Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022  11:09 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a negative binomial
  		model for a count dependent variable with
  		two covariates
  DATA:		FILE IS ex3.8b.dat;
  VARIABLE:	NAMES ARE u1 x1 x3;
  		COUNT IS u1 (nb);
  MODEL:	u1 ON x1 x3;



INPUT READING TERMINATED NORMALLY



this is an example of a negative binomial
model for a count 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

  Count
   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)
  ex3.8b.dat
Input data format  FREE


COUNT PROPORTION OF ZERO, MINIMUM AND MAXIMUM VALUES

      U1          0.422         0       139



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                        4

Loglikelihood

          H0 Value                       -1087.414
          H0 Scaling Correction Factor      0.9158
            for MLR

Information Criteria

          Akaike (AIC)                    2182.829
          Bayesian (BIC)                  2199.687
          Sample-Size Adjusted BIC        2186.991
            (n* = (n + 2) / 24)



MODEL RESULTS

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

 U1         ON
    X1                 0.665      0.074      8.986      0.000
    X3                 0.256      0.074      3.482      0.000

 Intercepts
    U1                 1.121      0.074     15.103      0.000

 Dispersion
    U1                 2.388      0.200     11.930      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.127E+00
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  23:09:25
        Ending Time:  23:09:25
       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) 1998-2022 Muthen & Muthen

Back to examples