statistics-0.16.1.2: A library of statistical types, data, and functions
Copyright 2014 Bryan O'Sullivan
License BSD3
Safe Haskell None
Language Haskell2010

Statistics.Regression

Description

Functions for regression analysis.

Synopsis

Documentation

olsRegress Source #

Arguments

:: [ Vector ]

Non-empty list of predictor vectors. Must all have the same length. These will become the columns of the matrix A solved by ols .

-> Vector

Responder vector. Must have the same length as the predictor vectors.

-> ( Vector , Double )

Perform an ordinary least-squares regression on a set of predictors, and calculate the goodness-of-fit of the regression.

The returned pair consists of:

  • A vector of regression coefficients. This vector has one more element than the list of predictors; the last element is the y -intercept value.
  • , the coefficient of determination (see rSquare for details).

ols Source #

Arguments

:: Matrix

A has at least as many rows as columns.

-> Vector

b has the same length as columns in A .

-> Vector

Compute the ordinary least-squares solution to A x = b .

rSquare Source #

Arguments

:: Matrix

Predictors (regressors).

-> Vector

Responders.

-> Vector

Regression coefficients.

-> Double

Compute , the coefficient of determination that indicates goodness-of-fit of a regression.

This value will be 1 if the predictors fit perfectly, dropping to 0 if they have no explanatory power.

bootstrapRegress Source #

Arguments

:: GenIO
-> Int

Number of resamples to compute.

-> CL Double

Confidence level.

-> ([ Vector ] -> Vector -> ( Vector , Double ))

Regression function.

-> [ Vector ]

Predictor vectors.

-> Vector

Responder vector.

-> IO ( Vector ( Estimate ConfInt Double ), Estimate ConfInt Double )

Bootstrap a regression function. Returns both the results of the regression and the requested confidence interval values.