Statistics.Regression

Description

Functions for regression analysis.

Synopsis

Documentation

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.
R² , the coefficient of determination (see rSquare for details).

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 .

Arguments

Compute R² , 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.

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.