OpenMS  2.7.0
Public Member Functions | Static Public Member Functions | Protected Member Functions | Protected Attributes | Private Member Functions | List of all members
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>

Collaboration diagram for LinearRegression:
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Public Member Functions

 LinearRegression ()
 Constructor. More...
 
virtual ~LinearRegression ()=default
 Destructor. More...
 
template<typename Iterator >
void computeRegression (double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin, bool compute_goodness=true)
 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, bool compute_goodness=true)
 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...
 

Static Public Member Functions

static double computePointY (double x, double slope, double intercept)
 given x compute y = slope * x + intercept 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() [1/2]

LinearRegression ( )
inline

Constructor.

◆ ~LinearRegression()

virtual ~LinearRegression ( )
virtualdefault

Destructor.

◆ LinearRegression() [2/2]

LinearRegression ( const LinearRegression arg)
private

Not implemented.

Member Function Documentation

◆ computeChiSquare()

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

Compute the chi squared of a linear fit.

References LinearRegression::computePointY().

Referenced by LinearRegression::computeRegression().

◆ computeGoodness_()

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

Computes the goodness of the fitted regression line.

Referenced by LinearRegression::computeRegression(), and LinearRegression::computeRegressionWeighted().

◆ computePointY()

static double computePointY ( double  x,
double  slope,
double  intercept 
)
inlinestatic

given x compute y = slope * x + intercept

Referenced by LinearRegression::computeChiSquare(), and LinearRegression::computeWeightedChiSquare().

◆ computeRegression()

void computeRegression ( double  confidence_interval_P,
Iterator  x_begin,
Iterator  x_end,
Iterator  y_begin,
bool  compute_goodness = true 
)

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.

Parameters
confidence_interval_PValue between 0-1 to determine lower and upper CI borders.
x_beginBegin iterator of x values
x_endEnd iterator of x values
y_beginBegin iterator of y values (same length as x)
compute_goodnessCompute meta stats about the fit. If this is not done, none of the members (except slope and intercept) are meaningful.
Returns
If an error occurred during the fit.
Exceptions
Exception::UnableToFitis thrown if fitting cannot be performed

References LinearRegression::chi_squared_, LinearRegression::computeChiSquare(), LinearRegression::computeGoodness_(), LinearRegression::intercept_, OpenMS::Math::iteratorRange2Wm5Vectors(), and LinearRegression::slope_.

◆ computeRegressionWeighted()

void computeRegressionWeighted ( double  confidence_interval_P,
Iterator  x_begin,
Iterator  x_end,
Iterator  y_begin,
Iterator  w_begin,
bool  compute_goodness = true 
)

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.

Parameters
confidence_interval_PValue between 0-1 to determine lower and upper CI borders.
x_beginBegin iterator of x values
x_endEnd iterator of x values
y_beginBegin iterator of y values (same length as x)
w_beginBegin iterator of weight values (same length as x)
compute_goodnessCompute meta stats about the fit. If this is not done, none of the members (except slope and intercept) are meaningful.
Returns
If an error occurred during the fit.
Exceptions
Exception::UnableToFitis thrown if fitting cannot be performed

References LinearRegression::chi_squared_, LinearRegression::computeGoodness_(), LinearRegression::computeWeightedChiSquare(), LinearRegression::intercept_, OpenMS::Math::iteratorRange2Wm5Vectors(), and LinearRegression::slope_.

◆ computeWeightedChiSquare()

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.

References LinearRegression::computePointY().

Referenced by LinearRegression::computeRegressionWeighted().

◆ getChiSquared()

double getChiSquared ( ) const

Non-mutable access to the chi squared value.

◆ getIntercept()

double getIntercept ( ) const

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

◆ getLower()

double getLower ( ) const

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

◆ getMeanRes()

double getMeanRes ( ) const

Non-mutable access to the residual mean.

◆ getRSD()

double getRSD ( ) const

Non-mutable access to relative standard deviation.

◆ getRSquared()

double getRSquared ( ) const

Non-mutable access to the squared Pearson coefficient.

◆ getSlope()

double getSlope ( ) const

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

◆ getStandDevRes()

double getStandDevRes ( ) const

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

◆ getStandErrSlope()

double getStandErrSlope ( ) const

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

◆ getTValue()

double getTValue ( ) const

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

◆ getUpper()

double getUpper ( ) const

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

◆ getXIntercept()

double getXIntercept ( ) const

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

◆ operator=()

LinearRegression& operator= ( const LinearRegression arg)
private

Not implemented.

Member Data Documentation

◆ chi_squared_

double chi_squared_
protected

The value of the Chi Squared statistic.

Referenced by LinearRegression::computeRegression(), and LinearRegression::computeRegressionWeighted().

◆ intercept_

double intercept_
protected

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

Referenced by LinearRegression::computeRegression(), and LinearRegression::computeRegressionWeighted().

◆ lower_

double lower_
protected

The lower bound of the confidence interval.

◆ mean_residuals_

double mean_residuals_
protected

Mean of residuals.

◆ r_squared_

double r_squared_
protected

The squared correlation coefficient (Pearson)

◆ rsd_

double rsd_
protected

the relative standard deviation

◆ slope_

double slope_
protected

◆ stand_dev_residuals_

double stand_dev_residuals_
protected

The standard deviation of the residuals.

◆ stand_error_slope_

double stand_error_slope_
protected

The standard error of the slope.

◆ t_star_

double t_star_
protected

The value of the t-statistic.

◆ upper_

double upper_
protected

The upper bound of the confidence interval.

◆ x_intercept_

double x_intercept_
protected

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