Mplus VERSION 7.4
MUTHEN & MUTHEN
06/06/2016  10:24 AM

INPUT INSTRUCTIONS

  title: Hilbe page 112 example

  data:
      file = affairs1.dat;

  variable:
  names = id	male	age	yrsmarr	kids
  relig	educ	occup	ratemarr
  naffairs	affair	vryhap	hapavg
  avgmarr	unhap	vryrel	smerel	slghtrel
  notrel;

  usevar = naffairs kids vryhap	hapavg
  avgmarr vryrel	smerel	slghtrel notrel
  yrsmarr3 yrsmarr4 yrsmarr5 yrsmarr6;

  ! vryhap: very happily married
  ! hapavg: happily married
  ! avgmarr: avg marriage
  ! vryrel: very religious
  ! smerel: somwhat religious
  ! slghtrel: slighly religious
  ! notrel: not religious

  count = naffairs(p);

  define:
  if (yrsmarr==4) then yrsmarr3=1 else yrsmarr3=0;
  if (yrsmarr==7) then yrsmarr4=1 else yrsmarr4=0;
  if (yrsmarr==10) then yrsmarr5=1 else yrsmarr5=0;
  if (yrsmarr==15) then yrsmarr6=1 else yrsmarr6=0;


  model:
      naffairs on  kids-yrsmarr6;
      f by naffairs; f;

  analysis:
      estimator=ml;

  plot:
      type = plot3;



INPUT READING TERMINATED NORMALLY



Hilbe page 112 example

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         601

Number of dependent variables                                    1
Number of independent variables                                 12
Number of continuous latent variables                            1

Observed dependent variables

  Count
   NAFFAIRS

Observed independent variables
   KIDS        VRYHAP      HAPAVG      AVGMARR     VRYREL      SMEREL
   SLGHTREL    NOTREL      YRSMARR3    YRSMARR4    YRSMARR5    YRSMARR6

Continuous latent variables
   F


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                            1
  Adaptive quadrature                                           ON
Cholesky                                                        ON

Input data file(s)
  affairs1.dat
Input data format  FREE


COUNT PROPORTION OF ZERO, MINIMUM AND MAXIMUM VALUES

      NAFFAIRS    0.750         0        12



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       14

Loglikelihood

          H0 Value                        -735.942

Information Criteria

          Akaike (AIC)                    1499.885
          Bayesian (BIC)                  1561.465
          Sample-Size Adjusted BIC        1517.019
            (n* = (n + 2) / 24)



MODEL RESULTS

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

 F        BY
    NAFFAIRS           1.000      0.000    999.000    999.000

 NAFFAIRS   ON
    KIDS               0.012      0.437      0.028      0.977
    VRYHAP            -2.574      0.455     -5.658      0.000
    HAPAVG            -1.784      0.419     -4.262      0.000
    AVGMARR           -1.531      0.489     -3.130      0.002
    VRYREL            -2.265      0.669     -3.383      0.001
    SMEREL            -2.396      0.553     -4.336      0.000
    SLGHTREL          -0.923      0.541     -1.707      0.088
    NOTREL            -1.265      0.538     -2.349      0.019
    YRSMARR3           1.214      0.549      2.210      0.027
    YRSMARR4           1.554      0.607      2.561      0.010
    YRSMARR5           2.104      0.620      3.395      0.001
    YRSMARR6           1.920      0.549      3.498      0.000

 Intercepts
    NAFFAIRS          -0.427      0.702     -0.608      0.543

 Variances
    F                  6.185      0.896      6.900      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.348E-02
       (ratio of smallest to largest eigenvalue)


SAMPLE STATISTICS FOR ESTIMATED FACTOR SCORES


     SAMPLE STATISTICS


           Means
              F             F_SE
              ________      ________
 1             -0.001         1.669


           Covariances
              F             F_SE
              ________      ________
 F              2.994
 F_SE          -0.898         0.406


           Correlations
              F             F_SE
              ________      ________
 F              1.000
 F_SE          -0.814         1.000


PLOT INFORMATION

The following plots are available:

  Histograms (sample values, estimated factor scores)
  Scatterplots (sample values, estimated factor scores)
  Sample proportions, estimated and conditional estimated probabilities
  Latent variable distribution plots

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\chapter6\ex6.2_part2.dgm

     Beginning Time:  10:24:26
        Ending Time:  10:24:27
       Elapsed Time:  00:00:01



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