statistics-linreg: Linear regression between two samples, based on the 'statistics' package.
|Versions||0.1, 0.2, 0.2.1, 0.2.2, 0.2.3, 0.2.4, 0.3|
|Dependencies||base (==4.*), MonadRandom (>=0.1), random (>=1.0), random‑shuffle (>=0.0.4), safe (>=0.3), statistics (>=0.5), vector (>=0.5) [details]|
|Copyright||2010-2012 Alp Mestanogullari|
|Author||Alp Mestanogullari <firstname.lastname@example.org>, Uri Barenholz <email@example.com>|
|Maintainer||Alp Mestanogullari <firstname.lastname@example.org>|
|Source repo||head: git clone http://github.com/alpmestan/statistics-linreg.git|
|Uploaded||by AlpMestanogullari at Sat Jan 5 16:04:03 UTC 2013|
|Downloads||3430 total (26 in the last 30 days)|
|Rating||2.0 (votes: 1) [estimated by rule of succession]|
|Status||Docs uploaded by user
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Provides functions to perform a linear regression between 2 samples, see the documentation of the linearRegression functions. This library is based on the
0.2.3: added robust-fit support.
0.2.2: added the Total-Least-Squares version and made some refactoring to eliminate code duplication
0.2.1: added the r-squared version and improved the performances.
import qualified Data.Vector.Unboxed as U test :: Int -> IO () test k = do let n = 10000000 let a = k*n + 1 let b = (k+1)*n let xs = U.fromList [a..b] let ys = U.map (\x -> x*100 + 2000) xs -- thus 100 and 2000 are the alpha and beta we want putStrLn "linearRegression:" print $ linearRegression xs ys
The r-squared and Total-Least-Squares versions work the same way.
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