#include <yat/regression/Linear.h>
Public Member Functions | |
Linear (void) | |
The default constructor. | |
virtual | ~Linear (void) |
The destructor. | |
double | alpha (void) const |
double | alpha_var (void) const |
double | beta (void) const |
double | beta_var (void) const |
void | fit (const utility::VectorBase &x, const utility::VectorBase &y) |
double | predict (const double x) const |
double | s2 (void) const |
double | standard_error2 (const double x) const |
double | chisq (void) const |
Chi-squared. | |
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 |
Protected Member Functions | |
double | variance (void) const |
Protected Attributes | |
statistics::AveragerPair | ap_ |
double | chisq_ |
Data are modeled as .
double theplu::yat::regression::Linear::alpha | ( | void | ) | const |
The parameter is estimated as
double theplu::yat::regression::Linear::alpha_var | ( | void | ) | const |
The variance is estimated as where
double theplu::yat::regression::Linear::beta | ( | void | ) | const |
The parameter is estimated as
double theplu::yat::regression::Linear::beta_var | ( | void | ) | const |
The variance is estimated as where
double theplu::yat::regression::OneDimensional::chisq | ( | void | ) | const [inherited] |
Chi-squared.
Chi-squared is defined as the
void theplu::yat::regression::Linear::fit | ( | const utility::VectorBase & | x, | |
const utility::VectorBase & | y | |||
) | [virtual] |
Model is fitted by minimizing . By construction and are independent.
Implements theplu::yat::regression::OneDimensional.
double theplu::yat::regression::Linear::predict | ( | const double | x | ) | const [virtual] |
double theplu::yat::regression::OneDimensional::prediction_error2 | ( | const double | x | ) | const [inherited] |
The prediction error is defined as the expected squared deviation a new data point will have from value the model provides: and is typically divided into the conditional variance ( see s2() ) given and the squared standard error ( see standard_error2() ) of the model estimation in .
std::ostream& theplu::yat::regression::OneDimensional::print | ( | std::ostream & | os, | |
const double | min, | |||
double | max, | |||
const unsigned int | n | |||
) | const [inherited] |
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()).
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 [inherited] |
double theplu::yat::regression::Linear::s2 | ( | void | ) | const [virtual] |
double theplu::yat::regression::Linear::standard_error2 | ( | const double | x | ) | const [virtual] |
The error of the model is estimated as
Implements theplu::yat::regression::OneDimensional.
double theplu::yat::regression::OneDimensional::variance | ( | void | ) | const [protected, inherited] |
Variance of y
statistics::AveragerPair theplu::yat::regression::OneDimensional::ap_ [protected, inherited] |
Averager for pair of x and y
double theplu::yat::regression::OneDimensional::chisq_ [protected, inherited] |