.20% -0.908 -0.338 -0.047
500.000 1.031 -0.190 2.988 0.20% 0.206 0.850
Z3 -0.107 0.188 -2.698 0.20% -0.947 -0.436 -0.143
500.000 0.980 -0.092 3.128 0.20% 0.096 0.779
Z4 0.031 0.174 -2.567 0.20% -0.761 -0.230 -0.006
500.000 0.986 0.154 3.637 0.20% 0.247 0.829
Z5 -0.035 -0.043 -3.167 0.20% -0.882 -0.241 -0.023
500.000 0.949 -0.152 2.587 0.20% 0.210 0.766
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
Save missing symbol *
Beginning Time: 23:05:15
Ending Time: 23:05:16
Elapsed Time: 00:00:01
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