Mplus VERSION 6
MUTHEN & MUTHEN
04/25/2010 10:58 PM
INPUT INSTRUCTIONS
TITLE:
penn1
1-class no covariates
DATA: FILE IS lsay.dat;
FORMAT is 3f8 f8.4 8f8.2 3f8 2f8.2;
VARIABLE: NAMES ARE cohort id school weight math7 math8 math9 math10
att7 att8 att9 att10 gender mothed homeres;
USEOBS = (gender EQ 1 AND cohort EQ 2);
MISSING = ALL (999);
USEVAR = math7-math10 ;
classes = c(1);
ANALYSIS: TYPE = mixture;
miterations = 5;
MODEL:
%overall%
intercpt BY math7-math10 @1;
slope BY math8@1 math9@2.5 math10@3.5;
[math7-math10@0];
math7-math9*7 math10*13;
intercpt*64.5 slope*1.3;
slope with intercpt*3.1;
%c#1%
[intercpt*42.8 slope*.6];
OUTPUT: TECH1 tech8;
*** WARNING
Data set contains cases with missing on all variables.
These cases were not included in the analysis.
Number of cases with missing on all variables: 8
1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS
penn1
1-class no covariates
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1482
Number of dependent variables 4
Number of independent variables 0
Number of continuous latent variables 2
Number of categorical latent variables 1
Observed dependent variables
Continuous
MATH7 MATH8 MATH9 MATH10
Continuous latent variables
INTERCPT SLOPE
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 5
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
Maximum number of iterations for H1 2000
Convergence criterion for H1 0.100D-03
Optimization algorithm EMA
Input data file(s)
lsay.dat
Input data format
(3F8 F8.4 8F8.2 3F8 2F8.2)
SUMMARY OF DATA
Number of missing data patterns 13
Number of y missing data patterns 13
Number of u missing data patterns 0
COVARIANCE COVERAGE OF DATA
Minimum covariance coverage value 0.100
PROPORTION OF DATA PRESENT FOR Y
Covariance Coverage
MATH7 MATH8 MATH9 MATH10
________ ________ ________ ________
MATH7 0.990
MATH8 0.881 0.890
MATH9 0.790 0.758 0.799
MATH10 0.744 0.708 0.702 0.750
THE MODEL ESTIMATION TERMINATED NORMALLY
TESTS OF MODEL FIT
Loglikelihood
H0 Value -16741.551
H0 Scaling Correction Factor 1.364
for MLR
Information Criteria
Number of Free Parameters 9
Akaike (AIC) 33501.101
Bayesian (BIC) 33548.812
Sample-Size Adjusted BIC 33520.221
(n* = (n + 2) / 24)
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 1482.00000 1.00000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 1482.00000 1.00000
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 1482 1.00000
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1
1 1.000
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
INTERCPT BY
MATH7 1.000 0.000 999.000 999.000
MATH8 1.000 0.000 999.000 999.000
MATH9 1.000 0.000 999.000 999.000
MATH10 1.000 0.000 999.000 999.000
SLOPE BY
MATH8 1.000 0.000 999.000 999.000
MATH9 2.500 0.000 999.000 999.000
MATH10 3.500 0.000 999.000 999.000
SLOPE WITH
INTERCPT 3.686 0.761 4.841 0.000
Means
INTERCPT 51.668 0.239 216.181 0.000
SLOPE 2.464 0.060 40.726 0.000
Intercepts
MATH7 0.000 0.000 999.000 999.000
MATH8 0.000 0.000 999.000 999.000
MATH9 0.000 0.000 999.000 999.000
MATH10 0.000 0.000 999.000 999.000
Variances
INTERCPT 73.916 3.026 24.425 0.000
SLOPE 1.518 0.278 5.452 0.000
Residual Variances
MATH7 13.824 1.317 10.492 0.000
MATH8 13.461 1.058 12.723 0.000
MATH9 16.148 1.258 12.833 0.000
MATH10 28.345 2.884 9.829 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.724E-03
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
MATH7 MATH8 MATH9 MATH10
________ ________ ________ ________
1 0 0 0 0
LAMBDA
INTERCPT SLOPE
________ ________
MATH7 0 0
MATH8 0 0
MATH9 0 0
MATH10 0 0
THETA
MATH7 MATH8 MATH9 MATH10
________ ________ ________ ________
MATH7 1
MATH8 0 2
MATH9 0 0 3
MATH10 0 0 0 4
ALPHA
INTERCPT SLOPE
________ ________
1 5 6
BETA
INTERCPT SLOPE
________ ________
INTERCPT 0 0
SLOPE 0 0
PSI
INTERCPT SLOPE
________ ________
INTERCPT 7
SLOPE 8 9
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0
STARTING VALUES FOR LATENT CLASS 1
NU
MATH7 MATH8 MATH9 MATH10
________ ________ ________ ________
1 0.000 0.000 0.000 0.000
LAMBDA
INTERCPT SLOPE
________ ________
MATH7 1.000 0.000
MATH8 1.000 1.000
MATH9 1.000 2.500
MATH10 1.000 3.500
THETA
MATH7 MATH8 MATH9 MATH10
________ ________ ________ ________
MATH7 7.000
MATH8 0.000 7.000
MATH9 0.000 0.000 7.000
MATH10 0.000 0.000 0.000 13.000
ALPHA
INTERCPT SLOPE
________ ________
1 42.800 0.600
BETA
INTERCPT SLOPE
________ ________
INTERCPT 0.000 0.000
SLOPE 0.000 0.000
PSI
INTERCPT SLOPE
________ ________
INTERCPT 64.500
SLOPE 3.100 1.300
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1
________
1 0.000
TECHNICAL 8 OUTPUT
ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE CLASS COUNTS ALGORITHM
1 -0.18821421D+05 0.0000000 0.0000000 1482.000 EM
2 -0.16741551D+05 2079.8705490 0.1105055 1482.000 EM
3 -0.16741551D+05 0.0000000 0.0000000 1482.000 EM
Beginning Time: 22:58:14
Ending Time: 22:58:15
Elapsed Time: 00:00:01
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