Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:58 PM
INPUT INSTRUCTIONS
TITLE:
penn9
Piecewise growth modeling using mixtures:
capturing individually-varying transition points.
Simulated data. Starting values are true parameter values.
Source: Muthen, B. (1998). Second-generation structural equation
modeling with a combination of categorical and continuous latent
variables: New opportunities for latent class/latent growth modeling.
Forthcoming in New Methods for the Analysis of Change.
A. Sayer & L. Collins (eds.). Washington D.C.: APA.
DATA:
FILE IS wise102.dat;
VARIABLE:
NAMES ARE y1 y2 y3 y4 y5 y6 y7;
USEV ARE y1 y2 y3 y4 y5 y6 y7;
CLASSES = c(2);
ANALYSIS:
TYPE = MIXTURE;
MITERATIONS = 2000;
! many iterations are required for this model to converge
! for this particular data replication
MODEL:
%OVERALL%
int BY y1-y7@1;
slope1 BY y1-y7;
slope2 BY y1-y7;
[y1-y7@0];
y1-y7*1;
int WITH slope1@0;
int WITH slope2@0;
slope1 WITH slope2@0;
int*1.00;
slope1*0.20;
slope2*0.20;
[int*0.0];
[slope1*0.33];
[slope2*1.0];
[c#1*-.405];
%c#1%
slope1 BY y1@0 y2@1 y3@2 y4@3 y5@3 y6@3 y7@3;
slope2 BY y1@0 y2@0 y3@0 y4@0 y5@1 y6@2 y7@3;
[int*0.0];
[slope1*.33];
[slope2*1.0];
%c#2%
slope1 BY y1@0 y2@1 y3@2 y4@3 y5@4 y6@4 y7@4;
slope2 BY y1@0 y2@0 y3@0 y4@0 y5@0 y6@1 y7@2;
[int*0.0];
[slope1*.25];
[slope2*.5];
INPUT READING TERMINATED NORMALLY
penn9
Piecewise growth modeling using mixtures:
capturing individually-varying transition points.
Simulated data. Starting values are true parameter values.
Source: Muthen, B. (1998). Second-generation structural equation
modeling with a combination of categorical and continuous latent
variables: New opportunities for latent class/latent growth modeling.
Forthcoming in New Methods for the Analysis of Change.
A. Sayer & L. Collins (eds.). Washington D.C.: APA.
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 400
Number of dependent variables 7
Number of independent variables 0
Number of continuous latent variables 3
Number of categorical latent variables 1
Observed dependent variables
Continuous
Y1 Y2 Y3 Y4 Y5 Y6
Y7
Continuous latent variables
INT SLOPE1 SLOPE2
Categorical latent variables
C
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 2000
Convergence criteria
Loglikelihood change 0.100D-06
Relative loglikelihood change 0.100D-06
Derivative 0.100D-05
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-05
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-05
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
Optimization algorithm EMA
Random Starts Specifications
Number of initial stage random starts 10
Number of final stage optimizations 2
Number of initial stage iterations 10
Initial stage convergence criterion 0.100D+01
Random starts scale 0.500D+01
Random seed for generating random starts 0
Input data file(s)
wise102.dat
Input data format FREE
RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES
Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers:
-4873.313 unperturbed 0
-4873.313 195873 6
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Loglikelihood
H0 Value -4873.313
H0 Scaling Correction Factor 1.096
for MLR
Information Criteria
Number of Free Parameters 17
Akaike (AIC) 9780.626
Bayesian (BIC) 9848.481
Sample-Size Adjusted BIC 9794.538
(n* = (n + 2) / 24)
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 197.21556 0.49304
2 202.78444 0.50696
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 197.21556 0.49304
2 202.78444 0.50696
CLASSIFICATION QUALITY
Entropy 0.197
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 184 0.46000
2 216 0.54000
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.718 0.282
2 0.301 0.699
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
INT BY
Y1 1.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 1.000 0.000 999.000 999.000
Y4 1.000 0.000 999.000 999.000
Y5 1.000 0.000 999.000 999.000
Y6 1.000 0.000 999.000 999.000
Y7 1.000 0.000 999.000 999.000
SLOPE1 BY
Y1 0.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 2.000 0.000 999.000 999.000
Y4 3.000 0.000 999.000 999.000
Y5 3.000 0.000 999.000 999.000
Y6 3.000 0.000 999.000 999.000
Y7 3.000 0.000 999.000 999.000
SLOPE2 BY
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
Y5 1.000 0.000 999.000 999.000
Y6 2.000 0.000 999.000 999.000
Y7 3.000 0.000 999.000 999.000
INT WITH
SLOPE1 0.000 0.000 999.000 999.000
SLOPE2 0.000 0.000 999.000 999.000
SLOPE1 WITH
SLOPE2 0.000 0.000 999.000 999.000
Means
INT -0.008 0.174 -0.048 0.961
SLOPE1 0.268 0.077 3.497 0.000
SLOPE2 0.952 0.655 1.453 0.146
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
Y5 0.000 0.000 999.000 999.000
Y6 0.000 0.000 999.000 999.000
Y7 0.000 0.000 999.000 999.000
Variances
INT 1.109 0.118 9.424 0.000
SLOPE1 0.177 0.040 4.399 0.000
SLOPE2 0.210 0.206 1.019 0.308
Residual Variances
Y1 1.077 0.117 9.196 0.000
Y2 1.048 0.087 12.039 0.000
Y3 0.850 0.077 11.012 0.000
Y4 1.008 0.129 7.822 0.000
Y5 1.090 0.126 8.657 0.000
Y6 0.991 0.102 9.674 0.000
Y7 0.986 0.202 4.868 0.000
Latent Class 2
INT BY
Y1 1.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 1.000 0.000 999.000 999.000
Y4 1.000 0.000 999.000 999.000
Y5 1.000 0.000 999.000 999.000
Y6 1.000 0.000 999.000 999.000
Y7 1.000 0.000 999.000 999.000
SLOPE1 BY
Y1 0.000 0.000 999.000 999.000
Y2 1.000 0.000 999.000 999.000
Y3 2.000 0.000 999.000 999.000
Y4 3.000 0.000 999.000 999.000
Y5 4.000 0.000 999.000 999.000
Y6 4.000 0.000 999.000 999.000
Y7 4.000 0.000 999.000 999.000
SLOPE2 BY
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
Y5 0.000 0.000 999.000 999.000
Y6 1.000 0.000 999.000 999.000
Y7 2.000 0.000 999.000 999.000
INT WITH
SLOPE1 0.000 0.000 999.000 999.000
SLOPE2 0.000 0.000 999.000 999.000
SLOPE1 WITH
SLOPE2 0.000 0.000 999.000 999.000
Means
INT -0.292 0.271 -1.076 0.282
SLOPE1 0.296 0.061 4.841 0.000
SLOPE2 0.387 0.085 4.542 0.000
Intercepts
Y1 0.000 0.000 999.000 999.000
Y2 0.000 0.000 999.000 999.000
Y3 0.000 0.000 999.000 999.000
Y4 0.000 0.000 999.000 999.000
Y5 0.000 0.000 999.000 999.000
Y6 0.000 0.000 999.000 999.000
Y7 0.000 0.000 999.000 999.000
Variances
INT 1.109 0.118 9.424 0.000
SLOPE1 0.177 0.040 4.399 0.000
SLOPE2 0.210 0.206 1.019 0.308
Residual Variances
Y1 1.077 0.117 9.196 0.000
Y2 1.048 0.087 12.039 0.000
Y3 0.850 0.077 11.012 0.000
Y4 1.008 0.129 7.822 0.000
Y5 1.090 0.126 8.657 0.000
Y6 0.991 0.102 9.674 0.000
Y7 0.986 0.202 4.868 0.000
Categorical Latent Variables
Means
C#1 -0.028 2.270 -0.012 0.990
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.309E-04
(ratio of smallest to largest eigenvalue)
Beginning Time: 22:58:25
Ending Time: 22:58:25
Elapsed Time: 00:00:00
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