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.