Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a multinomial
logistic regression for an unordered
categorical (nominal) dependent variable
with two covariates
DATA: FILE IS ex3.6.dat;
VARIABLE: NAMES ARE u1 x1 x3;
NOMINAL IS u1;
MODEL: u1 ON x1 x3;
INPUT READING TERMINATED NORMALLY
this is an example of a multinomial
logistic regression for an unordered
categorical (nominal) 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
Unordered categorical (nominal)
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)
ex3.6.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U1
Category 1 0.242 121.000
Category 2 0.368 184.000
Category 3 0.390 195.000
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 6
Loglikelihood
H0 Value -433.426
H0 Scaling Correction Factor 1.0174
for MLR
Information Criteria
Akaike (AIC) 878.853
Bayesian (BIC) 904.140
Sample-Size Adjusted BIC 885.096
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
U1#1 ON
X1 0.769 0.165 4.669 0.000
X3 2.259 0.203 11.147 0.000
U1#2 ON
X1 0.280 0.114 2.444 0.015
X3 0.885 0.143 6.200 0.000
Intercepts
U1#1 -0.749 0.158 -4.728 0.000
U1#2 0.262 0.120 2.192 0.028
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.849E-01
(ratio of smallest to largest eigenvalue)
LOGISTIC REGRESSION ODDS RATIO RESULTS
95% C.I.
Estimate S.E. Lower 2.5% Upper 2.5%
U1#1 ON
X1 2.157 0.355 1.562 2.978
X3 9.578 1.941 6.438 14.249
U1#2 ON
X1 1.323 0.151 1.057 1.656
X3 2.423 0.346 1.832 3.206
Beginning Time: 23:09:23
Ending Time: 23:09:23
Elapsed Time: 00:00:00
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