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

     Beginning Time:  17:37:27
        Ending Time:  17:37:27
       Elapsed Time:  00:00:00



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) 1998-2015 Muthen & Muthen

Back to examples