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{-# LANGUAGE FlexibleContexts #-}
-- | Pearson's chi squared test.
module Statistics.Test.ChiSquared (
chi2test
-- * Data types
, TestType(..)
, TestResult(..)
) where
import qualified Data.Vector.Generic as G
import Statistics.Distribution
import Statistics.Distribution.ChiSquared
import Statistics.Test.Types
-- | Generic form of Pearson chi squared tests for binned data. Data
-- sample is supplied in form of tuples (observed quantity,
-- expected number of events). Both must be positive.
chi2test :: (G.Vector v (Int,Double), G.Vector v Double)
=> Double -- ^ p-value
-> Int -- ^ Number of additional degrees of
-- freedom. One degree of freedom
-- is due to the fact that the are
-- N observation in total and
-- accounted for automatically.
-> v (Int,Double) -- ^ Observation and expectation.
-> TestResult
chi2test p ndf vec
| ndf < 0 = error $ "Statistics.Test.ChiSquare.chi2test: negative NDF " ++ show ndf
| n < 0 = error $ "Statistics.Test.ChiSquare.chi2test: too short data sample"
| p > 0 && p < 1 = significant $ complCumulative d chi2 < p
| otherwise = error $ "Statistics.Test.ChiSquare.chi2test: bad p-value: " ++ show p
where
n = G.length vec - ndf - 1
chi2 = G.sum $ G.map (\(o,e) -> sqr (fromIntegral o - e) / e) vec
d = chiSquared n
sqr x = x * x
{-# INLINE chi2test #-}
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