Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 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
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 -0.035 -3.139 0.20% -0.842 -0.239 -0.016
500.000 1.041 0.091 3.252 0.20% 0.254 0.887
X3 -0.067 -0.060 -3.145 0.20% -0.870 -0.304 -0.034
500.000 0.960 0.073 2.857 0.20% 0.205 0.741
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: 23:09:21
Ending Time: 23:09:22
Elapsed Time: 00:00:01
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