Robust standard errors

In a recent article in Multivariate Behavioral Research, we (Huang, Wiedermann, and Zhang; HWZ; doi: 10.¹⁰⁸⁰⁄_00273171.2022.2077290) discuss a robust standard error that can be used with mixed models that accounts for violations of homogeneity. Note that these robust standard errors have been around for years though are not always provided in statistical software. These can also be computed using the CR2 package or the clubSandwich package. This page shows how to compute the traditional Liang and Zeger (1986) robust standard errors (CR0) and the CR2 estimator- see Bell and McCaffrey (2002) as well as McCaffrey, Bell, and Botts (2001) (BM and MBB).

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.

Illustration showing different flavors of robust standard errors. Load in library, dataset, and recode. Do not really need to dummy code but may make making the X matrix easier. Using the High School & Beyond (hsb) dataset. library(mlmRev) #has the hsb dataset ## Loading required package: lme4 ## Loading required package: Matrix library(summarytools) #for descriptives library(jtools) #for output library(dplyr) #for pipes and selecting ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(sandwich) #robust SEs library(lmtest) #for coeftest ## Loading required package: zoo ## ## Attaching package: 'zoo' ## The following objects are masked from 'package:base': ## ## as.