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 <[email protected]> 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 > [email protected] > 801.408.8111 > > >> -----Original Message----- >> From: [email protected] [mailto:r-help-boun...@r- >> project.org] On Behalf Of jastar >> Sent: Thursday, May 14, 2009 2:06 PM >> To: [email protected] >> 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. >> >> ______________________________________________ >> [email protected] mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting- >> guide.html >> and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > [email protected] mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Joshua Stults Website: http://j-stults.blogspot.com ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
