Copyright	(c) 2009 2010 Bryan O'Sullivan
License	BSD3
Maintainer	bos@serpentine.com
Stability	experimental
Portability	portable
Safe Haskell	None
Language	Haskell98

Statistics.Resampling

Description

Resampling statistics.

Synopsis

Documentation

newtype Resample #

A resample drawn randomly, with replacement, from a set of data points. Distinct from a normal array to make it harder for your humble author's brain to go wrong.

Constructors

Resample
Fields fromResample :: Vector Double

Instances

Eq Resample #
Methods (==) :: Resample -> Resample -> Bool # (/=) :: Resample -> Resample -> Bool #
Data Resample #
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Resample -> c Resample # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Resample # toConstr :: Resample -> Constr # dataTypeOf :: Resample -> DataType # dataCast1 :: Typeable (* -> ) t => (forall d. Data d => c (t d)) -> Maybe (c Resample) # dataCast2 :: Typeable ( -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Resample) # gmapT :: (forall b. Data b => b -> b) -> Resample -> Resample # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Resample -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Resample -> r # gmapQ :: (forall d. Data d => d -> u) -> Resample -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> Resample -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> Resample -> m Resample # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Resample -> m Resample # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Resample -> m Resample #
Read Resample #
Methods readsPrec :: Int -> ReadS Resample # readList :: ReadS [Resample] # readPrec :: ReadPrec Resample # readListPrec :: ReadPrec [Resample] #
Show Resample #
Methods showsPrec :: Int -> Resample -> ShowS # show :: Resample -> String # showList :: [Resample] -> ShowS #
Generic Resample #
Associated Types type Rep Resample :: * -> * # Methods from :: Resample -> Rep Resample x # to :: Rep Resample x -> Resample #
ToJSON Resample #
Methods toJSON :: Resample -> Value # toEncoding :: Resample -> Encoding # toJSONList :: [Resample] -> Value # toEncodingList :: [Resample] -> Encoding #
FromJSON Resample #
Methods parseJSON :: Value -> Parser Resample # parseJSONList :: Value -> Parser [Resample] #
Binary Resample #
Methods put :: Resample -> Put # get :: Get Resample # putList :: [Resample] -> Put #
type Rep Resample #
type Rep Resample = D1 (MetaData "Resample" "Statistics.Resampling" "statistics-0.13.3.0-5G6LnnwwEAsBXhZQyOoIJ5" True) (C1 (MetaCons "Resample" PrefixI True) (S1 (MetaSel (Just Symbol "fromResample") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 (Vector Double))))

jackknife :: Estimator -> Sample -> Vector Double #

O(n) or O(n^2) Compute a statistical estimate repeatedly over a sample, each time omitting a successive element.

jackknifeMean :: Sample -> Vector Double #

O(n) Compute the jackknife mean of a sample.

jackknifeVariance :: Sample -> Vector Double #

O(n) Compute the jackknife variance of a sample.

jackknifeVarianceUnb :: Sample -> Vector Double #

O(n) Compute the unbiased jackknife variance of a sample.

jackknifeStdDev :: Sample -> Vector Double #

O(n) Compute the jackknife standard deviation of a sample.

resample #

Arguments

:: GenIO
-> [Estimator]	Estimation functions.
-> Int	Number of resamples to compute.
-> Sample	Original sample.
-> IO [Resample]

O(e*r*s) Resample a data set repeatedly, with replacement, computing each estimate over the resampled data.

This function is expensive; it has to do work proportional to e*r*s, where e is the number of estimation functions, r is the number of resamples to compute, and s is the number of original samples.

To improve performance, this function will make use of all available CPUs. At least with GHC 7.0, parallel performance seems best if the parallel garbage collector is disabled (RTS option -qg).

estimate :: Estimator -> Sample -> Double #

Run an Estimator over a sample.

splitGen :: Int -> GenIO -> IO [GenIO] #

Split a generator into several that can run independently.