Jonathan Bartlett

The robust sandwich variance estimator for linear regression (using R)

May 10, 2014February 14, 2014 by Jonathan Bartlett

In a previous post we looked at the (robust) sandwich variance estimator for linear regression. This method allowed us to estimate valid standard errors for our coefficients in linear regression, without requiring the usual assumption that the residual errors have constant variance.

In this post we’ll look at how this can be done in practice using R, with the sandwich package (I’ll assume below that you’ve installed this library). To illustrate, we’ll first simulate some simple data from a linear regression model where the residual variance increases sharply with the covariate:

Wald vs likelihood ratio test

June 28, 2016February 8, 2014 by Jonathan Bartlett

When taking a course on likelihood based inference, one of the key topics is that of testing and confidence interval construction based on the likelihood function. Usually the Wald, likelihood ratio, and score tests are covered. In this post I’m going to revise the advantages and disadvantages of the Wald and likelihood ratio test. I will focus on confidence intervals rather than tests, because the deficiencies of the Wald approach are more transparently seen here.

R squared in logistic regression

February 28, 2020February 8, 2014 by Jonathan Bartlett

In previous posts I’ve looked at R squared in linear regression, and argued that I think it is more appropriate to think of it is a measure of explained variation, rather than goodness of fit.