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

INPUT INSTRUCTIONS

  TITLE:	this is an example of a logistic regression for a categorical observed
  	dependent variable with two covariates
  DATA:	FILE IS ex3.5.dat;
  VARIABLE:	NAMES ARE u1 x1 x3;
  		CATEGORICAL IS u1;
  ANALYSIS:	ESTIMATOR = ML;
  MODEL:	u1 ON x1 x3;



INPUT READING TERMINATED NORMALLY



this is an example of a logistic regression for a categorical observed
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

  Binary and ordered categorical (ordinal)
   U1

Observed independent variables
   X1          X3


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)
  ex3.5.dat
Input data format  FREE


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U1
      Category 1    0.654          327.000
      Category 2    0.346          173.000



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                    0.001      -0.133      -3.145    0.20%      -0.922     -0.235      0.023
             500.000       1.094      -0.162       2.920    0.20%       0.304      0.876
     X3                   -0.042      -0.057      -3.139    0.20%      -0.921     -0.353     -0.040
             500.000       0.957      -0.357       2.875    0.20%       0.274      0.859


THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        3

Loglikelihood

          H0 Value                        -202.620

Information Criteria

          Akaike (AIC)                     411.240
          Bayesian (BIC)                   423.884
          Sample-Size Adjusted BIC         414.362
            (n* = (n + 2) / 24)



MODEL RESULTS

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

 U1         ON
    X1                 1.072      0.143      7.502      0.000
    X3                 1.839      0.179     10.243      0.000

 Thresholds
    U1$1               1.026      0.137      7.492      0.000


LOGISTIC REGRESSION ODDS RATIO RESULTS

 U1         ON
    X1                 2.921
    X3                 6.288


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.314E+00
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  04:39:40
        Ending Time:  04:39:40
       Elapsed Time:  00:00:00



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