Bootstrap analysis for partial correlations

Hello, I was hoping someone could please help or direct me to another page that explains bootstrapping with partial correlations (i.e., correlations between two variables after controlling for a third variable)?

For example, correlating posttest performance with another variable (e.g., SAT score) AFTER controlling for pretest performance. Given our low N, we thought a bootstrap analysis would be useful, but my google-fu has been unsuccessful thus far in terms of how-to.

Note: this does not necessarily have to be in R, though I'm assuming it's my best bet.

Thanks so much for reading my question!


Phineas Packard
Not overly familiar with boot strapping BUT would something like the following work:
#stolen from
partial.cor <- function (X, ...) 
  R <- cor(X, ...)
  RI <- solve(R)
  D <- 1/sqrt(diag(RI))
  R <- -RI * (D %o% D)
  diag(R) <- 0
  rownames(R) <- colnames(R) <- colnames(X)

#Create some fake data set
myData <- data.frame(xPre = rnorm(100), xPost = rnorm (100), SAT = rnorm(100))
#Partial correlation

#creat bootstrap sample
R <- 100 #number of replications
#Bootstrap samples
boots<- list()
for (i in 1:R){
                boots[[i]] <-[sample(1:nrow(myData),nrow(myData), replace=TRUE),])

#Bootstrap responses for xPost SAT partial correlation ONLY
#change index [3,2] if you want a different one.
boot.partial <- sapply(boots, function(x) partial.cor(x)[3,2])
quantile(boot.partial, probs=c(.025, .975))
Sounds like "An introduction to bootstrap methods with applications to R" is good for you ... Authors Chernick & LaBudde.

A good one that's focused on regression analysis only (and hence probably not what you're after) is Bootstrap Tests for Regression Models by Leslie Godfrey.