theoretica::regression::linear_model Class Reference

structure for computation and storage of least squares linear regression results with model \(y = A + Bx\). More...

Public Member Functions
	linear_model ()
	Default constructor.
template<typename Dataset1 , typename Dataset2 >
	linear_model (const Dataset1 &X, const Dataset2 &Y)
	Construct a linear model from data and compute the fit.
template<typename Dataset1 , typename Dataset2 >
	linear_model (const Dataset1 &X, const Dataset2 &Y, real sigma_Y)
	Construct a linear model from data and compute the fit.
template<typename Dataset1 , typename Dataset2 , typename Dataset3 >
	linear_model (const Dataset1 &X, const Dataset2 &Y, const Dataset3 &sigma_Y)
	Construct a linear model from data and compute the fit.
template<typename Dataset1 , typename Dataset2 >
	linear_model (const Dataset1 &X, const Dataset2 &Y, real sigma_X, real sigma_Y)
	Construct a linear model from data and compute the fit.
template<typename Dataset1 , typename Dataset2 >
void	fit (const Dataset1 &X, const Dataset2 &Y)
	Compute the linear regression of two sets of data of the same size using ordinary least squares linear regression. More...
template<typename Dataset1 , typename Dataset2 >
void	fit (const Dataset1 &X, const Dataset2 &Y, real sigma_Y)
	Compute the linear regression of two sets of data of the same size using ordinary least squares linear regression. More...
template<typename Dataset1 , typename Dataset2 , typename Dataset3 >
void	fit (const Dataset1 &X, const Dataset2 &Y, const Dataset3 &sigma)
	Compute the linear regression of two sets of data of the same size using least squares linear regression. More...
template<typename Dataset1 , typename Dataset2 >
void	fit (const Dataset1 &X, const Dataset2 &Y, real sigma_X, real sigma_Y)
	Compute the linear regression of two sets of data of the same size using least squares linear regression. More...
real	operator() (real x)
	Compute the expected Y value for the given X value, following the computed model.
std::string	to_string () const
	Convert the vector to string representation.
	operator std::string ()
	Convert the vector to string representation.

Public Attributes
real	A
	Intercept.
real	sigma_A
	Estimated error on A.
real	B
	Slope.
real	sigma_B
	Estimated error on B.
mat2	covar_mat
	Covariance matrix of the coefficients A and B.
real	err
	Total error on linearization.
real	chi_squared
	Chi-squared on linearization.
unsigned int	ndf
	Number of degrees of freedom of the linear regression.
real	p_value
	The p-value associated to the computed Chi-squared.

Friends
std::ostream &	operator<< (std::ostream &out, const linear_model &obj)
	Stream the vector in string representation to an output stream (std::ostream)

Detailed Description

structure for computation and storage of least squares linear regression results with model \(y = A + Bx\).

Member Function Documentation

fit() [1/4]

template<typename Dataset1 , typename Dataset2 >

void theoretica::regression::linear_model::fit ( const Dataset1 &  X, const Dataset2 &  Y  )

inline

Compute the linear regression of two sets of data of the same size using ordinary least squares linear regression.

Without the error on the y axis, the chi-squared and the error on the coefficients cannot be computed.

Parameters

X	The set of values on the x axis
Y	The set of values on the y axis

fit() [2/4]

template<typename Dataset1 , typename Dataset2 , typename Dataset3 >

void theoretica::regression::linear_model::fit ( const Dataset1 &  X, const Dataset2 &  Y, const Dataset3 &  sigma  )

inline

Compute the linear regression of two sets of data of the same size using least squares linear regression.

Parameters

X	The set of values on the x axis
Y	The set of values on the y axis
sigma	The different errors on the y axis

fit() [3/4]

template<typename Dataset1 , typename Dataset2 >

void theoretica::regression::linear_model::fit ( const Dataset1 &  X, const Dataset2 &  Y, real  sigma_X, real  sigma_Y  )

inline

Compute the linear regression of two sets of data of the same size using least squares linear regression.

Parameters

X	The set of values on the x axis
Y	The set of values on the y axis
sigma	The different errors on the y axis

fit() [4/4]

template<typename Dataset1 , typename Dataset2 >

void theoretica::regression::linear_model::fit ( const Dataset1 &  X, const Dataset2 &  Y, real  sigma_Y  )

inline

Compute the linear regression of two sets of data of the same size using ordinary least squares linear regression.

Parameters

X	The set of values on the x axis
Y	The set of values on the y axis
sigma_Y	The constant error on the y axis

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

• statistics/regression.h