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

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

Statistics.Sample.KernelDensity.Simple

Contents

- Simple entry points
- Building blocks
  - Choosing points from a sample
  - Bandwidth estimation
  - Kernels
  - Low-level estimation
- References

Description

Deprecated: Use Statistics.Sample.KernelDensity instead.

Kernel density estimation code, providing non-parametric ways to estimate the probability density function of a sample.

The techniques used by functions in this module are relatively fast, but they generally give inferior results to the KDE function in the main `KernelDensity` module (due to the oversmoothing documented for `bandwidth` below).

Synopsis

- epanechnikovPDF :: Vector v Double => Int -> v Double -> (Points, Vector Double)
- gaussianPDF :: Vector v Double => Int -> v Double -> (Points, Vector Double)
- newtype Points = Points {
  - fromPoints :: Vector Double
  }
- choosePoints :: Vector v Double => Int -> Double -> v Double -> Points
- type Bandwidth = Double
- bandwidth :: Vector v Double => (Double -> Bandwidth) -> v Double -> Bandwidth
- epanechnikovBW :: Double -> Bandwidth
- gaussianBW :: Double -> Bandwidth
- type Kernel = Double -> Double -> Double -> Double -> Double
- epanechnikovKernel :: Kernel
- gaussianKernel :: Kernel
- estimatePDF :: Vector v Double => Kernel -> Bandwidth -> v Double -> Points -> Vector Double
- simplePDF :: Vector v Double => (Double -> Double) -> Kernel -> Double -> Int -> v Double -> (Points, Vector Double)

# Simple entry points

epanechnikovPDF #

Arguments

:: Vector v Double |
=> Int | Number of points at which to estimate
-> v Double | Data sample
-> (Points, Vector Double) |

Simple Epanechnikov kernel density estimator. Returns the uniformly spaced points from the sample range at which the density function was estimated, and the estimates at those points.

gaussianPDF #

Arguments

:: Vector v Double |
=> Int | Number of points at which to estimate
-> v Double | Data sample
-> (Points, Vector Double) |

Simple Gaussian kernel density estimator. Returns the uniformly spaced points from the sample range at which the density function was estimated, and the estimates at those points.

# Building blocks

## Choosing points from a sample

newtype Points #

Points from the range of a `Sample`.

Constructors

Points |
Fields fromPoints :: Vector Double

Instances

Eq Points # |
Methods (==) :: Points -> Points -> Bool # (/=) :: Points -> Points -> Bool #
Data Points # |
Methods gfoldl :: (forall d b. Data d => c (d -> b) -> d -> c b) -> (forall g. g -> c g) -> Points -> c Points # gunfold :: (forall b r. Data b => c (b -> r) -> c r) -> (forall r. r -> c r) -> Constr -> c Points # toConstr :: Points -> Constr # dataTypeOf :: Points -> DataType # dataCast1 :: Typeable (* -> *) t => (forall d. Data d => c (t d)) -> Maybe (c Points) # dataCast2 :: Typeable (* -> * -> *) t => (forall d e. (Data d, Data e) => c (t d e)) -> Maybe (c Points) # gmapT :: (forall b. Data b => b -> b) -> Points -> Points # gmapQl :: (r -> r' -> r) -> r -> (forall d. Data d => d -> r') -> Points -> r # gmapQr :: (r' -> r -> r) -> r -> (forall d. Data d => d -> r') -> Points -> r # gmapQ :: (forall d. Data d => d -> u) -> Points -> [u] # gmapQi :: Int -> (forall d. Data d => d -> u) -> Points -> u # gmapM :: Monad m => (forall d. Data d => d -> m d) -> Points -> m Points # gmapMp :: MonadPlus m => (forall d. Data d => d -> m d) -> Points -> m Points # gmapMo :: MonadPlus m => (forall d. Data d => d -> m d) -> Points -> m Points #
Read Points # |
Methods readsPrec :: Int -> ReadS Points # readList :: ReadS [Points] # readPrec :: ReadPrec Points # readListPrec :: ReadPrec [Points] #
Show Points # |
Methods showsPrec :: Int -> Points -> ShowS # show :: Points -> String # showList :: [Points] -> ShowS #
Generic Points # |
Associated Types type Rep Points :: * -> * # Methods from :: Points -> Rep Points x # to :: Rep Points x -> Points #
FromJSON Points # |
Methods parseJSON :: Value -> Parser Points # parseJSONList :: Value -> Parser [Points] #
ToJSON Points # |
Methods toJSON :: Points -> Value # toEncoding :: Points -> Encoding # toJSONList :: [Points] -> Value # toEncodingList :: [Points] -> Encoding #
Binary Points # |
Methods put :: Points -> Put # get :: Get Points # putList :: [Points] -> Put #
type Rep Points # |
type Rep Points = D1 (MetaData "Points" "Statistics.Sample.KernelDensity.Simple" "statistics-0.13.3.0-LGqAKLOG2BiECOKTeBVRge" True) (C1 (MetaCons "Points" PrefixI True) (S1 (MetaSel (Just Symbol "fromPoints") NoSourceUnpackedness NoSourceStrictness DecidedLazy) (Rec0 (Vector Double))))

choosePoints #

Arguments

:: Vector v Double |
=> Int | Number of points to select, n
-> Double | Sample bandwidth, h
-> v Double | Input data
-> Points |

Choose a uniform range of points at which to estimate a sample's probability density function.

If you are using a Gaussian kernel, multiply the sample's bandwidth by 3 before passing it to this function.

If this function is passed an empty vector, it returns values of positive and negative infinity.

## Bandwidth estimation

type Bandwidth = Double #

The width of the convolution kernel used.

bandwidth :: Vector v Double => (Double -> Bandwidth) -> v Double -> Bandwidth #

Compute the optimal bandwidth from the observed data for the given kernel.

This function uses an estimate based on the standard deviation of a sample (due to Deheuvels), which performs reasonably well for unimodal distributions but leads to oversmoothing for more complex ones.

epanechnikovBW :: Double -> Bandwidth #

Bandwidth estimator for an Epanechnikov kernel.

gaussianBW :: Double -> Bandwidth #

Bandwidth estimator for a Gaussian kernel.

## Kernels

type Kernel = Double -> Double -> Double -> Double -> Double #

The convolution kernel. Its parameters are as follows:

- Scaling factor, 1/nh
- Bandwidth, h
- A point at which to sample the input, p
- One sample value, v

epanechnikovKernel :: Kernel #

Epanechnikov kernel for probability density function estimation.

gaussianKernel :: Kernel #

Gaussian kernel for probability density function estimation.

## Low-level estimation

estimatePDF #

Arguments

:: Vector v Double |
=> Kernel | Kernel function
-> Bandwidth | Bandwidth, h
-> v Double | Sample data
-> Points | Points at which to estimate
-> Vector Double |

Kernel density estimator, providing a non-parametric way of estimating the PDF of a random variable.

simplePDF #

Arguments

:: Vector v Double |
=> (Double -> Double) | Bandwidth function
-> Kernel | Kernel function
-> Double | Bandwidth scaling factor (3 for a Gaussian kernel, 1 for all others)
-> Int | Number of points at which to estimate
-> v Double | sample data
-> (Points, Vector Double) |

A helper for creating a simple kernel density estimation function with automatically chosen bandwidth and estimation points.

# References

- Deheuvels, P. (1977) Estimation non paramétrique de la densité par histogrammes généralisés. Mhttp:/archive.numdam.orgarticle/RSA_1977__25_3_5_0.pdf>