Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a censored
regression for a censored dependent
variable with two covariates
DATA: FILE IS ex3.2.dat;
VARIABLE: NAMES ARE y1 x1 x3;
CENSORED ARE y1 (b);
ANALYSIS: ESTIMATOR = MLR;
MODEL: y1 ON x1 x3;
INPUT READING TERMINATED NORMALLY
this is an example of a censored
regression for a censored dependent
variable with two covariates
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 1000
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)
ex3.2.dat
Input data format FREE
SUMMARY OF CENSORED LIMITS
Y1 0.000
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 0.000 0.041 -3.266 0.10% -0.847 -0.253 -0.029
1000.000 1.028 0.156 3.468 0.10% 0.232 0.854
X3 -0.021 0.067 -3.145 0.10% -0.868 -0.274 -0.029
1000.000 1.003 -0.039 2.857 0.10% 0.222 0.838
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 4
Loglikelihood
H0 Value -1142.885
H0 Scaling Correction Factor 0.9863
for MLR
Information Criteria
Akaike (AIC) 2293.771
Bayesian (BIC) 2313.402
Sample-Size Adjusted BIC 2300.697
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
Y1 ON
X1 1.075 0.043 25.087 0.000
X3 0.495 0.037 13.337 0.000
Intercepts
Y1 0.515 0.040 12.803 0.000
Residual Variances
Y1 1.148 0.067 17.199 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.223E+00
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:09:20
Ending Time: 23:09:21
Elapsed Time: 00:00:01
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