Statistics.Resampling

statistics-0.13.3.0: A library of statistical types, data, and functions

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

- newtype Resample = Resample {
  - fromResample :: Vector Double
  }
- jackknife :: Estimator -> Sample -> Vector Double
- jackknifeMean :: Sample -> Vector Double
- jackknifeVariance :: Sample -> Vector Double
- jackknifeVarianceUnb :: Sample -> Vector Double
- jackknifeStdDev :: Sample -> Vector Double
- resample :: GenIO -> [Estimator] -> Int -> Sample -> IO [Resample]
- estimate :: Estimator -> Sample -> Double
- splitGen :: Int -> GenIO -> IO [GenIO]

# 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 #
FromJSON Resample # |
Methods parseJSON :: Value -> Parser Resample # parseJSONList :: Value -> Parser [Resample] #
ToJSON Resample # |
Methods toJSON :: Resample -> Value # toEncoding :: Resample -> Encoding # toJSONList :: [Resample] -> Value # toEncodingList :: [Resample] -> Encoding #
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-A6M5lvDj6Zg1bzI7OU7tSj" 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.