Interface Class for One Dimensional fitting. More...

#include </scratch/bob/jari/tmp/pristine/yat-0.10.x/yat/regression/OneDimensional.h>

Inheritance diagram for theplu::yat::regression::OneDimensional:

Public Member Functions
	OneDimensional (void)
	The default constructor.
virtual	~OneDimensional (void)
	The destructor.
double	chisq (void) const
	Chi-squared.
virtual void	fit (const utility::VectorBase &x, const utility::VectorBase &y)=0
virtual double	predict (const double x) const =0
double	prediction_error2 (const double x) const
std::ostream &	print (std::ostream &os, const double min, double max, const unsigned int n) const
	print output to ostream os
double	r2 (void) const
virtual double	s2 (void) const =0
virtual double	standard_error2 (const double x) const =0

Protected Member Functions
double	variance (void) const

Protected Attributes
statistics::AveragerPair	ap_
double	chisq_

Detailed Description

Interface Class for One Dimensional fitting.

See also:: OneDimensionalWeighted.

Member Function Documentation

double theplu::yat::regression::OneDimensional::chisq ( void ) const

Chi-squared.

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

virtual void theplu::yat::regression::OneDimensional::fit	(	const utility::VectorBase &	x,
		const utility::VectorBase &	y
	)

pure virtual

This function computes the best-fit given a model (see specific class for details) by minimizing $\sum{(\hat{y_i}-y_i)^2}$ , where $\hat{y}$ is the fitted value.

Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Polynomial, and theplu::yat::regression::Naive.

virtual double theplu::yat::regression::OneDimensional::predict ( const double x ) const

pure virtual

Returns:: expected value in x accrding to the fitted model

Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Polynomial, and theplu::yat::regression::Naive.

double theplu::yat::regression::OneDimensional::prediction_error2 ( const double x ) const

The prediction error is defined as the expected squared deviation a new data point will have from value the model provides: $E(Y|x - \hat{y}(x))^2$ 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 $ .

Returns:: expected squared prediction error for a new data point in x

std::ostream& theplu::yat::regression::OneDimensional::print	(	std::ostream &	os,
		const double	min,
		double	max,
		const unsigned int	n
	)		const

print output to ostream os

Printing estimated model to os in the points defined by min, max, and n. The values printed for each point is the x-value, the estimated y-value, and the estimated standard deviation of a new data poiunt will have from the y-value given the x-value (see prediction_error()).

Parameters:

os	Ostream printout is sent to
n	number of points printed
min	smallest x-value for which the model is printed
max	largest x-value for which the model is printed

double theplu::yat::regression::OneDimensional::r2 ( void ) const

r2 is defined as $1 - \frac{Var(Y|x)}{Var(Y)}$ or the fraction of the variance explained by the regression model.

See also:: s2()

virtual double theplu::yat::regression::OneDimensional::s2 ( void ) const

pure virtual

$ E(Y|x - E(Y|x))^2 $

  @return Conditional variance of Y

Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Polynomial, and theplu::yat::regression::Naive.

virtual double theplu::yat::regression::OneDimensional::standard_error2 ( const double x ) const

pure virtual

The standard error is defined as $E(Y|x - \hat{y}(x))^2$

Returns:: expected squared error of model value in x

Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Polynomial, and theplu::yat::regression::Naive.

double theplu::yat::regression::OneDimensional::variance ( void ) const

protected

Variance of y

Member Data Documentation

statistics::AveragerPair theplu::yat::regression::OneDimensional::ap_

protected

Averager for pair of x and y

double theplu::yat::regression::OneDimensional::chisq_

protected

See also:: chisq()

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

/scratch/bob/jari/tmp/pristine/yat-0.10.x/yat/regression/OneDimensional.h

Public Member Functions

Protected Member Functions

Protected Attributes

Detailed Description

Member Function Documentation

Member Data Documentation