R help - Mar 2009 - Efficient calculation of partial correlations in R

Hello,

I'm looking for an efficient function for calculating partial correlations.
I'm currently using the pcor.test () function, which is equivalent to the
cor.test() function, and can receive only single vectors as input. I'm
looking for something which is equivalent to the cor() function, and can
receive matrixes as input (which should make the calculations much more
efficient). 

 

Thanks,

Schragi

 


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Dear Schragi,

There's a function named partial.cor() in the Rcmdr package, but it's so
simple that I'll just reproduce it here:

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)
    R
}

Of course, this gives you the partial correlation between each pair of
variables controlling for all others, which is I assume what you want.

I hope this helps,
 John

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at
r-project.org]
On
> Behalf Of Schragi Schwartz
> Sent: March-31-09 5:21 AM
> To: r-help at r-project.org
> Cc: 'Dror Hollander'
> Subject: [R] Efficient calculation of partial correlations in R
> 
> Hello,
> 
> I'm looking for an efficient function for calculating partial
correlations.
> I'm currently using the pcor.test () function, which is equivalent to
the
> cor.test() function, and can receive only single vectors as input. I'm
> looking for something which is equivalent to the cor() function, and can
> receive matrixes as input (which should make the calculations much more
> efficient).
> 
> 
> 
> Thanks,
> 
> Schragi
> 
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
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