Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:12 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a two-part (semicontinuous) growth model for a
continuous outcome
DATA: FILE = ex6.16.dat;
DATA TWOPART:
NAMES = y1-y4;
BINARY = bin1-bin4;
CONTINUOUS = cont1-cont4;
VARIABLE: NAMES = x y1-y4;
USEVARIABLES = bin1-bin4 cont1-cont4;
CATEGORICAL = bin1-bin4;
MISSING = ALL(999);
ANALYSIS: ESTIMATOR = MLR;
MODEL: iu su | bin1@0 bin2@1 bin3@2 bin4@3;
iy sy | cont1@0 cont2@1 cont3@2 cont4@3;
su@0; iu WITH sy@0;
*** WARNING in MODEL command
All continuous latent variable covariances involving SU have been fixed to 0
because the variance of SU is fixed at 0.
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
this is an example of a two-part (semicontinuous) growth model for a
continuous outcome
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of dependent variables 8
Number of independent variables 0
Number of continuous latent variables 4
Observed dependent variables
Continuous
CONT1 CONT2 CONT3 CONT4
Binary and ordered categorical (ordinal)
BIN1 BIN2 BIN3 BIN4
Continuous latent variables
IU SU IY SY
Estimator MLR
Information matrix OBSERVED
Optimization Specifications for the Quasi-Newton Algorithm for
Continuous Outcomes
Maximum number of iterations 100
Convergence criterion 0.100D-05
Optimization Specifications for the EM Algorithm
Maximum number of iterations 500
Convergence criteria
Loglikelihood change 0.100D-02
Relative loglikelihood change 0.100D-05
Derivative 0.100D-02
Optimization Specifications for the M step of the EM Algorithm for
Categorical Latent variables
Number of M step iterations 1
M step convergence criterion 0.100D-02
Basis for M step termination ITERATION
Optimization Specifications for the M step of the EM Algorithm for
Censored, Binary or Ordered Categorical (Ordinal), Unordered
Categorical (Nominal) and Count Outcomes
Number of M step iterations 1
M step convergence criterion 0.100D-02
Basis for M step termination ITERATION
Maximum value for logit thresholds 15
Minimum value for logit thresholds -15
Minimum expected cell size for chi-square 0.100D-01
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Optimization algorithm EMA
Integration Specifications
Type STANDARD
Number of integration points 15
Dimensions of numerical integration 1
Adaptive quadrature ON
Link LOGIT
Cholesky OFF
Input data file(s)
ex6.16.dat
Input data format FREE
SUMMARY OF DATA
Number of missing data patterns 16
Number of y missing data patterns 16
Number of u missing data patterns 1
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR Y
Covariance Coverage
CONT1 CONT2 CONT3 CONT4
________ ________ ________ ________
CONT1 0.598
CONT2 0.490 0.740
CONT3 0.550 0.670 0.866
CONT4 0.574 0.698 0.810 0.918
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
BIN1
Category 1 0.402 201.000
Category 2 0.598 299.000
BIN2
Category 1 0.260 130.000
Category 2 0.740 370.000
BIN3
Category 1 0.134 67.000
Category 2 0.866 433.000
BIN4
Category 1 0.082 41.000
Category 2 0.918 459.000
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
CONT1 0.801 -0.224 -5.116 0.33% -0.526 0.504 0.810
299.000 2.540 0.355 4.987 0.33% 1.241 2.080
CONT2 1.703 0.127 -3.474 0.27% 0.091 1.127 1.645
370.000 3.833 0.187 7.325 0.27% 2.101 3.188
CONT3 2.682 -0.218 -5.116 0.23% 0.835 2.139 2.697
433.000 6.185 0.036 9.465 0.23% 3.318 4.903
CONT4 3.709 -0.110 -6.215 0.22% 1.358 3.188 3.761
459.000 9.405 0.437 13.749 0.22% 4.299 6.116
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 13
Loglikelihood
H0 Value -3576.088
H0 Scaling Correction Factor 1.0031
for MLR
Information Criteria
Akaike (AIC) 7178.176
Bayesian (BIC) 7232.966
Sample-Size Adjusted BIC 7191.703
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes
Pearson Chi-Square
Value 6.260
Degrees of Freedom 12
P-Value 0.9024
Likelihood Ratio Chi-Square
Value 5.689
Degrees of Freedom 12
P-Value 0.9309
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
IU |
BIN1 1.000 0.000 999.000 999.000
BIN2 1.000 0.000 999.000 999.000
BIN3 1.000 0.000 999.000 999.000
BIN4 1.000 0.000 999.000 999.000
SU |
BIN1 0.000 0.000 999.000 999.000
BIN2 1.000 0.000 999.000 999.000
BIN3 2.000 0.000 999.000 999.000
BIN4 3.000 0.000 999.000 999.000
IY |
CONT1 1.000 0.000 999.000 999.000
CONT2 1.000 0.000 999.000 999.000
CONT3 1.000 0.000 999.000 999.000
CONT4 1.000 0.000 999.000 999.000
SY |
CONT1 0.000 0.000 999.000 999.000
CONT2 1.000 0.000 999.000 999.000
CONT3 2.000 0.000 999.000 999.000
CONT4 3.000 0.000 999.000 999.000
IU WITH
SY 0.000 0.000 999.000 999.000
IY WITH
IU 0.893 0.183 4.883 0.000
SY WITH
IY 0.582 0.058 10.011 0.000
Means
IU 0.000 0.000 999.000 999.000
SU 0.880 0.068 12.979 0.000
IY 0.530 0.077 6.899 0.000
SY 1.026 0.035 29.554 0.000
Intercepts
CONT1 0.000 0.000 999.000 999.000
CONT2 0.000 0.000 999.000 999.000
CONT3 0.000 0.000 999.000 999.000
CONT4 0.000 0.000 999.000 999.000
Thresholds
BIN1$1 -0.541 0.114 -4.755 0.000
BIN2$1 -0.541 0.114 -4.755 0.000
BIN3$1 -0.541 0.114 -4.755 0.000
BIN4$1 -0.541 0.114 -4.755 0.000
Variances
IU 1.803 0.330 5.465 0.000
SU 0.000 0.000 999.000 999.000
IY 1.905 0.174 10.974 0.000
SY 0.403 0.042 9.533 0.000
Residual Variances
CONT1 0.583 0.095 6.106 0.000
CONT2 0.555 0.053 10.476 0.000
CONT3 0.522 0.064 8.153 0.000
CONT4 0.450 0.094 4.814 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.614E-02
(ratio of smallest to largest eigenvalue)
RESULTS IN PROBABILITY SCALE
Estimate
BIN1
Category 1 0.401
Category 2 0.599
BIN2
Category 1 0.255
Category 2 0.745
BIN3
Category 1 0.146
Category 2 0.854
BIN4
Category 1 0.076
Category 2 0.924
Beginning Time: 23:12:18
Ending Time: 23:12:19
Elapsed Time: 00:00:01
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