statistics-0.14.0.2: A library of statistical types, data, and functions

Copyright(c) 2009 Bryan O'Sullivan
LicenseBSD3
Maintainerbos@serpentine.com
Stabilityexperimental
Portabilityportable
Safe HaskellNone
LanguageHaskell98

Statistics.Quantile

Contents

Description

Functions for approximating quantiles, i.e. points taken at regular intervals from the cumulative distribution function of a random variable.

The number of quantiles is described below by the variable q, so with q=4, a 4-quantile (also known as a quartile) has 4 intervals, and contains 5 points. The parameter k describes the desired point, where 0 ≤ kq.

Synopsis

Quantile estimation functions

weightedAvg Source #

Arguments

:: Vector v Double 
=> Int

k, the desired quantile.

-> Int

q, the number of quantiles.

-> v Double

x, the sample data.

-> Double 

O(n log n). Estimate the kth q-quantile of a sample, using the weighted average method.

The following properties should hold: * the length of the input is greater than 0 * the input does not contain NaN * k ≥ 0 and k ≤ q

otherwise an error will be thrown.

data ContParam Source #

Parameters a and b to the continuousBy function.

Constructors

ContParam !Double !Double 

continuousBy Source #

Arguments

:: Vector v Double 
=> ContParam

Parameters a and b.

-> Int

k, the desired quantile.

-> Int

q, the number of quantiles.

-> v Double

x, the sample data.

-> Double 

O(n log n). Estimate the kth q-quantile of a sample x, using the continuous sample method with the given parameters. This is the method used by most statistical software, such as R, Mathematica, SPSS, and S.

midspread Source #

Arguments

:: Vector v Double 
=> ContParam

Parameters a and b.

-> Int

q, the number of quantiles.

-> v Double

x, the sample data.

-> Double 

O(n log n). Estimate the range between q-quantiles 1 and q-1 of a sample x, using the continuous sample method with the given parameters.

For instance, the interquartile range (IQR) can be estimated as follows:

midspread medianUnbiased 4 (U.fromList [1,1,2,2,3])
==> 1.333333

Parameters for the continuous sample method

cadpw :: ContParam Source #

California Department of Public Works definition, a=0, b=1. Gives a linear interpolation of the empirical CDF. This corresponds to method 4 in R and Mathematica.

hazen :: ContParam Source #

Hazen's definition, a=0.5, b=0.5. This is claimed to be popular among hydrologists. This corresponds to method 5 in R and Mathematica.

s :: ContParam Source #

Definition used by the S statistics application, with a=1, b=1. The interpolation points divide the sample range into n-1 intervals. This corresponds to method 7 in R and Mathematica.

spss :: ContParam Source #

Definition used by the SPSS statistics application, with a=0, b=0 (also known as Weibull's definition). This corresponds to method 6 in R and Mathematica.

medianUnbiased :: ContParam Source #

Median unbiased definition, a=1/3, b=1/3. The resulting quantile estimates are approximately median unbiased regardless of the distribution of x. This corresponds to method 8 in R and Mathematica.

normalUnbiased :: ContParam Source #

Normal unbiased definition, a=3/8, b=3/8. An approximately unbiased estimate if the empirical distribution approximates the normal distribution. This corresponds to method 9 in R and Mathematica.

References