vcovHC.fastglm: Heteroskedasticity-consistent (HC) variance estimators for...

In fastglm: Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen'

Heteroskedasticity-consistent (HC) variance estimators for 'fastglm' objects

Description

Methods for 'sandwich::vcovHC()' on objects of class '"fastglm"' and '"fastglmFit"'. Load 'sandwich' ('library(sandwich)') before calling 'vcovHC(fit)'; otherwise no 'vcovHC' generic is in scope.

Usage

vcovHC.fastglm(object, type = c("HC3", "HC2", "HC1", "HC0"), ...)

vcovHC.fastglmFit(object, type = c("HC3", "HC2", "HC1", "HC0"), ...)

Arguments

object	a fitted object of class '"fastglm"' or '"fastglmFit"'.
type	one of '"HC0"', '"HC1"', '"HC2"', '"HC3"'. Default '"HC3"' matches 'sandwich::vcovHC.glm'.
...	not used.

Details

Computes the Eicker-Huber-White sandwich estimator 'bread ‘cov.unscaled') and 'meat = X’ diag(omega_i) X'. With 's_i = w_i^2 * r_i' the score contribution from observation 'i', the omegas are:

'HC0'

'omega_i = s_i^2'

'HC1'

'HC0' rescaled by 'n / (n - p)'

'HC2'

'omega_i = s_i^2 / (1 - h_i)'

'HC3'

'omega_i = s_i^2 / (1 - h_i)^2'

where 'r_i' is the working residual '(y - mu) / mu.eta(eta)', 'w_i^2 = prior.weight * mu.eta(eta)^2 / variance(mu)' is the IRLS working weight, and ‘h_i = w_i^2 * x_i’ (X' W X)^(-1) x_i' is the IRLS leverage. Equivalent to 'sandwich::vcovHC.glm'.

Requires the model matrix 'x' stored on the fitted object (set automatically by 'fastglm()', 'fastglmPure()', and 'fastglm_fit()' since version 0.0.6).

Value

A 'p x p' heteroskedasticity-consistent variance-covariance matrix.

Examples

if (requireNamespace("sandwich", quietly = TRUE)) {
  x <- cbind(1, matrix(rnorm(500 * 4), ncol = 4))
  y <- rbinom(500, 1, plogis(x %*% c(0.2, 0.3, -0.4, 0.1, 0.2)))
  fit <- fastglm(x, y, family = binomial())
  sandwich::vcovHC(fit)
  sandwich::vcovHC(fit, type = "HC0")
}

fastglm documentation built on Aug. 27, 2026, 9:07 a.m.