Mplus VERSION 7.3
MUTHEN & MUTHEN
09/22/2014   5:46 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a path analysis
  	with a combination of censored,
  	categorical, and unordered categorical
  	(nominal) dependent variables
  DATA:	FILE IS ex3.15.dat;
  VARIABLE:	NAMES ARE y1 u2 u1 x1-x3;
  	CENSORED IS y1 (a);
  	CATEGORICAL IS u1;
  	NOMINAL IS u2;
  MODEL:	y1 u1 ON x1 x2 x3;
  	u2 ON y1 u1 x2;



INPUT READING TERMINATED NORMALLY



this is an example of a path analysis
with a combination of censored,
categorical, and unordered categorical
(nominal) dependent variables

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                         500

Number of dependent variables                                    3
Number of independent variables                                  3
Number of continuous latent variables                            0

Observed dependent variables

  Censored
   Y1

  Binary and ordered categorical (ordinal)
   U1

  Unordered categorical (nominal)
   U2

Observed independent variables
   X1          X2          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
Link                                                         LOGIT
Cholesky                                                       OFF

Input data file(s)
  ex3.15.dat
Input data format  FREE


UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES

    U1
      Category 1    0.510      255.000
      Category 2    0.490      245.000
    U2
      Category 1    0.454      227.000
      Category 2    0.252      126.000
      Category 3    0.294      147.000


SUMMARY OF CENSORED LIMITS

      Y1                 1.000



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                       17

Loglikelihood

          H0 Value                       -1181.376
          H0 Scaling Correction Factor      1.0091
            for MLR

Information Criteria

          Akaike (AIC)                    2396.751
          Bayesian (BIC)                  2468.400
          Sample-Size Adjusted BIC        2414.440
            (n* = (n + 2) / 24)



MODEL RESULTS

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

 Y1         ON
    X1                -0.992      0.054    -18.311      0.000
    X2                 0.488      0.048     10.108      0.000
    X3                 0.920      0.049     18.807      0.000

 U1         ON
    X1                 1.031      0.121      8.520      0.000
    X2                -0.599      0.124     -4.826      0.000
    X3                -0.925      0.117     -7.938      0.000

 U2#1       ON
    Y1                 0.450      0.097      4.665      0.000
    U1                 1.503      0.258      5.833      0.000
    X2                -0.665      0.164     -4.043      0.000

 U2#2       ON
    Y1                -0.020      0.153     -0.132      0.895
    U1                -0.887      0.360     -2.466      0.014
    X2                 1.731      0.219      7.905      0.000

 Intercepts
    Y1                 0.417      0.054      7.786      0.000
    U2#1              -0.496      0.176     -2.825      0.005
    U2#2              -0.544      0.200     -2.714      0.007

 Thresholds
    U1$1               0.094      0.109      0.867      0.386

 Residual Variances
    Y1                 0.904      0.073     12.448      0.000


LOGISTIC REGRESSION ODDS RATIO RESULTS

 U1         ON
    X1                 2.803
    X2                 0.549
    X3                 0.396

 U2#1       ON
    Y1                 1.569
    U1                 4.495
    X2                 0.514

 U2#2       ON
    Y1                 0.980
    U1                 0.412
    X2                 5.644


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.100E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  17:46:24
        Ending Time:  17:46:24
       Elapsed Time:  00:00:00



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