Table of Contents Logistic regression is a modeling technique that has attracted a lot of attention, especially from folks interested in classification, machine learning, and prediction using binary outcomes. One of the neat things about using R is that users can revisit commonly used procedures and figure out how they work.
Oct 27, 2023
In an earlier post, I had shown this using iteratively reweighted least squares (IRLS). This is just an alternative method using Newton Raphson and the Fisher scoring algorithm. For further details, you can look here as well.
Feb 15, 2022
Jan 1, 2022
Linear probability models and modified Poisson regression models are good alternatives.
Jan 1, 2021
ROUGH NOTES: [let me know if you spot any errors– there might be a couple!] Often, in randomized control trial where individuals are randomly assigned to treatment and control conditions, covariates are included to improve precision by reducing error and improving statistical power. However, when binary outcomes are used (e.g., patient recovers or not), there are several additional concerns that have gone unnoticed by many applied researchers.
Jun 28, 2020
Introduction Logistic regression is often used to analyze experiments with binary outcomes (e.g., pass vs fail) and binary predictors (e.g., treatment vs control). Although appropriate, there are other possible models that can be run that may provide easier to interpret results.
Oct 27, 2019