Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 10:24 PM
INPUT INSTRUCTIONS
title: this is an example of a Monte Carlo simulation of
Cox mixture regression
montecarlo: names are t u1-u5 x;
generate=t(s 10*1) u1-u5(1);
hazardc = t (1);
survival=t(all);
categorical=u1-u5;
nobs=1000;
class=c(2);
genclass=c(2);
nrep=1;
save = ex8.17.dat;
model population:
%overall%
x*1;
t on x*0.3;
c#1 on x*1.0;
%c#1%
[t#1-t#11*1];
[u1$1-u5$1*1];
t on x*0.3;
%c#2%
[t#1-t#11*0.5];
[u1$1-u5$1*-1];
t on x*0.8;
analysis: type=mixture;
model:
%overall%
t on x*0.3;
c#1 on x*1.0;
%c#1%
[u1$1-u5$1*1];
t on x*0.3;
%c#2%
[u1$1-u5$1*-1];
t on x*0.8;
output: tech8;
INPUT READING TERMINATED NORMALLY
this is an example of a Monte Carlo simulation of
Cox mixture regression
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1000
Number of replications
Requested 1
Completed 1
Value of seed 0
Number of dependent variables 6
Number of independent variables 1
Number of continuous latent variables 0
Number of categorical latent variables 1
Observed dependent variables
Binary and ordered categorical (ordinal)
U1 U2 U3 U4 U5
Time-to-event (survival)
Non-parametric
T
Observed independent variables
X
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-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
Link LOGIT
Base Hazard EQUAL ACROSS CLASSES
SAMPLE STATISTICS FOR THE FIRST REPLICATION
SAMPLE STATISTICS
Means
X
________
-0.001
Covariances
X
________
X 0.996
Correlations
X
________
X 1.000
MODEL FIT INFORMATION
Number of Free Parameters 15
Loglikelihood
H0 Value
Mean -3025.790
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 -3025.790 -3025.790
0.980 0.000 -3025.790 -3025.790
0.950 0.000 -3025.790 -3025.790
0.900 0.000 -3025.790 -3025.790
0.800 0.000 -3025.790 -3025.790
0.700 0.000 -3025.790 -3025.790
0.500 0.000 -3025.790 -3025.790
0.300 0.000 -3025.790 -3025.790
0.200 0.000 -3025.790 -3025.790
0.100 0.000 -3025.790 -3025.790
0.050 0.000 -3025.790 -3025.790
0.020 0.000 -3025.790 -3025.790
0.010 0.000 -3025.790 -3025.790
Information Criteria
Akaike (AIC)
Mean 6081.579
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6081.579 6081.579
0.980 0.000 6081.579 6081.579
0.950 0.000 6081.579 6081.579
0.900 0.000 6081.579 6081.579
0.800 0.000 6081.579 6081.579
0.700 0.000 6081.579 6081.579
0.500 0.000 6081.579 6081.579
0.300 0.000 6081.579 6081.579
0.200 0.000 6081.579 6081.579
0.100 0.000 6081.579 6081.579
0.050 0.000 6081.579 6081.579
0.020 0.000 6081.579 6081.579
0.010 0.000 6081.579 6081.579
Bayesian (BIC)
Mean 6155.195
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6155.195 6155.195
0.980 0.000 6155.195 6155.195
0.950 0.000 6155.195 6155.195
0.900 0.000 6155.195 6155.195
0.800 0.000 6155.195 6155.195
0.700 0.000 6155.195 6155.195
0.500 0.000 6155.195 6155.195
0.300 0.000 6155.195 6155.195
0.200 0.000 6155.195 6155.195
0.100 0.000 6155.195 6155.195
0.050 0.000 6155.195 6155.195
0.020 0.000 6155.195 6155.195
0.010 0.000 6155.195 6155.195
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 6107.555
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 6107.555 6107.555
0.980 0.000 6107.555 6107.555
0.950 0.000 6107.555 6107.555
0.900 0.000 6107.555 6107.555
0.800 0.000 6107.555 6107.555
0.700 0.000 6107.555 6107.555
0.500 0.000 6107.555 6107.555
0.300 0.000 6107.555 6107.555
0.200 0.000 6107.555 6107.555
0.100 0.000 6107.555 6107.555
0.050 0.000 6107.555 6107.555
0.020 0.000 6107.555 6107.555
0.010 0.000 6107.555 6107.555
Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes
Pearson Chi-Square
Mean 22.661
Std Dev 0.000
Degrees of freedom 20
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 1.000 8.260 22.661
0.980 1.000 9.237 22.661
0.950 1.000 10.851 22.661
0.900 1.000 12.443 22.661
0.800 1.000 14.578 22.661
0.700 1.000 16.266 22.661
0.500 1.000 19.337 22.661
0.300 0.000 22.775 22.661
0.200 0.000 25.038 22.661
0.100 0.000 28.412 22.661
0.050 0.000 31.410 22.661
0.020 0.000 35.020 22.661
0.010 0.000 37.566 22.661
Likelihood Ratio Chi-Square
Mean 22.148
Std Dev 0.000
Degrees of freedom 20
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 1.000 8.260 22.148
0.980 1.000 9.237 22.148
0.950 1.000 10.851 22.148
0.900 1.000 12.443 22.148
0.800 1.000 14.578 22.148
0.700 1.000 16.266 22.148
0.500 1.000 19.337 22.148
0.300 0.000 22.775 22.148
0.200 0.000 25.038 22.148
0.100 0.000 28.412 22.148
0.050 0.000 31.410 22.148
0.020 0.000 35.020 22.148
0.010 0.000 37.566 22.148
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 494.25355 0.49425
2 505.74645 0.50575
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 494.25355 0.49425
2 505.74645 0.50575
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 497 0.49700
2 503 0.50300
CLASSIFICATION QUALITY
Entropy 0.647
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.895 0.105
2 0.099 0.901
Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 0.900 0.100
2 0.104 0.896
Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 2.192 0.000
2 -2.158 0.000
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Latent Class 1
T ON
X 0.300 0.4018 0.0000 0.0772 0.0104 1.000 1.000
Intercepts
T 0.000 0.7927 0.0000 0.1468 0.6283 0.000 1.000
Thresholds
U1$1 1.000 0.9291 0.0000 0.1257 0.0050 1.000 1.000
U2$1 1.000 1.0646 0.0000 0.1285 0.0042 1.000 1.000
U3$1 1.000 1.0441 0.0000 0.1240 0.0019 1.000 1.000
U4$1 1.000 1.1333 0.0000 0.1332 0.0178 1.000 1.000
U5$1 1.000 1.0796 0.0000 0.1278 0.0063 1.000 1.000
Latent Class 2
T ON
X 0.800 0.6621 0.0000 0.1100 0.0190 1.000 1.000
Intercepts
T 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Thresholds
U1$1 -1.000 -0.9735 0.0000 0.1170 0.0007 1.000 1.000
U2$1 -1.000 -1.0770 0.0000 0.1302 0.0059 1.000 1.000
U3$1 -1.000 -1.0777 0.0000 0.1305 0.0060 1.000 1.000
U4$1 -1.000 -0.9591 0.0000 0.1223 0.0017 1.000 1.000
U5$1 -1.000 -0.8914 0.0000 0.1193 0.0118 1.000 1.000
Categorical Latent Variables
C#1 ON
X 1.000 1.1296 0.0000 0.1124 0.0168 1.000 1.000
Intercepts
C#1 0.000 -0.0245 0.0000 0.1193 0.0006 1.000 0.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.998E-01
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
X
________
0
LAMBDA
X
________
X 0
THETA
X
________
X 0
ALPHA
X
________
0
BETA
X
________
X 0
PSI
X
________
X 0
PARAMETER SPECIFICATION FOR LATENT CLASS 2
NU
X
________
0
LAMBDA
X
________
X 0
THETA
X
________
X 0
ALPHA
X
________
0
BETA
X
________
X 0
PSI
X
________
X 0
PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U1$1 U2$1 U3$1 U4$1 U5$1
________ ________ ________ ________ ________
1 2 3 4 5
TAU(U) FOR LATENT CLASS 2
U1$1 U2$1 U3$1 U4$1 U5$1
________ ________ ________ ________ ________
6 7 8 9 10
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
11 0
GAMMA(C)
X
________
C#1 12
C#2 0
PARAMETER SPECIFICATION FOR THE CENSORED/NOMINAL/COUNT MODEL PART
NU(P) FOR LATENT CLASS 1
T#1 T
________ ________
0 13
KAPPA(P) FOR LATENT CLASS 1
X
________
T#1 0
T 14
NU(P) FOR LATENT CLASS 2
T#1 T
________ ________
0 0
KAPPA(P) FOR LATENT CLASS 2
X
________
T#1 0
T 15
STARTING VALUES FOR LATENT CLASS 1
NU
X
________
0.000
LAMBDA
X
________
X 1.000
THETA
X
________
X 0.000
ALPHA
X
________
0.000
BETA
X
________
X 0.000
PSI
X
________
X 0.500
STARTING VALUES FOR LATENT CLASS 2
NU
X
________
0.000
LAMBDA
X
________
X 1.000
THETA
X
________
X 0.000
ALPHA
X
________
0.000
BETA
X
________
X 0.000
PSI
X
________
X 0.500
STARTING VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U1$1 U2$1 U3$1 U4$1 U5$1
________ ________ ________ ________ ________
1.000 1.000 1.000 1.000 1.000
TAU(U) FOR LATENT CLASS 2
U1$1 U2$1 U3$1 U4$1 U5$1
________ ________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000 -1.000
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
GAMMA(C)
X
________
C#1 1.000
C#2 0.000
STARTING VALUES FOR THE CENSORED/NOMINAL/COUNT MODEL PART
NU(P) FOR LATENT CLASS 1
T#1 T
________ ________
-20.000 0.000
KAPPA(P) FOR LATENT CLASS 1
X
________
T#1 0.000
T 0.300
NU(P) FOR LATENT CLASS 2
T#1 T
________ ________
-20.000 0.000
KAPPA(P) FOR LATENT CLASS 2
X
________
T#1 0.000
T 0.800
POPULATION VALUES FOR LATENT CLASS 1
NU
X
________
0.000
LAMBDA
X
________
X 1.000
THETA
X
________
X 0.000
ALPHA
X
________
0.000
BETA
X
________
X 0.000
PSI
X
________
X 1.000
POPULATION VALUES FOR LATENT CLASS 2
NU
X
________
0.000
LAMBDA
X
________
X 1.000
THETA
X
________
X 0.000
ALPHA
X
________
0.000
BETA
X
________
X 0.000
PSI
X
________
X 1.000
POPULATION VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U1$1 U2$1 U3$1 U4$1 U5$1
________ ________ ________ ________ ________
1.000 1.000 1.000 1.000 1.000
TAU(U) FOR LATENT CLASS 2
U1$1 U2$1 U3$1 U4$1 U5$1
________ ________ ________ ________ ________
-1.000 -1.000 -1.000 -1.000 -1.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
GAMMA(C)
X
________
C#1 1.000
C#2 0.000
POPULATION VALUES FOR THE CENSORED/NOMINAL/COUNT MODEL PART
NU(P) FOR LATENT CLASS 1
T#1 T
________ ________
-20.000 0.000
KAPPA(P) FOR LATENT CLASS 1
X
________
T#1 0.000
T 0.300
NU(P) FOR LATENT CLASS 2
T#1 T
________ ________
-20.000 0.000
KAPPA(P) FOR LATENT CLASS 2
X
________
T#1 0.000
T 0.800
POPULATION VALUES FOR THE BASE HAZARD PARAMETERS
BASE HAZARD PARAMETERS FOR LATENT CLASS 1
T#1 T#2 T#3 T#4 T#5
________ ________ ________ ________ ________
1.000 1.000 1.000 1.000 1.000
BASE HAZARD PARAMETERS FOR LATENT CLASS 1
T#6 T#7 T#8 T#9 T#10
________ ________ ________ ________ ________
1.000 1.000 1.000 1.000 1.000
BASE HAZARD PARAMETERS FOR LATENT CLASS 1
T#11
________
1.000
BASE HAZARD PARAMETERS FOR LATENT CLASS 2
T#1 T#2 T#3 T#4 T#5
________ ________ ________ ________ ________
0.500 0.500 0.500 0.500 0.500
BASE HAZARD PARAMETERS FOR LATENT CLASS 2
T#6 T#7 T#8 T#9 T#10
________ ________ ________ ________ ________
0.500 0.500 0.500 0.500 0.500
BASE HAZARD PARAMETERS FOR LATENT CLASS 2
T#11
________
0.500
TECHNICAL 8 OUTPUT
TECHNICAL 8 OUTPUT FOR REPLICATION 1
E STEP ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE ALGORITHM
1 -0.30567642D+04 0.0000000 0.0000000 EM
2 -0.30291468D+04 27.6173795 0.0090348 EM
3 -0.30263359D+04 2.8109081 0.0009280 EM
4 -0.30259087D+04 0.4271786 0.0001412 EM
5 -0.30258273D+04 0.0814716 0.0000269 EM
6 -0.30258065D+04 0.0207237 0.0000068 EM
7 -0.30257992D+04 0.0073266 0.0000024 EM
8 -0.30257957D+04 0.0035168 0.0000012 EM
9 -0.30257936D+04 0.0020606 0.0000007 EM
10 -0.30257923D+04 0.0013272 0.0000004 EM
11 -0.30257914D+04 0.0008876 0.0000003 EM
12 -0.30257908D+04 0.0006018 0.0000002 EM
13 -0.30257904D+04 0.0004101 0.0000001 EM
14 -0.30257901D+04 0.0002799 0.0000001 EM
15 -0.30257899D+04 0.0001912 0.0000001 EM
16 -0.30257898D+04 0.0001306 0.0000000 EM
17 -0.30257897D+04 0.0000893 0.0000000 EM
18 -0.30257897D+04 0.0000610 0.0000000 EM
19 -0.30257896D+04 0.0000417 0.0000000 EM
20 -0.30257896D+04 0.0000285 0.0000000 EM
21 -0.30257896D+04 0.0000195 0.0000000 EM
22 -0.30257896D+04 0.0000133 0.0000000 EM
23 -0.30257895D+04 0.0000091 0.0000000 EM
24 -0.30257895D+04 0.0000062 0.0000000 EM
25 -0.30257895D+04 0.0000042 0.0000000 EM
26 -0.30257895D+04 0.0000029 0.0000000 EM
27 -0.30257895D+04 0.0000020 0.0000000 EM
28 -0.30257895D+04 0.0000014 0.0000000 EM
29 -0.30257895D+04 0.0000009 0.0000000 EM
30 -0.30257895D+04 0.0000006 0.0000000 EM
31 -0.30257895D+04 0.0000004 0.0000000 EM
32 -0.30257895D+04 0.0000003 0.0000000 EM
33 -0.30257895D+04 0.0000002 0.0000000 EM
34 -0.30257895D+04 0.0000001 0.0000000 EM
35 -0.30257895D+04 0.0000001 0.0000000 EM
36 -0.30257895D+04 0.0000001 0.0000000 EM
37 -0.30257895D+04 0.0000000 0.0000000 EM
38 -0.30257895D+04 0.0000000 0.0000000 EM
39 -0.30257895D+04 0.0000000 0.0000000 EM
40 -0.30257895D+04 0.0000000 0.0000000 FS
SAVEDATA INFORMATION
Order of variables
T
U1
U2
U3
U4
U5
X
_TCENT
C
Save file
ex8.17.dat
Save file format Free
Save file record length 10000
Beginning Time: 22:24:52
Ending Time: 22:24:53
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) 1998-2022 Muthen & Muthen
Back to examples