naive fitting. More...

#include <yat/regression/NaiveWeighted.h>

Inheritance diagram for theplu::yat::regression::NaiveWeighted:

Public Member Functions
	NaiveWeighted (void)
	The default constructor.

virtual	~NaiveWeighted (void)
	The destructor.

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

double	predict (const double x) const

double	s2 (const double w=1) 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. More...

Detailed Description

naive fitting.

Member Function Documentation

void theplu::yat::regression::NaiveWeighted::fit	(	const utility::VectorBase &	x,
		const utility::VectorBase &	y,
		const utility::VectorBase &	w
	)

virtual

This function computes the best-fit for the naive model $ y = m $ from vectors x and y, by minimizing $\sum w_i(y_i-m)^2$ . The weight $ w_i $ is proportional to the inverse of the variance for $ y_i $

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

double theplu::yat::regression::NaiveWeighted::predict ( const double x ) const

virtual

Function predicting value using the naive model, i.e. a weighted average.

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.

double theplu::yat::regression::NaiveWeighted::s2 ( const double w = 1 ) const

virtual

$\frac{\sum w_i\epsilon_i^2}{ w \left(\frac{\left(\sum w_i\right)^2}{\sum w_i^2}-1\right)}$

Rescaling all weights, both in fit and the passed w, results in the same returned value.

Returns: Conditional variance of Y with weight w.

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

double theplu::yat::regression::NaiveWeighted::standard_error2 ( const double x ) const

virtual

$\frac{\sum w_i\epsilon_i^2}{ \left(\frac{\left(\sum w_i\right)^2}{\sum w_i^2}-1\right)\sum w_i}$

Returns: estimated squared error of model value in x

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

Member Data Documentation

statistics::AveragerPairWeighted theplu::yat::regression::OneDimensionalWeighted::ap_

protectedinherited

Averager for pair of x and y

double theplu::yat::regression::OneDimensionalWeighted::chisq_

protectedinherited

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/NaiveWeighted.h

Public Member Functions

Protected Attributes

Detailed Description

Member Function Documentation

Member Data Documentation