statistics-0.14.0.0: A library of statistical types, data, and functions

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.
• , 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

 :: 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.

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.