Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a negative binomial
model for a count dependent variable with
two covariates
DATA: FILE IS ex3.8b.dat;
VARIABLE: NAMES ARE u1 x1 x3;
COUNT IS u1 (nb);
MODEL: u1 ON x1 x3;
INPUT READING TERMINATED NORMALLY
this is an example of a negative binomial
model for a count 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
Count
U1
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)
ex3.8b.dat
Input data format FREE
COUNT PROPORTION OF ZERO, MINIMUM AND MAXIMUM VALUES
U1 0.422 0 139
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 4
Loglikelihood
H0 Value -1087.414
H0 Scaling Correction Factor 0.9158
for MLR
Information Criteria
Akaike (AIC) 2182.829
Bayesian (BIC) 2199.687
Sample-Size Adjusted BIC 2186.991
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
U1 ON
X1 0.665 0.074 8.986 0.000
X3 0.256 0.074 3.482 0.000
Intercepts
U1 1.121 0.074 15.103 0.000
Dispersion
U1 2.388 0.200 11.930 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.127E+00
(ratio of smallest to largest eigenvalue)
Beginning Time: 23:09:25
Ending Time: 23:09:25
Elapsed Time: 00:00:00
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