Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:58 PM
INPUT INSTRUCTIONS
TITLE:
cont6
Missing data modeling under MAR using ML
Apple tree crop data from Snedecor & Cochran (1967), n = 18,
2 missing data patterns. Little & Rubin (1987), p. 101
Source:
Little, R.J. & Rubin, D.B. (1987). Statistical Analysis with
Missing Data. New York: Wiley & Sons
DATA:
FILE IS ft29.dat;
VARIABLE:
NAMES ARE y1 y2;
MISSING ARE ALL (999);
ANALYSIS:
TYPE = MEANSTRUCTURE MISSING;
CONVERGENCE = .000001;
! for this example a somewhat stricter H0 convergence criterion than
! the default is needed to get the H0 results (obtained by quasi-
! newton optimization) and the H1 results (obtained by EM) to agree
MODEL:
y1 WITH y2;
! alternatively a regression model could be applied, using y2 ON y1
OUTPUT: SAMPSTAT;
*** WARNING in ANALYSIS command
Starting with Version 5, TYPE=MEANSTRUCTURE is the default for all
analyses. To remove means from the model, use
MODEL=NOMEANSTRUCTURE in the ANALYSIS command.
*** WARNING in ANALYSIS command
Starting with Version 5, TYPE=MISSING is the default for all analyses.
To obtain listwise deletion, use LISTWISE=ON in the DATA command.
2 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
cont6
Missing data modeling under MAR using ML
Apple tree crop data from Snedecor & Cochran (1967), n = 18,
2 missing data patterns. Little & Rubin (1987), p. 101
Source:
Little, R.J. & Rubin, D.B. (1987). Statistical Analysis with
Missing Data. New York: Wiley & Sons
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 18
Number of dependent variables 2
Number of independent variables 0
Number of continuous latent variables 0
Observed dependent variables
Continuous
Y1 Y2
Estimator ML
Information matrix OBSERVED
Maximum number of iterations 1000
Convergence criterion 0.100D-05
Maximum number of steepest descent iterations 20
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Input data file(s)
ft29.dat
Input data format FREE
SUMMARY OF DATA
Number of missing data patterns 2
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT
Covariance Coverage
Y1 Y2
________ ________
Y1 1.000
Y2 0.667 0.667
SAMPLE STATISTICS
ESTIMATED SAMPLE STATISTICS
Means
Y1 Y2
________ ________
1 14.722 49.333
Covariances
Y1 Y2
________ ________
Y1 89.534
Y2 -90.694 114.690
Correlations
Y1 Y2
________ ________
Y1 1.000
Y2 -0.895 1.000
MAXIMUM LOG-LIKELIHOOD VALUE FOR THE UNRESTRICTED (H1) MODEL IS -101.786
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Chi-Square Test of Model Fit
Value 0.000
Degrees of Freedom 0
P-Value 0.0000
Chi-Square Test of Model Fit for the Baseline Model
Value 17.948
Degrees of Freedom 1
P-Value 0.0000
CFI/TLI
CFI 1.000
TLI 1.000
Loglikelihood
H0 Value -101.786
H1 Value -101.786
Information Criteria
Number of Free Parameters 5
Akaike (AIC) 213.571
Bayesian (BIC) 218.023
Sample-Size Adjusted BIC 202.660
(n* = (n + 2) / 24)
RMSEA (Root Mean Square Error Of Approximation)
Estimate 0.000
90 Percent C.I. 0.000 0.000
Probability RMSEA <= .05 0.000
SRMR (Standardized Root Mean Square Residual)
Value 0.000
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Y1 WITH
Y2 -90.697 33.346 -2.720 0.007
Means
Y1 14.722 2.230 6.601 0.000
Y2 49.333 2.731 18.065 0.000
Variances
Y1 89.534 29.845 3.000 0.003
Y2 114.695 42.864 2.676 0.007
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.200E-02
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:58:08
Ending Time: 22:58:08
Elapsed Time: 00:00:00
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