# Logistic regression

## Logistic regression from scratch (Newton Raphson and Fisher Scoring)

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. library(MLMusingR) data(suspend) m1 <- glm(sus ~ male + gpa * frpl + fight + frmp.c * pminor.c, data = suspend, family = binomial) ### extracting raw components dat <- model.frame(m1) fml <- formula(m1) X <- model.

## Logistic regression (from scratch) using matrices

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. What follows are some logistic regression notes (this is not on interpreting results). Even though I’ve written about how other alternatives might be simpler than logistic regression or that there are challenges when comparing coefficients across models, it is interesting to see how the procedure works.

## Comparing coefficients across logistic regression models

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. Take a simulated example where our true model data generating process is (to keep things simple, the intercept is zero and the parameters are both set to 1):