"Fast" correlation algorithm
If you need auto(cross)correlations in O(n*log(n)) rather than O(n^2) you can use an FFT. Here's a good short write-up on using the FFT for this (numerical recipes chapter): http://hebb.mit.edu/courses/9.29/2002/readings/c13-2.pdf Won't get you p values, but is faster than a normal matrix-vector multiply. If I understand your post correctly though, you are doing bunches of vectors of dimension ~100, probably the standard method is plenty fast, you may not see speed up by using an FFT for vectors this small (larger overhead for the transform -> operations -> inverse transform).
On Thu, May 14, 2009 at 5:02 PM, Greg Snow <Greg.Snow at imail.org> wrote:
Well if your matrix and vector are centered and properly scaled (and there are no missing values), then the correlations are just a crossproduct and matrix arithmetic is already fairly fast (assuming you have enough memory). -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.snow at imail.org 801.408.8111
-----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r- project.org] On Behalf Of jastar Sent: Thursday, May 14, 2009 2:06 PM To: r-help at r-project.org Subject: [R] "Fast" correlation algorithm Hi, Is in R any "fast" algorithm for correlation? What I mean is: I have very large dataset (microarray) with 55000 rows and 100 columns. I want to count correlation (p-value and cor.coef) between each row of dataset and some vector (of course length of this vector is equal to number of columns of dataset). In short words: For t-test we have: "normal" algorithm - t.test "fast" algorithm - rowttests For correlation: "normal" algorithm - cor.test "fast" algorithm - ??? Thank's for help -- View this message in context: http://www.nabble.com/%22Fast%22- correlation-algorithm-tp23548016p23548016.html Sent from the R help mailing list archive at Nabble.com.
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