Regression Class Reference

This is the base class used to fit a regression line to compare 2 vectors. More...

#include <regressionline.h>

Inheritance diagram for Regression:

Collaboration diagram for Regression:

Public Member Functions
	Regression ()
	This is the default Regression constructor. More...
	Regression (LineType ltype)
	This is the Regression constructor for a regression line of a specified LineType. More...
virtual	~Regression ()
	This is the default Regression destructor. More...
virtual void	storeVectors (const DoubleVector &modData, const DoubleVector &obsData)=0
	This is the function that stores 2 vectors that will be compared using a regression line. More...
void	calcFit ()
	This is the function that fits a regression line to compare the 2 vectors that have been stored, according to the LineType that has been defined. More...
void	setIntercept (double intercept)
	This function will set the intercept of the regression line. More...
void	setSlope (double slope)
	This function will set the slope of the regression line. More...
void	setWeights (const DoubleVector &weights)
	This function will set the weights that can be used to fit the regression line. More...
int	getError ()
	This function will check to see if an error has occured. More...
double	getSSE ()
	This function will return the sum of squares of errors calculated when fitting the regression line. More...
double	getIntercept ()
	This function will return the intercept of the regression line. More...
double	getSlope ()
	This function will return the slope of the regression line. More...
LineType	getType () const
	This function will return the fit type for the regression line. More...

Protected Member Functions
void	calcSSE ()
	This function will calculate the sum of squares of errors for the regession line. More...
void	calcSSEWeights ()
	This function will calculate the weighted sum of squares of errors for the regession line. More...
void	calcSlope ()
	This function will calculate the slope of the regession line. More...
void	calcIntercept ()
	This function will calculate the intercept of the regession line. More...
void	calcSlopeIntercept ()
	This function will calculate both the slope and the intercept of the regession line. More...

Protected Attributes
int	error
	This is the flag to denote whether an error has occured. More...
int	useweights
	This is the flag to denote whether the weights should be used when calculating the fit to the regression line. More...
double	sse
	This is the sum of squares of errors from the regression line. More...
double	a
	This is the intercept of the regression line. More...
double	b
	This is the slope of the regression line. More...
DoubleVector	w
	This is the DoubleVector of weights that can be used to fit the regression line. More...
DoubleVector	x
	This is the DoubleVector that will contain the the modelled data to be used to fit the regression line. More...
DoubleVector	y
	This is the DoubleVector that will contain the the observed data to be used to fit the regression line. More...
LineType	linetype
	This denotes what type of line fit is to be used for the regression line. More...

Detailed Description

This is the base class used to fit a regression line to compare 2 vectors.

Note

This will always be overridden by the derived classes that actually fit the regression line

Constructor & Destructor Documentation

Regression() [1/2]

Regression::Regression

(

)

This is the default Regression constructor.

Regression() [2/2]

Regression::Regression

(

LineType

ltype

)

This is the Regression constructor for a regression line of a specified LineType.

Parameters

ltype

is the LineType of the regression line

~Regression()

virtual Regression::~Regression ( )

inlinevirtual

This is the default Regression destructor.

Member Function Documentation

calcFit()

void Regression::calcFit

(

)

This is the function that fits a regression line to compare the 2 vectors that have been stored, according to the LineType that has been defined.

calcIntercept()

void Regression::calcIntercept ( )

protected

This function will calculate the intercept of the regession line.

calcSlope()

void Regression::calcSlope ( )

protected

This function will calculate the slope of the regession line.

calcSlopeIntercept()

void Regression::calcSlopeIntercept ( )

protected

This function will calculate both the slope and the intercept of the regession line.

calcSSE()

void Regression::calcSSE ( )

protected

This function will calculate the sum of squares of errors for the regession line.

calcSSEWeights()

void Regression::calcSSEWeights ( )

protected

This function will calculate the weighted sum of squares of errors for the regession line.

getError()

int Regression::getError ( )

inline

This function will check to see if an error has occured.

Returns

error

getIntercept()

double Regression::getIntercept ( )

inline

This function will return the intercept of the regression line.

Returns

a

getSlope()

double Regression::getSlope ( )

inline

This function will return the slope of the regression line.

Returns

b

getSSE()

double Regression::getSSE

(

)

This function will return the sum of squares of errors calculated when fitting the regression line.

Returns

sse

getType()

LineType Regression::getType ( ) const

inline

This function will return the fit type for the regression line.

Returns

fittype

setIntercept()

void Regression::setIntercept ( double  intercept )

inline

This function will set the intercept of the regression line.

Parameters

intercept

is the intercept of the regression line

setSlope()

void Regression::setSlope ( double  slope )

inline

This function will set the slope of the regression line.

Parameters

slope

is the slope of the regession line

setWeights()

void Regression::setWeights

(

const DoubleVector &

weights

)

This function will set the weights that can be used to fit the regression line.

Parameters

weights

is the DoubleVector of weights to be used

storeVectors()

virtual void Regression::storeVectors ( const DoubleVector &  modData, const DoubleVector &  obsData  )

pure virtual

This is the function that stores 2 vectors that will be compared using a regression line.

Parameters

modData	is the DoubleVector containing the modelled data
obsData	is the DoubleVector containing the observed data

Implemented in LogWeightRegression, WeightRegression, LogLinearRegression, and LinearRegression.

Member Data Documentation

a

double Regression::a

protected

This is the intercept of the regression line.

b

double Regression::b

protected

This is the slope of the regression line.

error

int Regression::error

protected

This is the flag to denote whether an error has occured.

linetype

LineType Regression::linetype

protected

This denotes what type of line fit is to be used for the regression line.

sse

double Regression::sse

protected

This is the sum of squares of errors from the regression line.

useweights

int Regression::useweights

protected

This is the flag to denote whether the weights should be used when calculating the fit to the regression line.

w

DoubleVector Regression::w

protected

This is the DoubleVector of weights that can be used to fit the regression line.

x

DoubleVector Regression::x

protected

This is the DoubleVector that will contain the the modelled data to be used to fit the regression line.

y

DoubleVector Regression::y

protected

This is the DoubleVector that will contain the the observed data to be used to fit the regression line.

---

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

• src/include/regressionline.h
• src/regressionline.cc