Mplus VERSION 8.7 (Mac) MUTHEN & MUTHEN 10/21/2021 10:45 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: 22:45:56 Ending Time: 22:45:56 Elapsed Time: 00:00:00 MUTHEN & MUTHEN 3463 Stoner Ave. Los Angeles, CA 90066 Tel: (310) 391-9971 Fax: (310) 391-8971 Web: www.StatModel.com Support: Support@StatModel.com Copyright (c) 1998-2021 Muthen & Muthen