Mplus VERSION 8
MUTHEN & MUTHEN
04/10/2017   4:39 AM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a multinomial
          logistic regression for an unordered
          categorical (nominal) dependent variable
          with two covariates
  DATA:	FILE IS ex3.6.dat;
  VARIABLE:	NAMES ARE u1 x1 x3;
  	NOMINAL IS u1;
  MODEL:  u1 ON x1 x3;



INPUT READING TERMINATED NORMALLY



this is an example of a multinomial
logistic regression for an unordered
categorical (nominal) dependent variable
with two covariates

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    1
Number of independent variables                                  2
Number of continuous latent variables                            0

Observed dependent variables

  Unordered categorical (nominal)
   U1

Observed independent variables
   X1          X3


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-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
Cholesky                                                       OFF

Input data file(s)
  ex3.6.dat
Input data format  FREE


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U1
      Category 1    0.242          121.000
      Category 2    0.368          184.000
      Category 3    0.390          195.000



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

     X1                    0.000      -0.035      -3.139    0.20%      -0.842     -0.239     -0.016
             500.000       1.041       0.091       3.252    0.20%       0.254      0.887
     X3                   -0.067      -0.060      -3.145    0.20%      -0.870     -0.304     -0.034
             500.000       0.960       0.073       2.857    0.20%       0.205      0.741


THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        6

Loglikelihood

          H0 Value                        -433.426
          H0 Scaling Correction Factor      1.0174
            for MLR

Information Criteria

          Akaike (AIC)                     878.853
          Bayesian (BIC)                   904.140
          Sample-Size Adjusted BIC         885.096
            (n* = (n + 2) / 24)



MODEL RESULTS

                                                    Two-Tailed
                    Estimate       S.E.  Est./S.E.    P-Value

 U1#1       ON
    X1                 0.769      0.165      4.669      0.000
    X3                 2.259      0.203     11.147      0.000

 U1#2       ON
    X1                 0.280      0.114      2.444      0.015
    X3                 0.885      0.143      6.200      0.000

 Intercepts
    U1#1              -0.749      0.158     -4.728      0.000
    U1#2               0.262      0.120      2.192      0.028


LOGISTIC REGRESSION ODDS RATIO RESULTS

 U1#1       ON
    X1                 2.157
    X3                 9.578

 U1#2       ON
    X1                 1.323
    X3                 2.423


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.849E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  04:39:40
        Ending Time:  04:39:40
       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) 1998-2017 Muthen & Muthen
