hmatrix-gsl-stats-0.2.1: GSL Statistics interface

Copyright(c) A. V. H. McPhail 2010
LicenseBSD3
Maintainerhaskell.vivian.mcphail <at> gmail <dot> com
Stabilityprovisional
Portabilityuses ffi
Safe HaskellNone
LanguageHaskell2010

Numeric.GSL.Fitting.Linear

Description

GSL linear regression functions

http://www.gnu.org/software/gsl/manual/

Synopsis

Documentation

linear Source

Arguments

:: Vector Double

x data

-> Vector Double

y data

-> (Double, Double, Double, Double, Double, Double)

(c_0,c_1,cov_00,cov_01,cov_11,chi_sq)

fits the model Y = C X

linear_w Source

Arguments

:: Vector Double

x data

-> Vector Double

x weights

-> Vector Double

y data

-> (Double, Double, Double, Double, Double, Double)

(c_0,c_1,cov_00,cov_01,cov_11,chi_sq)

fits the model Y = C X, with x data weighted

linear_est Source

Arguments

:: Double

x data point

-> Double

c0

-> Double

c1

-> Double

cov00

-> Double

cov01

-> Double

cov11

-> (Double, Double)

(y,error)

computes the fitted function and standard deviation at the input point

multifit Source

Arguments

:: Matrix Double

design matrix (X)

-> Vector Double

observations

-> (Vector Double, Matrix Double, Double)

(coefficients,covariance,chi_sq)

fit the model Y = C X, with design matrix X | X is a design matrix X_{ij} = x_j(i) with i observations and p predictors | a polynomial would be X_{ij} = x_i^j | a fourier series would be X_{ij} = sin (omega_j x_i)

multifit_w Source

Arguments

:: Matrix Double

design matrix (X)

-> Vector Double

weights

-> Vector Double

observations

-> (Vector Double, Matrix Double, Double)

(coefficients,covariance,chi_sq)

fit the model Y = C X, with design matrix X, and x weighted

multifit_est Source

Arguments

:: Vector Double

input point

-> Vector Double

the coefficients

-> Matrix Double

the covariance matrix

-> (Double, Double)

(y,y_error)

computes the fitted function and standard deviation at the input point