Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:09 PM
INPUT INSTRUCTIONS
TITLE: this is an example of a logistic regression for a categorical observed
dependent variable with two covariates
DATA: FILE IS ex3.5.dat;
VARIABLE: NAMES ARE u1 x1 x3;
CATEGORICAL IS u1;
ANALYSIS: ESTIMATOR = ML;
MODEL: u1 ON x1 x3;
INPUT READING TERMINATED NORMALLY
this is an example of a logistic regression for a categorical observed
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
Binary and ordered categorical (ordinal)
U1
Observed independent variables
X1 X3
Estimator ML
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
Link LOGIT
Cholesky OFF
Input data file(s)
ex3.5.dat
Input data format FREE
UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES
U1
Category 1 0.654 327.000
Category 2 0.346 173.000
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 0.001 -0.133 -3.145 0.20% -0.922 -0.235 0.023
500.000 1.094 -0.162 2.920 0.20% 0.304 0.876
X3 -0.042 -0.057 -3.139 0.20% -0.921 -0.353 -0.040
500.000 0.957 -0.357 2.875 0.20% 0.274 0.859
THE MODEL ESTIMATION TERMINATED NORMALLY
MODEL FIT INFORMATION
Number of Free Parameters 3
Loglikelihood
H0 Value -202.620
Information Criteria
Akaike (AIC) 411.240
Bayesian (BIC) 423.884
Sample-Size Adjusted BIC 414.362
(n* = (n + 2) / 24)
MODEL RESULTS
Two-Tailed
Estimate S.E. Est./S.E. P-Value
U1 ON
X1 1.072 0.143 7.502 0.000
X3 1.839 0.179 10.243 0.000
Thresholds
U1$1 1.026 0.137 7.492 0.000
QUALITY OF NUMERICAL RESULTS
Condition Number for the Information Matrix 0.314E+00
(ratio of smallest to largest eigenvalue)
RESULTS IN PROBABILITY SCALE
Estimate
U1
Category 1 0.654
Category 2 0.346
LOGISTIC REGRESSION ODDS RATIO RESULTS
95% C.I.
Estimate S.E. Lower 2.5% Upper 2.5%
U1 ON
X1 2.921 0.417 2.207 3.864
X3 6.288 1.129 4.423 8.939
Beginning Time: 23:09:22
Ending Time: 23:09:22
Elapsed Time: 00:00:00
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