Logistic Regression Diagnostics

This tutorial describes the implementation of tools for the diagnostic and the assessment of a logistic regression. These tools are available in Tanagra version 1.4.33 (and later).

We deal with a credit scoring problem. We try to determine by using logistic regression the factors underlying the agreement or refusal of a credit to customers. We perform the following steps:
- Estimating the parameters of the classifier;
- Retrieving the covariance matrix of coefficients;
- Assessment using the Hosmer and Lemeshow goodness of fit test;
- Assessment using the reliability diagram;
- Assessment using the ROC curve;
- Analysis of residuals, detection of outliers and influential points.

On the one hand, we use Tanagra 1.4.33. Then, on the other hand, we perform the same analysis using the R 2.9.2 software [glm(.) procedure].

Keywords: logistic regression, residual analysis, outliers, influential points, pearson residual, deviance residual, leverage, cook's distance, dfbeta, dfbetas, hosmer-lemeshow goodness of fit test, reliability diagram, calibration plot, glm()
Components: BINARY LOGISTIC REGRESSION, HOSMER LEMESHOW TEST, RELIABILITY DIAGRAM, LOGISTIC REGRESSION RESIDUALS
Tutorial: en_Tanagra_Logistic_Regression_Diagnostics.pdf
Dataset: logistic_regression_diagnostics.zip
References :
D. Garson, "Logistic Regression"
D. Hosmer, S. Lemeshow, « Applied Logistic Regression », John Wiley &Sons, Inc, Second Edition, 2000.