On Sat, Jan 21, 2012 at 6:26 PM, John Salvatier
I ran into this a while ago and was confused why cov did not behave the way pierre suggested.
same here, When I rewrote scipy.stats.spearmanr, I matched the numpy behavior for two arrays, while R only returns the cross-correlation part. Josef
On Jan 21, 2012 12:48 PM, "Elliot Saba"
wrote: Thank you Sturla, that's exactly what I want.
I'm sorry that I was not able to reply for so long, but Pierre's code is similar to what I have already implemented, and I am in support of changing the functionality of cov(). I am unaware of any arguments for a covariance function that works in this way, except for the fact that the MATLAB cov() function behaves in the same way. [1]
MATLAB, however, has an xcov() function, which is similar to what we have been discussing. [2]
Unless you all wish to retain compatibility with MATLAB, I feel that the behaviour of cov() suggested by Pierre is the most straightforward method, and that if users wish to calculate the covariance of X concatenated with Y, then they may simply concatenate the matrices explicitly before passing into cov(), as this way the default method does not use 75% more CPU time.
Again, if there is an argument for this functionality, I would love to learn of it! -E
[1] http://www.mathworks.com/help//techdoc/ref/cov.html [2] http://www.mathworks.com/help/toolbox/signal/ref/xcov.html
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