.lm.fit is considerably faster than lm for reasons documented in several places, but it is not as straight forward to get an adjusted r-squared value so I'm hoping for some help.
Using lm() and then summary() to get the adjusted r-squared.
tstlm <- lm(cyl ~ hp + wt, data = mtcars)
summary(tstlm)$adj.r.squared
Using .lm.fit
mtmatrix <- as.matrix(mtcars)
tstlmf <- .lm.fit(cbind(1,mtmatrix [,c("hp","wt")]), mtmatrix [,"cyl"])
And here I'm stuck. I suspect the information I need to calculate adjusted r-squared is found in the .lm.fit model somewhere but I can't quite figure out how to proceed.
Thanks in advance for any suggestions.
The following function computes the adjusted R2 from an object returned by .lm.fit and the response vector y.
adj_r2_lmfit <- function(object, y){
ypred <- y - resid(object)
mss <- sum((ypred - mean(ypred))^2)
rss <- sum(resid(object)^2)
rdf <- length(resid(object)) - object$rank
r.squared <- mss/(mss + rss)
adj.r.squared <- 1 - (1 - r.squared)*(NROW(y) - 1)/rdf
adj.r.squared
}
tstlm <- lm(cyl ~ hp + wt, data = mtcars)
tstlmf <- .lm.fit(cbind(1,mtmatrix [,c("hp","wt")]), mtmatrix [,"cyl"])
summary(tstlm)$adj.r.squared
#[1] 0.7753073
adj_r2_lmfit(tstlmf, mtmatrix [,"cyl"])
#[1] 0.7753073