Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:12 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a LCA with binary,
censored, unordered, and count latent
class indicators using user-specified
starting values without random starts
DATA: FILE IS ex7.11.dat;
VARIABLE: NAMES ARE u3 y1 u2 u1 c;
USEVARIABLES ARE u3-u1;
CLASSES = c (2);
CATEGORICAL = u1;
CENSORED = y1 (b);
NOMINAL = u2;
COUNT = u3 (i);
ANALYSIS: TYPE = MIXTURE;
STARTS = 0;
MODEL:
%OVERALL%
%c#1%
[u1$1*-1 y1*3 u2#1*0 u2#2*1 u3*.5 u3#1*1.5];
y1*2;
%c#2%
[u1$1*0 y1*1 u2#1*-1 u2#2*0 u3*1 u3#1*1];
y1*1;
OUTPUT: TECH1 TECH8;
INPUT READING TERMINATED NORMALLY
this is an example of a LCA with binary,
censored, unordered, and count latent
class indicators using user-specified
starting values without random starts
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1000
Number of dependent variables 4
Number of independent variables 0
Number of continuous latent variables 0
Number of categorical latent variables 1
Observed dependent variables
Censored
Y1
Binary and ordered categorical (ordinal)
U1
Unordered categorical (nominal)
U2
Count
U3
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
Input data file(s)
ex7.11.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U1
Category 1 0.383 383.000
Category 2 0.617 617.000
U2
Category 1 0.203 203.000
Category 2 0.513 513.000
Category 3 0.284 284.000
SUMMARY OF CENSORED LIMITS
Y1 0.000
COUNT PROPORTION OF ZERO, MINIMUM AND MAXIMUM VALUES
U3 0.796 0 7
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 15
Loglikelihood
H0 Value -4348.434
H0 Scaling Correction Factor 0.9935
for MLR
Information Criteria
Akaike (AIC) 8726.869
Bayesian (BIC) 8800.485
Sample-Size Adjusted BIC 8752.844
(n* = (n + 2) / 24)
Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes
Pearson Chi-Square
Value 0.000
Degrees of freedom cannot be computed for this model part.
Likelihood Ratio Chi-Square
Value 0.000
Degrees of freedom cannot be computed for this model part.
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 621.01529 0.62102
2 378.98471 0.37898
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 621.01529 0.62102
2 378.98471 0.37898
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 582 0.58200
2 418 0.41800
CLASSIFICATION QUALITY
Entropy 0.369
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 0.843 0.157
2 0.312 0.688
Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 0.790 0.210
2 0.241 0.759
Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 1.327 0.000
2 -1.149 0.000
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
Means
U3#1 1.505 0.163 9.245 0.000
U3 0.726 0.103 7.066 0.000
Y1 2.735 0.207 13.243 0.000
U2#1 -0.006 0.163 -0.037 0.971
U2#2 0.960 0.145 6.639 0.000
Thresholds
U1$1 -0.756 0.136 -5.545 0.000
Variances
Y1 2.332 0.317 7.349 0.000
Latent Class 2
Means
U3#1 0.826 0.180 4.584 0.000
U3 0.897 0.075 11.978 0.000
Y1 0.939 0.141 6.674 0.000
U2#1 -0.772 0.241 -3.197 0.001
U2#2 0.076 0.208 0.365 0.715
Thresholds
U1$1 -0.053 0.180 -0.292 0.770
Variances
Y1 0.991 0.213 4.651 0.000
Categorical Latent Variables
Means
C#1 0.494 0.344 1.435 0.151
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.481E-02
(ratio of smallest to largest eigenvalue)
RESULTS IN PROBABILITY SCALE
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Latent Class 1
U1
Category 1 0.320 0.030 10.787 0.000
Category 2 0.680 0.030 22.964 0.000
Latent Class 2
U1
Category 1 0.487 0.045 10.843 0.000
Category 2 0.513 0.045 11.428 0.000
LATENT CLASS INDICATOR ODDS RATIOS FOR THE LATENT CLASSES
95% C.I.
Estimate S.E. Lower 2.5% Upper 2.5%
Latent Class 1 Compared to Latent Class 2
U1
Category > 1 2.020 0.528 1.210 3.370
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
PARAMETER SPECIFICATION FOR LATENT CLASS 2
PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U1$1
________
1
TAU(U) FOR LATENT CLASS 2
U1$1
________
2
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
3 0
PARAMETER SPECIFICATION FOR THE CENSORED/NOMINAL/COUNT MODEL PART
NU(P) FOR LATENT CLASS 1
U3#1 U3 Y1#1 Y1 U2#1
________ ________ ________ ________ ________
4 5 0 6 7
NU(P) FOR LATENT CLASS 1
U2#2
________
8
THETA(C) FOR CLASS LATENT CLASS 1
Y1
________
9
NU(P) FOR LATENT CLASS 2
U3#1 U3 Y1#1 Y1 U2#1
________ ________ ________ ________ ________
10 11 0 12 13
NU(P) FOR LATENT CLASS 2
U2#2
________
14
THETA(C) FOR CLASS LATENT CLASS 2
Y1
________
15
STARTING VALUES FOR LATENT CLASS 1
STARTING VALUES FOR LATENT CLASS 2
STARTING VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U1$1
________
-1.000
TAU(U) FOR LATENT CLASS 2
U1$1
________
0.000
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
STARTING VALUES FOR THE CENSORED/NOMINAL/COUNT MODEL PART
NU(P) FOR LATENT CLASS 1
U3#1 U3 Y1#1 Y1 U2#1
________ ________ ________ ________ ________
1.500 0.500 -20.000 3.000 0.000
NU(P) FOR LATENT CLASS 1
U2#2
________
1.000
THETA(C) FOR LATENT CLASS 1
Y1
________
2.000
NU(P) FOR LATENT CLASS 2
U3#1 U3 Y1#1 Y1 U2#1
________ ________ ________ ________ ________
1.000 1.000 -20.000 1.000 -1.000
NU(P) FOR LATENT CLASS 2
U2#2
________
0.000
THETA(C) FOR LATENT CLASS 2
Y1
________
1.000
TECHNICAL 8 OUTPUT
E STEP ITER LOGLIKELIHOOD ABS CHANGE REL CHANGE ALGORITHM
1 -0.43579548D+04 0.0000000 0.0000000 EM
2 -0.43501136D+04 7.8411567 0.0017993 EM
3 -0.43491950D+04 0.9186629 0.0002112 EM
4 -0.43489540D+04 0.2409893 0.0000554 EM
5 -0.43488802D+04 0.0737856 0.0000170 EM
6 -0.43488517D+04 0.0284651 0.0000065 EM
7 -0.43488364D+04 0.0153532 0.0000035 EM
8 -0.43488251D+04 0.0112674 0.0000026 EM
9 -0.43488153D+04 0.0098209 0.0000023 EM
10 -0.43488061D+04 0.0091792 0.0000021 EM
11 -0.43487973D+04 0.0087980 0.0000020 EM
12 -0.43487888D+04 0.0085104 0.0000020 EM
13 -0.43487805D+04 0.0082631 0.0000019 EM
14 -0.43487725D+04 0.0080376 0.0000018 EM
15 -0.43487647D+04 0.0078270 0.0000018 EM
16 -0.43487570D+04 0.0076277 0.0000018 EM
17 -0.43487496D+04 0.0074381 0.0000017 EM
18 -0.43487423D+04 0.0072566 0.0000017 EM
19 -0.43487353D+04 0.0070825 0.0000016 EM
20 -0.43487283D+04 0.0069148 0.0000016 EM
21 -0.43487216D+04 0.0067529 0.0000016 EM
22 -0.43487150D+04 0.0065963 0.0000015 EM
23 -0.43487085D+04 0.0064445 0.0000015 EM
24 -0.43487022D+04 0.0062972 0.0000014 EM
25 -0.43486961D+04 0.0061541 0.0000014 EM
26 -0.43486901D+04 0.0060148 0.0000014 EM
27 -0.43486842D+04 0.0058792 0.0000014 EM
28 -0.43486784D+04 0.0057471 0.0000013 EM
29 -0.43486728D+04 0.0056182 0.0000013 EM
30 -0.43486673D+04 0.0054925 0.0000013 EM
31 -0.43486620D+04 0.0053697 0.0000012 EM
32 -0.43486567D+04 0.0052499 0.0000012 EM
33 -0.43486516D+04 0.0051328 0.0000012 EM
34 -0.43486466D+04 0.0050184 0.0000012 EM
35 -0.43486417D+04 0.0049065 0.0000011 EM
36 -0.43486369D+04 0.0047972 0.0000011 EM
37 -0.43486322D+04 0.0046903 0.0000011 EM
38 -0.43486276D+04 0.0045858 0.0000011 EM
39 -0.43486231D+04 0.0044836 0.0000010 EM
40 -0.43486187D+04 0.0043836 0.0000010 EM
41 -0.43486144D+04 0.0042858 0.0000010 EM
42 -0.43486102D+04 0.0041902 0.0000010 EM
43 -0.43486061D+04 0.0040966 0.0000009 EM
44 -0.43486021D+04 0.0040051 0.0000009 EM
45 -0.43485982D+04 0.0039155 0.0000009 EM
46 -0.43485944D+04 0.0038279 0.0000009 EM
47 -0.43485907D+04 0.0037422 0.0000009 EM
48 -0.43485870D+04 0.0036583 0.0000008 EM
49 -0.43485834D+04 0.0035763 0.0000008 EM
50 -0.43485799D+04 0.0034960 0.0000008 EM
51 -0.43485765D+04 0.0034174 0.0000008 EM
52 -0.43485732D+04 0.0033406 0.0000008 EM
53 -0.43485699D+04 0.0032654 0.0000008 EM
54 -0.43485667D+04 0.0031918 0.0000007 EM
55 -0.43485636D+04 0.0031198 0.0000007 EM
56 -0.43485605D+04 0.0030494 0.0000007 EM
57 -0.43485576D+04 0.0029805 0.0000007 EM
58 -0.43485546D+04 0.0029131 0.0000007 EM
59 -0.43485518D+04 0.0028471 0.0000007 EM
60 -0.43484345D+04 0.1173271 0.0000270 QN
61 -0.43484345D+04 0.0000000 0.0000000 EM
Beginning Time: 23:12:55
Ending Time: 23:12:55
Elapsed Time: 00:00:00
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