Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:51 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a censored-inflated
          regression for a censored dependent
          variable with two covariates
  DATA:	FILE IS ex3.3.dat;
  VARIABLE:	NAMES ARE y1 x1 x3;
  	CENSORED ARE y1 (bi);
  MODEL:	y1 ON x1 x3;
  	y1#1 ON x1 x3;



INPUT READING TERMINATED NORMALLY



this is an example of a censored-inflated
regression for a censored 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

  Censored
   Y1

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)
  ex3.3.dat
Input data format  FREE


SUMMARY OF CENSORED LIMITS

      Y1                 0.000



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        7

Loglikelihood

          H0 Value                        -501.525
          H0 Scaling Correction Factor      0.9922
            for MLR

Information Criteria

          Akaike (AIC)                    1017.051
          Bayesian (BIC)                  1046.553
          Sample-Size Adjusted BIC        1024.335
            (n* = (n + 2) / 24)



MODEL RESULTS

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

 Y1         ON
    X1                 1.204      0.089     13.500      0.000
    X3                 0.595      0.093      6.425      0.000

 Y1#1       ON
    X1                 0.338      0.215      1.574      0.116
    X3                 1.183      0.240      4.935      0.000

 Intercepts
    Y1#1              -0.838      0.276     -3.043      0.002
    Y1                 0.664      0.118      5.633      0.000

 Residual Variances
    Y1                 1.156      0.155      7.448      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.211E-01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:51:47
        Ending Time:  22:51:47
       Elapsed Time:  00:00:00



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