Mplus VERSION 7.4
MUTHEN & MUTHEN
06/06/2016 5:37 PM
INPUT INSTRUCTIONS
title:
Ordered polytomous regression
Agresti's example p. 325
Mental impairment related to SES and life events
data:
file = impair.dat;
variable:
names = subject u ses events;
! u = well (0), mild (1), moderate (2), impaired (3)
idvariable = subject;
categorical = u;
usevariables = u ses events x1x2;
define:
x1x2 = ses*events;
analysis:
estimator = ml;
model:
u on ses events x1x2;
output:
sampstat;
plot:
type = plot3;
INPUT READING TERMINATED NORMALLY
Ordered polytomous regression
Agresti's example p. 325
Mental impairment related to SES and life events
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 40
Number of dependent variables 1
Number of independent variables 3
Number of continuous latent variables 0
Observed dependent variables
Binary and ordered categorical (ordinal)
U
Observed independent variables
SES EVENTS X1X2
Variables with special functions
ID variable SUBJECT
Estimator ML
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
Optimization algorithm EMA
Integration Specifications
Type STANDARD
Number of integration points 15
Dimensions of numerical integration 0
Adaptive quadrature ON
Link LOGIT
Cholesky OFF
Input data file(s)
impair.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U
Category 1 0.300 12.000
Category 2 0.300 12.000
Category 3 0.175 7.000
Category 4 0.225 9.000
SAMPLE STATISTICS
SAMPLE STATISTICS
Means
SES EVENTS X1X2
________ ________ ________
1 0.550 4.275 2.525
Covariances
SES EVENTS X1X2
________ ________ ________
SES 0.247
EVENTS 0.174 7.299
X1X2 1.136 4.831 9.249
Correlations
SES EVENTS X1X2
________ ________ ________
SES 1.000
EVENTS 0.129 1.000
X1X2 0.751 0.588 1.000
UNIVARIATE SAMPLE STATISTICS
UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS
Variable/ Mean/ Skewness/ Minimum/ % with Percentiles
Sample Size Variance Kurtosis Maximum Min/Max 20%/60% 40%/80% Median
SES 0.550 -0.201 0.000 45.00% 0.000 0.000 1.000
40.000 0.247 -1.960 1.000 55.00% 1.000 1.000
EVENTS 4.275 0.362 0.000 5.00% 2.000 3.000 4.000
40.000 7.299 -0.999 9.000 10.00% 4.000 7.000
X1X2 2.525 0.871 0.000 47.50% 0.000 0.000 1.000
40.000 9.249 -0.654 9.000 5.00% 3.000 5.000
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 6
Loglikelihood
H0 Value -49.252
Information Criteria
Akaike (AIC) 110.504
Bayesian (BIC) 120.638
Sample-Size Adjusted BIC 101.862
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
U ON
SES -0.371 1.136 -0.326 0.744
EVENTS 0.420 0.186 2.255 0.024
X1X2 -0.181 0.238 -0.761 0.447
Thresholds
U$1 0.098 0.819 0.120 0.905
U$2 1.592 0.840 1.897 0.058
U$3 2.607 0.904 2.885 0.004
LOGISTIC REGRESSION ODDS RATIO RESULTS
U ON
SES 0.690
EVENTS 1.523
X1X2 0.834
BRANT WALD TEST FOR PROPORTIONAL ODDS
Degrees of
Chi-Square Freedom P-Value
U
Overall test 1.672 6 0.947
SES 0.629 2 0.730
EVENTS 0.701 2 0.704
X1X2 0.551 2 0.759
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.608E-03
(ratio of smallest to largest eigenvalue)
PLOT INFORMATION
The following plots are available:
Histograms (sample values, estimated values, residuals)
Scatterplots (sample values, estimated values, residuals)
Sample proportions, estimated and conditional estimated probabilities
DIAGRAM INFORMATION
Use View Diagram under the Diagram menu in the Mplus Editor to view the diagram.
If running Mplus from the Mplus Diagrammer, the diagram opens automatically.
Diagram output
c:\users\gryphon\desktop\chapter5\ex5.20.dgm
Beginning Time: 17:37:30
Ending Time: 17:37:30
Elapsed Time: 00:00:00
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