This article will cover the basic theory behind logistic regression, the types of logistic regression, when to use them and take you through a worked example.

Logistic regression Logistic regression is a statistical method used for modeling the relationship between a binary outcome variable and one or more exposure variables. Logistic regression is used ...

In a data set the size of ours, it is practical to apply the LASSO with standard logistic regression but not with the more computationally demanding logistic GLMM.

Eq. 3 implies a polychotomous logistic regression of the full n × J taxa count table. This is numerically difficult as the analysis of each taxon potentially requires all βj parameters. Instead, we ...

A new study investigated how logistic regression model training affects performance, and which features are best to include when examining datasets from individuals suffering from COVID-19.

For our purposes, we refer to unpenalized logistic regression as “standard logistic regression” and consider penalized logistic regression to be a machine learning algorithm. In penalized regression, ...

Here, we introduce prLogistic, an R package specifically built to assist estimation of PRs in cross-sectional studies via logistic regression models for analysis of both independent and correlated ...

Here, we introduce prLogistic, an R package specifically built to assist estimation of PRs in cross-sectional studies via logistic regression models for analysis of both independent and correlated ...