| Copyright | 2014 Bryan O'Sullivan |
|---|---|
| License | BSD3 |
| Safe Haskell | Safe-Inferred |
| Language | Haskell2010 |
Statistics.Regression
Description
Functions for regression analysis.
Synopsis
- olsRegress :: [Vector] -> Vector -> (Vector, Double)
- ols :: Matrix -> Vector -> Vector
- rSquare :: Matrix -> Vector -> Vector -> Double
- bootstrapRegress :: GenIO -> Int -> CL Double -> ([Vector] -> Vector -> (Vector, Double)) -> [Vector] -> Vector -> IO (Vector (Estimate ConfInt Double), Estimate ConfInt Double)
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 |
| -> 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
rSquarefor 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 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.