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LinearRegression Class Reference

This class offers functions to perform least-squares fits to a straight line model, $ Y(c,x) = c_0 + c_1 x $. More...

#include <OpenMS/MATH/STATISTICS/LinearRegression.h>

Public Member Functions

 LinearRegression ()
 Constructor. More...
 
virtual ~LinearRegression ()
 Destructor. More...
 
template<typename Iterator >
void computeRegression (double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin)
 This function computes the best-fit linear regression coefficients $ (c_0,c_1) $ of the model $ Y = c_0 + c_1 X $ for the dataset $ (x, y) $. More...
 
template<typename Iterator >
void computeRegressionWeighted (double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin)
 This function computes the best-fit linear regression coefficients $ (c_0,c_1) $ of the model $ Y = c_0 + c_1 X $ for the weighted dataset $ (x, y) $. More...
 
double getIntercept () const
 Non-mutable access to the y-intercept of the straight line. More...
 
double getSlope () const
 Non-mutable access to the slope of the straight line. More...
 
double getXIntercept () const
 Non-mutable access to the x-intercept of the straight line. More...
 
double getLower () const
 Non-mutable access to the lower border of confidence interval. More...
 
double getUpper () const
 Non-mutable access to the upper border of confidence interval. More...
 
double getTValue () const
 Non-mutable access to the value of the t-distribution. More...
 
double getRSquared () const
 Non-mutable access to the squared Pearson coefficient. More...
 
double getStandDevRes () const
 Non-mutable access to the standard deviation of the residuals. More...
 
double getMeanRes () const
 Non-mutable access to the residual mean. More...
 
double getStandErrSlope () const
 Non-mutable access to the standard error of the slope. More...
 
double getChiSquared () const
 Non-mutable access to the chi squared value. More...
 
double getRSD () const
 Non-mutable access to relative standard deviation. More...
 

Protected Member Functions

void computeGoodness_ (const std::vector< Wm5::Vector2d > &points, double confidence_interval_P)
 Computes the goodness of the fitted regression line. More...
 
template<typename Iterator >
double computeChiSquare (Iterator x_begin, Iterator x_end, Iterator y_begin, double slope, double intercept)
 Compute the chi squared of a linear fit. More...
 
template<typename Iterator >
double computeWeightedChiSquare (Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin, double slope, double intercept)
 Compute the chi squared of a weighted linear fit. More...
 

Protected Attributes

double intercept_
 The intercept of the fitted line with the y-axis. More...
 
double slope_
 The slope of the fitted line. More...
 
double x_intercept_
 The intercept of the fitted line with the x-axis. More...
 
double lower_
 The lower bound of the confidence interval. More...
 
double upper_
 The upper bound of the confidence interval. More...
 
double t_star_
 The value of the t-statistic. More...
 
double r_squared_
 The squared correlation coefficient (Pearson) More...
 
double stand_dev_residuals_
 The standard deviation of the residuals. More...
 
double mean_residuals_
 Mean of residuals. More...
 
double stand_error_slope_
 The standard error of the slope. More...
 
double chi_squared_
 The value of the Chi Squared statistic. More...
 
double rsd_
 the relative standard deviation More...
 

Private Member Functions

 LinearRegression (const LinearRegression &arg)
 Not implemented. More...
 
LinearRegressionoperator= (const LinearRegression &arg)
 Not implemented. More...
 

Detailed Description

This class offers functions to perform least-squares fits to a straight line model, $ Y(c,x) = c_0 + c_1 x $.

Next to the intercept with the y-axis and the slope of the fitted line, this class computes the:

Constructor & Destructor Documentation

LinearRegression ( )
inline

Constructor.

virtual ~LinearRegression ( )
inlinevirtual

Destructor.

LinearRegression ( const LinearRegression arg)
private

Not implemented.

Member Function Documentation

double computeChiSquare ( Iterator  x_begin,
Iterator  x_end,
Iterator  y_begin,
double  slope,
double  intercept 
)
protected

Compute the chi squared of a linear fit.

void computeGoodness_ ( const std::vector< Wm5::Vector2d > &  points,
double  confidence_interval_P 
)
protected

Computes the goodness of the fitted regression line.

void computeRegression ( double  confidence_interval_P,
Iterator  x_begin,
Iterator  x_end,
Iterator  y_begin 
)

This function computes the best-fit linear regression coefficients $ (c_0,c_1) $ of the model $ Y = c_0 + c_1 X $ for the dataset $ (x, y) $.

The values in x-dimension of the dataset $ (x,y) $ are given by the iterator range [x_begin,x_end) and the corresponding y-values start at position y_begin.

For a "x %" Confidence Interval use confidence_interval_P = x/100. For example the 95% Confidence Interval is supposed to be an interval that has a 95% chance of containing the true value of the parameter.

Returns
If an error occurred during the fit.
Exceptions
Exception::UnableToFitis thrown if fitting cannot be performed

References OpenMS::Math::iteratorRange2Wm5Vectors().

void computeRegressionWeighted ( double  confidence_interval_P,
Iterator  x_begin,
Iterator  x_end,
Iterator  y_begin,
Iterator  w_begin 
)

This function computes the best-fit linear regression coefficients $ (c_0,c_1) $ of the model $ Y = c_0 + c_1 X $ for the weighted dataset $ (x, y) $.

The values in x-dimension of the dataset $ (x, y) $ are given by the iterator range [x_begin,x_end) and the corresponding y-values start at position y_begin. They will be weighted by the values starting at w_begin.

For a "x %" Confidence Interval use confidence_interval_P = x/100. For example the 95% Confidence Interval is supposed to be an interval that has a 95% chance of containing the true value of the parameter.

Returns
If an error occurred during the fit.
Exceptions
Exception::UnableToFitis thrown if fitting cannot be performed

References OpenMS::Math::iteratorRange2Wm5Vectors().

double computeWeightedChiSquare ( Iterator  x_begin,
Iterator  x_end,
Iterator  y_begin,
Iterator  w_begin,
double  slope,
double  intercept 
)
protected

Compute the chi squared of a weighted linear fit.

double getChiSquared ( ) const

Non-mutable access to the chi squared value.

double getIntercept ( ) const

Non-mutable access to the y-intercept of the straight line.

double getLower ( ) const

Non-mutable access to the lower border of confidence interval.

double getMeanRes ( ) const

Non-mutable access to the residual mean.

double getRSD ( ) const

Non-mutable access to relative standard deviation.

double getRSquared ( ) const

Non-mutable access to the squared Pearson coefficient.

double getSlope ( ) const

Non-mutable access to the slope of the straight line.

double getStandDevRes ( ) const

Non-mutable access to the standard deviation of the residuals.

double getStandErrSlope ( ) const

Non-mutable access to the standard error of the slope.

double getTValue ( ) const

Non-mutable access to the value of the t-distribution.

double getUpper ( ) const

Non-mutable access to the upper border of confidence interval.

double getXIntercept ( ) const

Non-mutable access to the x-intercept of the straight line.

LinearRegression& operator= ( const LinearRegression arg)
private

Not implemented.

Member Data Documentation

double chi_squared_
protected

The value of the Chi Squared statistic.

double intercept_
protected

The intercept of the fitted line with the y-axis.

double lower_
protected

The lower bound of the confidence interval.

double mean_residuals_
protected

Mean of residuals.

double r_squared_
protected

The squared correlation coefficient (Pearson)

double rsd_
protected

the relative standard deviation

double slope_
protected

The slope of the fitted line.

double stand_dev_residuals_
protected

The standard deviation of the residuals.

double stand_error_slope_
protected

The standard error of the slope.

double t_star_
protected

The value of the t-statistic.

double upper_
protected

The upper bound of the confidence interval.

double x_intercept_
protected

The intercept of the fitted line with the x-axis.


OpenMS / TOPP release 2.0.0 Documentation generated on Tue Nov 1 2016 16:34:46 using doxygen 1.8.11