yat
0.8.3pre
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Interface Class for One Dimensional fitting. More...
#include <yat/regression/OneDimensional.h>
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_ |
Interface Class for One Dimensional fitting.
double theplu::yat::regression::OneDimensional::chisq | ( | void | ) | const |
Chi-squared.
Chi-squared is defined as the
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pure virtual |
This function computes the best-fit given a model (see specific class for details) by minimizing , where is the fitted value.
Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Polynomial, and theplu::yat::regression::Naive.
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pure virtual |
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: 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 |
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 |
r2 is defined as or the fraction of the variance explained by the regression model.
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pure virtual |
@return Conditional variance of Y
Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Polynomial, and theplu::yat::regression::Naive.
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pure virtual |
The standard error is defined as
Implemented in theplu::yat::regression::Linear, theplu::yat::regression::Polynomial, and theplu::yat::regression::Naive.
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protected |
Variance of y
Averager for pair of x and y
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protected |