Logistic Regression

In addition to the Summary, there are four sections in this chapter. Section 7.1 introduces logistic regression (LR), including the logistic model, estimating the coefficients of a logistic model, making predictions using the parameters from estimates of a logistic model, and regularized LR. Section 7.2 introduces and compares two regularized LR algorithms, Least Absolute Shrinkage and Selection Operator (LASSO) and ridge LR models, and illustrates their uses via the glmnet package. Section 7.3 uses the microbiomeMarker package to implement LR through supervised learning to identify the microbiome biomarkers. Section 7.4 provides some remarks on LR and regularized LR algorithms.