Copyright	(c) 2010 Alexey Khudyakov
License	BSD3
Maintainer	bos@serpentine.com
Stability	experimental
Portability	portable
Safe Haskell	None
Language	Haskell2010

Statistics.Distribution.ChiSquared

Contents

Constructors

Description

The chi-squared distribution. This is a continuous probability distribution of sum of squares of k independent standard normal distributions. It's commonly used in statistical tests

Synopsis

Documentation

data ChiSquared Source #

Chi-squared distribution

Instances

Instances details

FromJSON ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

parseJSON :: Value -> Parser ChiSquared #

parseJSONList :: Value -> Parser [ChiSquared] #

omittedField :: Maybe ChiSquared #

ToJSON ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

toJSON :: ChiSquared -> Value #

toEncoding :: ChiSquared -> Encoding #

toJSONList :: [ChiSquared] -> Value #

toEncodingList :: [ChiSquared] -> Encoding #

omitField :: ChiSquared -> Bool #

Data ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> ChiSquared -> c ChiSquared #

gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c ChiSquared #

toConstr :: ChiSquared -> Constr #

dataTypeOf :: ChiSquared -> DataType #

dataCast1 :: Typeable t => (forall d. Data d => c (t d)) -> Maybe (c ChiSquared) #

dataCast2 :: Typeable t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c ChiSquared) #

gmapT :: (forall b. Data b => b -> b) -> ChiSquared -> ChiSquared #

gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> ChiSquared -> r #

gmapQr :: forall r r'. (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> ChiSquared -> r #

gmapQ :: (forall d. Data d => d -> u) -> ChiSquared -> [u] #

gmapQi :: Int -> (forall d. Data d => d -> u) -> ChiSquared -> u #

gmapM :: Monad m => (forall d. Data d => d -> m d) -> ChiSquared -> m ChiSquared #

gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> ChiSquared -> m ChiSquared #

gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> ChiSquared -> m ChiSquared #

Generic ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Associated Types

type Rep ChiSquared

Instance details

Defined in Statistics.Distribution.ChiSquared

type Rep ChiSquared = D1 ('MetaData "ChiSquared" "Statistics.Distribution.ChiSquared" "statistics-0.16.4.0-1cbrVsgEUI21RJju9YwBZG" 'True) (C1 ('MetaCons "ChiSquared" 'PrefixI 'True) (S1 ('MetaSel ('Just "chiSquaredNDF") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Int)))

Methods

from :: ChiSquared -> Rep ChiSquared x #

to :: Rep ChiSquared x -> ChiSquared #

Read ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

readsPrec :: Int -> ReadS ChiSquared #

readList :: ReadS [ChiSquared] #

readPrec :: ReadPrec ChiSquared #

readListPrec :: ReadPrec [ChiSquared] #

Show ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

showsPrec :: Int -> ChiSquared -> ShowS #

show :: ChiSquared -> String #

showList :: [ChiSquared] -> ShowS #

Binary ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

put :: ChiSquared -> Put #

get :: Get ChiSquared #

putList :: [ChiSquared] -> Put #

Eq ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

(==) :: ChiSquared -> ChiSquared -> Bool #

(/=) :: ChiSquared -> ChiSquared -> Bool #

ContDistr ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

density :: ChiSquared -> Double -> Double Source #

logDensity :: ChiSquared -> Double -> Double Source #

quantile :: ChiSquared -> Double -> Double Source #

complQuantile :: ChiSquared -> Double -> Double Source #

ContGen ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

genContVar :: StatefulGen g m => ChiSquared -> g -> m Double Source #

Distribution ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

cumulative :: ChiSquared -> Double -> Double Source #

complCumulative :: ChiSquared -> Double -> Double Source #

Entropy ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

entropy :: ChiSquared -> Double Source #

MaybeEntropy ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

maybeEntropy :: ChiSquared -> Maybe Double Source #

MaybeMean ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

maybeMean :: ChiSquared -> Maybe Double Source #

MaybeVariance ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

maybeVariance :: ChiSquared -> Maybe Double Source #

maybeStdDev :: ChiSquared -> Maybe Double Source #

Mean ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

mean :: ChiSquared -> Double Source #

Variance ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

Methods

variance :: ChiSquared -> Double Source #

stdDev :: ChiSquared -> Double Source #

type Rep ChiSquared Source #

Instance details

Defined in Statistics.Distribution.ChiSquared

type Rep ChiSquared = D1 ('MetaData "ChiSquared" "Statistics.Distribution.ChiSquared" "statistics-0.16.4.0-1cbrVsgEUI21RJju9YwBZG" 'True) (C1 ('MetaCons "ChiSquared" 'PrefixI 'True) (S1 ('MetaSel ('Just "chiSquaredNDF") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Int)))

chiSquaredNDF :: ChiSquared -> Int Source #

Get number of degrees of freedom

Constructors

chiSquared :: Int -> ChiSquared Source #

Construct chi-squared distribution. Number of degrees of freedom must be positive.

chiSquaredE :: Int -> Maybe ChiSquared Source #

Construct chi-squared distribution. Number of degrees of freedom must be positive.