robust standard errors in ggplot2

Question

I would like to plot a model with ggplot2. I have estimated a robust variance-covariance matrix which I would like to use when estimating the confidence interval.

Can I tell ggplot2 to use my VCOV, or, alternatively, can I somehow force predict.lm to use my VCOV matrix? A dummy example:

source("http://people.su.se/~ma/clmclx.R")
df <- data.frame(x1 = rnorm(100), x2 = rnorm(100), y = rnorm(100), group = as.factor(sample(1:10, 100, replace=T))) 
lm1 <- lm(y ~ x1 + x2, data = df)
coeftest(lm1)
## outputs coef.test, but can be modified to output VCOV
clx(lm1, 1, df$group)

It would be relatively easy to add to a ggplot, if I could get 'correct' predictions given my augmented VCOV-matrix.

Answer 1

Only the standard errors, not the predictions, should change -- right?

getvcov <- function(fm,dfcw,cluster) {
  library(sandwich);library(lmtest)
  M <- length(unique(cluster))   
  N <- length(cluster)           
  K <- fm$rank                        
  dfc <- (M/(M-1))*((N-1)/(N-K))  
  uj  <- apply(estfun(fm),2, function(x) tapply(x, cluster, sum));
  dfc*sandwich(fm, meat=crossprod(uj)/N)*dfcw
}

V <- getvcov(lm1,1,df$group)
X <- as.matrix(model.frame(lm1))
se <- predict(lm1,se=TRUE)$se.fit
se_robust <- sqrt(diag(X %*% V %*% t(X)))