sandcov: Compute Sandwich Variance-Covariance Estimates

Description

'sandcov' computes sandwich variance-covariance estimates for the coefficients of a fitted model. These estimates may be used to calculate robust standard error estimates.

Usage

sandcov(model, id)

Arguments

model

a fitted model of class '"lm"' or '"glm"'.

the numeric vector used to identify subjects, expanded for each subject by the number of observations for that subject.

Value

A named matrix for the covariance of the regression coefficients specified in 'model', calculated using the sandwich method.

Details

For a model of class '"haplo.ccs"', the sandwich variance-covariance matrix is automatically provided as the object 'covariance'. The matrix may also be obtained by 'vcov'. See examples below.

Examples

Run this code

data(Renin)

## Fit a model for covariate effects.

m1 <- glm(case ~ age + factor(race) + gender, family = binomial(link = logit))

## Obtain sandwich variance-covariance matrix.

id <- 1:length(case)
v1 <- sandcov(m1, id)

## Calculate robust standard error estimates.

se1 <- sqrt(diag(v1))

## Fit a model for haplotype and covariate effects.

m2 <- haplo.ccs(case ~ gender + age + factor(race) + haplo(geno[,1:12], mode = "additive"),
                control = haplo.em.control(min.posterior=1e-4), referent = "223144")

## Obtain sandwich variance-covariance matrix by one of two methods.

v2 <- m2$covariance
v2 <- vcov(m2)

## Calculate robust standard error estimates.

se2 <- sqrt(diag(v2))