Copyright | 2014 Bryan O'Sullivan |
---|---|
License | BSD3 |
Safe Haskell | None |
Language | Haskell98 |
Functions for regression analysis.
Documentation
:: [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
rSquare
for details).
:: 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.
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.
:: GenIO | |
-> Int | Number of resamples to compute. |
-> Double | Confidence interval. |
-> ([Vector] -> Vector -> (Vector, Double)) | Regression function. |
-> [Vector] | Predictor vectors. |
-> Vector | Responder vector. |
-> IO (Vector Estimate, Estimate) |
Bootstrap a regression function. Returns both the results of the regression and the requested confidence interval values.