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patr1ckm/ mixturetest.inp

Created May 15, 2014 13:57
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Mplus GMM with a lot of errors
Raw
mixturetest.inp
TITLE: Your title goes here
DATA: FILE = "mixturetest.dat";
VARIABLE:
NAMES = V1 V2 V3 V4 V5 V6 V7 V8;
MISSING=.;
CLASSES = c(2);
ANALYSIS: TYPE = MIXTURE;
ALGO = INT;
STARTS = 100 10;
MODEL:
%OVERALL%
i| V1 V2 V3 V4 V5 V6 V7 V8;
[i];
i;
Raw
mixturetest.out
Mplus VERSION 7.11 (Mac)
MUTHEN & MUTHEN
05/15/2014 9:53 AM
INPUT INSTRUCTIONS
TITLE: Your title goes here
DATA: FILE = "mixturetest.dat";
VARIABLE:
NAMES = V1 V2 V3 V4 V5 V6 V7 V8;
MISSING=.;
CLASSES = c(2);
ANALYSIS: TYPE = MIXTURE;
ALGO = INT;
STARTS = 100 10;
MODEL:
%OVERALL%
i| V1 V2 V3 V4 V5 V6 V7 V8;
[i];
i;
INPUT READING TERMINATED NORMALLY
Your title goes here
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1000
Number of dependent variables 8
Number of independent variables 0
Number of continuous latent variables 1
Number of categorical latent variables 1
Observed dependent variables
Continuous
V1 V2 V3 V4 V5 V6
V7 V8
Continuous latent variables
I
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 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 0
Adaptive quadrature ON
Random Starts Specifications
Number of initial stage random starts 100
Number of final stage optimizations 10
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
Cholesky OFF
Input data file(s)
mixturetest.dat
Input data format FREE
SUMMARY OF DATA
Number of missing data patterns 1
Number of y missing data patterns 1
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
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
V1 1.000
V2 1.000 1.000
V3 1.000 1.000 1.000
V4 1.000 1.000 1.000 1.000
V5 1.000 1.000 1.000 1.000 1.000
V6 1.000 1.000 1.000 1.000 1.000
V7 1.000 1.000 1.000 1.000 1.000
V8 1.000 1.000 1.000 1.000 1.000
Covariance Coverage
V6 V7 V8
________ ________ ________
V6 1.000
V7 1.000 1.000
V8 1.000 1.000 1.000
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:
-17207.757 370466 41
-17207.759 848163 47
-17207.761 569131 26
-17207.761 462953 7
-17207.761 436460 89
-17207.761 784664 75
-17207.761 366706 29
-17207.761 227563 63
-17207.761 789985 67
-17207.761 568859 49
THE BEST LOGLIKELIHOOD VALUE HAS BEEN REPLICATED. RERUN WITH AT LEAST TWICE THE
RANDOM STARTS TO CHECK THAT THE BEST LOGLIKELIHOOD IS STILL OBTAINED AND REPLICATED.
WARNING: THE MODEL ESTIMATION HAS REACHED A SADDLE POINT OR A POINT WHERE THE
OBSERVED AND THE EXPECTED INFORMATION MATRICES DO NOT MATCH.
AN ADJUSTMENT TO THE ESTIMATION OF THE INFORMATION MATRIX HAS BEEN MADE.
THE CONDITION NUMBER IS -0.134D-02.
THE PROBLEM MAY ALSO BE RESOLVED BY DECREASING THE VALUE OF THE
MCONVERGENCE OR LOGCRITERION OPTIONS OR BY CHANGING THE STARTING VALUES
OR BY INCREASING THE NUMBER OF INTEGRATION POINTS OR BY USING THE MLF ESTIMATOR.
THE STANDARD ERRORS OF THE MODEL PARAMETER ESTIMATES MAY NOT BE
TRUSTWORTHY FOR SOME PARAMETERS DUE TO A NON-POSITIVE DEFINITE
FIRST-ORDER DERIVATIVE PRODUCT MATRIX. THIS MAY BE DUE TO THE STARTING
VALUES BUT MAY ALSO BE AN INDICATION OF MODEL NONIDENTIFICATION. THE
CONDITION NUMBER IS 0.288D-11. PROBLEM INVOLVING PARAMETER 12.
THE MODEL ESTIMATION TERMINATED NORMALLY
WARNING: THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE
DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A
LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT
VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES.
CHECK THE TECH4 OUTPUT FOR MORE INFORMATION.
MODEL FIT INFORMATION
Number of Free Parameters 12
Loglikelihood
H0 Value -17207.757
H0 Scaling Correction Factor 0.5844
for MLR
Information Criteria
Akaike (AIC) 34439.513
Bayesian (BIC) 34498.406
Sample-Size Adjusted BIC 34460.294
(n* = (n + 2) / 24)
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 126.56602 0.12657
2 873.43398 0.87343
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 126.56771 0.12657
2 873.43229 0.87343
CLASSIFICATION QUALITY
Entropy 0.455
CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 0 0.00000
2 1000 1.00000
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.000 0.000
2 0.127 0.873
Classification Probabilities for the Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.000 1.000
2 0.000 1.000
Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 -13.816 0.000
2 -13.816 0.000
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
I |
V1 1.000 0.000 999.000 999.000
V2 1.000 0.000 999.000 999.000
V3 1.000 0.000 999.000 999.000
V4 1.000 0.000 999.000 999.000
V5 1.000 0.000 999.000 999.000
V6 1.000 0.000 999.000 999.000
V7 1.000 0.000 999.000 999.000
V8 1.000 0.000 999.000 999.000
Means
I 4.540 0.037 122.078 0.000
Intercepts
V1 0.000 0.000 999.000 999.000
V2 0.000 0.000 999.000 999.000
V3 0.000 0.000 999.000 999.000
V4 0.000 0.000 999.000 999.000
V5 0.000 0.000 999.000 999.000
V6 0.000 0.000 999.000 999.000
V7 0.000 0.000 999.000 999.000
V8 0.000 0.000 999.000 999.000
Variances
I -0.313 0.015 -20.665 0.000
Residual Variances
V1 12.754 0.469 27.179 0.000
V2 7.222 0.354 20.393 0.000
V3 3.330 0.201 16.586 0.000
V4 1.891 0.095 20.006 0.000
V5 1.812 0.087 20.752 0.000
V6 3.507 0.209 16.797 0.000
V7 7.365 0.351 20.957 0.000
V8 12.998 0.479 27.165 0.000
Latent Class 2
I |
V1 1.000 0.000 999.000 999.000
V2 1.000 0.000 999.000 999.000
V3 1.000 0.000 999.000 999.000
V4 1.000 0.000 999.000 999.000
V5 1.000 0.000 999.000 999.000
V6 1.000 0.000 999.000 999.000
V7 1.000 0.000 999.000 999.000
V8 1.000 0.000 999.000 999.000
Means
I 4.469 0.060 74.063 0.000
Intercepts
V1 0.000 0.000 999.000 999.000
V2 0.000 0.000 999.000 999.000
V3 0.000 0.000 999.000 999.000
V4 0.000 0.000 999.000 999.000
V5 0.000 0.000 999.000 999.000
V6 0.000 0.000 999.000 999.000
V7 0.000 0.000 999.000 999.000
V8 0.000 0.000 999.000 999.000
Variances
I -0.313 0.015 -20.665 0.000
Residual Variances
V1 12.754 0.469 27.179 0.000
V2 7.222 0.354 20.393 0.000
V3 3.330 0.201 16.586 0.000
V4 1.891 0.095 20.006 0.000
V5 1.812 0.087 20.752 0.000
V6 3.507 0.209 16.797 0.000
V7 7.365 0.351 20.957 0.000
V8 12.998 0.479 27.165 0.000
Categorical Latent Variables
Means
C#1 -1.932 0.356 -5.427 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix -0.134E-02
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
1 0 0 0 0 0
NU
V6 V7 V8
________ ________ ________
1 0 0 0
LAMBDA
I
________
V1 0
V2 0
V3 0
V4 0
V5 0
V6 0
V7 0
V8 0
THETA
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
V1 1
V2 0 2
V3 0 0 3
V4 0 0 0 4
V5 0 0 0 0 5
V6 0 0 0 0 0
V7 0 0 0 0 0
V8 0 0 0 0 0
THETA
V6 V7 V8
________ ________ ________
V6 6
V7 0 7
V8 0 0 8
ALPHA
I
________
1 9
BETA
I
________
I 0
PSI
I
________
I 10
PARAMETER SPECIFICATION FOR LATENT CLASS 2
NU
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
1 0 0 0 0 0
NU
V6 V7 V8
________ ________ ________
1 0 0 0
LAMBDA
I
________
V1 0
V2 0
V3 0
V4 0
V5 0
V6 0
V7 0
V8 0
THETA
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
V1 1
V2 0 2
V3 0 0 3
V4 0 0 0 4
V5 0 0 0 0 5
V6 0 0 0 0 0
V7 0 0 0 0 0
V8 0 0 0 0 0
THETA
V6 V7 V8
________ ________ ________
V6 6
V7 0 7
V8 0 0 8
ALPHA
I
________
1 11
BETA
I
________
I 0
PSI
I
________
I 10
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
1 12 0
GAMMA(C)
I
________
C#1 0
C#2 0
STARTING VALUES FOR LATENT CLASS 1
NU
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
NU
V6 V7 V8
________ ________ ________
1 0.000 0.000 0.000
LAMBDA
I
________
V1 1.000
V2 1.000
V3 1.000
V4 1.000
V5 1.000
V6 1.000
V7 1.000
V8 1.000
THETA
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
V1 0.469
V2 0.000 0.484
V3 0.000 0.000 0.490
V4 0.000 0.000 0.000 0.514
V5 0.000 0.000 0.000 0.000 0.492
V6 0.000 0.000 0.000 0.000 0.000
V7 0.000 0.000 0.000 0.000 0.000
V8 0.000 0.000 0.000 0.000 0.000
THETA
V6 V7 V8
________ ________ ________
V6 0.504
V7 0.000 0.510
V8 0.000 0.000 0.528
ALPHA
I
________
1 0.000
BETA
I
________
I 0.000
PSI
I
________
I 0.107
STARTING VALUES FOR LATENT CLASS 2
NU
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
1 0.000 0.000 0.000 0.000 0.000
NU
V6 V7 V8
________ ________ ________
1 0.000 0.000 0.000
LAMBDA
I
________
V1 1.000
V2 1.000
V3 1.000
V4 1.000
V5 1.000
V6 1.000
V7 1.000
V8 1.000
THETA
V1 V2 V3 V4 V5
________ ________ ________ ________ ________
V1 0.469
V2 0.000 0.484
V3 0.000 0.000 0.490
V4 0.000 0.000 0.000 0.514
V5 0.000 0.000 0.000 0.000 0.492
V6 0.000 0.000 0.000 0.000 0.000
V7 0.000 0.000 0.000 0.000 0.000
V8 0.000 0.000 0.000 0.000 0.000
THETA
V6 V7 V8
________ ________ ________
V6 0.504
V7 0.000 0.510
V8 0.000 0.000 0.528
ALPHA
I
________
1 0.000
BETA
I
________
I 0.000
PSI
I
________
I 0.107
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
1 0.000 0.000
GAMMA(C)
I
________
C#1 0.000
C#2 0.000
Beginning Time: 09:53:45
Ending Time: 09:53:46
Elapsed Time: 00:00:01
MUTHEN & MUTHEN
3463 Stoner Ave.
Los Angeles, CA 90066
Tel: (310) 391-9971
Fax: (310) 391-8971
Web: www.StatModel.com
Support: Support@StatModel.com
Copyright (c) 1998-2013 Muthen & Muthen
Raw
mixturetest.R
library(MASS)
d <- mvrnorm(1000,1:8,diag(8))
setwd("~/Documents/play")
prepareMplusData(df=as.data.frame(d),filename="mixturetest.dat")
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