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

Copyright (c) 2009 2010 Bryan O'Sullivan BSD3 bos@serpentine.com experimental portable None 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 FieldsfromResample :: Vector Double

Instances

 # Methods # Methodsgfoldl :: (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 #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 # # Methods # MethodsshowList :: [Resample] -> ShowS # # Associated Typestype Rep Resample :: * -> * # Methodsto :: Rep Resample x -> Resample # # MethodstoJSONList :: [Resample] -> Value #toEncodingList :: [Resample] -> Encoding # # Methods # Methodsput :: Resample -> Put #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))))

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

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

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

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

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

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).

Run an Estimator over a sample.

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

Split a generator into several that can run independently.