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;
analysis:
estimator = ml;
model:
u on ses events;
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 2
Number of continuous latent variables 0
Observed dependent variables
Binary and ordered categorical (ordinal)
U
Observed independent variables
SES EVENTS
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
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 5
Loglikelihood
H0 Value -49.549
Information Criteria
Akaike (AIC) 109.098
Bayesian (BIC) 117.542
Sample-Size Adjusted BIC 101.896
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
U ON
SES -1.111 0.611 -1.819 0.069
EVENTS 0.319 0.121 2.635 0.008
Thresholds
U$1 -0.282 0.642 -0.439 0.661
U$2 1.213 0.661 1.836 0.066
U$3 2.209 0.721 3.064 0.002
LOGISTIC REGRESSION ODDS RATIO RESULTS
U ON
SES 0.329
EVENTS 1.376
BRANT WALD TEST FOR PROPORTIONAL ODDS
Degrees of
Chi-Square Freedom P-Value
U
Overall test 0.963 4 0.915
SES 0.432 2 0.806
EVENTS 0.304 2 0.859
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.246E-02
(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\ex5.18.dgm
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Ending Time: 17:37:27
Elapsed Time: 00:00:00
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