theplu::yat::regression::PolynomialWeighted Class Reference

Polynomial Regression in weighted fashion. More...

#include <yat/regression/PolynomialWeighted.h>

Inheritance diagram for theplu::yat::regression::PolynomialWeighted:

List of all members.

Public Member Functions

PolynomialWeighted (size_t power)

~PolynomialWeighted (void)

Destructor.

void fit (const utility::VectorBase &x, const utility::VectorBase &y, const utility::VectorBase &w)

const utility::Vector & fit_parameters (void) const

double s2 (const double w=1) const

Mean Squared Error.

double predict (const double x) const

double standard_error2 (const double x) const

double prediction_error2 (const double x, const double w=1.0) const

double r2 (void) const

Protected Attributes

statistics::AveragerPairWeighted ap_

double chisq_

Chi-squared.

Detailed Description

Polynomial Regression in weighted fashion.

Constructor & Destructor Documentation

theplu::yat::regression::PolynomialWeighted::PolynomialWeighted ( size_t power )

Parameters:

power

degree of polynomial model

Member Function Documentation

void theplu::yat::regression::PolynomialWeighted::fit	(	const utility::VectorBase &	x,
		const utility::VectorBase &	y,
		const utility::VectorBase &	w
	)			`[virtual]`

This function computes the best-fit given the polynomial model model by minimizing $\sum{w_i(\hat{y_i}-y_i)^2}$ , where $\hat{y}$ is the fitted value. The weight $ w_i $ should be proportional to the inverse of the variance for $ /// y_i $

Implements theplu::yat::regression::OneDimensionalWeighted.

const utility::Vector& theplu::yat::regression::PolynomialWeighted::fit_parameters ( void ) const

Returns:: parameters of the model

See also:: MultiDimensional

double theplu::yat::regression::PolynomialWeighted::predict ( const double x ) const [virtual]

function predicting in one point.

Implements theplu::yat::regression::OneDimensionalWeighted.

double theplu::yat::regression::PolynomialWeighted::standard_error2 ( const double x ) const [virtual]

Returns:: error of model value in x

Implements theplu::yat::regression::OneDimensionalWeighted.

double theplu::yat::regression::OneDimensionalWeighted::prediction_error2	(	const double	x,
		const double	w = `1.0`
	)			const `[inherited]`

The prediction error is defined as expected squared deviation a new data point (with weight w) will be from the model value $E((Y|x - \hat{y}(x))^2|w)$ and is typically divided into the conditional variance ( see s2() ) given $ x $ and the squared standard error ( see standard_error2() ) of the model estimation in $ x $ .

$E((Y|x - E(Y|x))^2|w) + E((E(Y|x) - \hat{y}(x))^2)$

Returns:: expected prediction error for a new data point in x with weight w.

double theplu::yat::regression::OneDimensionalWeighted::r2 ( void ) const [inherited]

r2 is defined as $\frac{\sum w_i(y_i-\hat{y}_i)^2}{\sum w_i(y_i-m_y)^2}$ or the fraction of the variance explained by the regression model.

Member Data Documentation

statistics::AveragerPairWeighted theplu::yat::regression::OneDimensionalWeighted::ap_ [protected, inherited]

Averager for pair of x and y

double theplu::yat::regression::OneDimensionalWeighted::chisq_ [protected, inherited]

Chi-squared.

Chi-squared is defined as the $\sum{w_i(\hat{y_i}-y_i)^2}$

The documentation for this class was generated from the following file:

yat/regression/PolynomialWeighted.h


Public Member Functions
	PolynomialWeighted (size_t power)
	~PolynomialWeighted (void)
	Destructor.
void	fit (const utility::VectorBase &x, const utility::VectorBase &y, const utility::VectorBase &w)
const utility::Vector &	fit_parameters (void) const
double	s2 (const double w=1) const
	Mean Squared Error.
double	predict (const double x) const
double	standard_error2 (const double x) const
double	prediction_error2 (const double x, const double w=1.0) const
double	r2 (void) const
Protected Attributes
statistics::AveragerPairWeighted	ap_
double	chisq_
	Chi-squared.