78 stand_dev_residuals_(0),
80 stand_error_slope_(0),
111 std::vector<double>::const_iterator x_begin,
112 std::vector<double>::const_iterator x_end,
113 std::vector<double>::const_iterator y_begin,
114 bool compute_goodness =
true);
139 std::vector<double>::const_iterator x_begin,
140 std::vector<double>::const_iterator x_end,
141 std::vector<double>::const_iterator y_begin,
142 std::vector<double>::const_iterator w_begin,
143 bool compute_goodness =
true);
171 static inline double computePointY(
double x,
double slope,
double intercept)
173 return slope * x + intercept;
204 void computeGoodness_(
const std::vector<double>& X,
const std::vector<double>& Y,
double confidence_interval_P);
207 template <
typename Iterator>
211 template <
typename Iterator>
224 template <
typename Iterator>
227 double chi_squared = 0.0;
230 for (; xIter != x_end; ++xIter, ++yIter)
232 chi_squared += std::pow(*yIter -
computePointY(*xIter, slope, intercept), 2);
239 template <
typename Iterator>
242 double chi_squared = 0.0;
246 for (; xIter != x_end; ++xIter, ++yIter, ++wIter)
248 chi_squared += *wIter * std::pow(*yIter -
computePointY(*xIter, slope, intercept), 2);
This class offers functions to perform least-squares fits to a straight line model,...
Definition: LinearRegression.h:66
void computeRegressionWeighted(double confidence_interval_P, std::vector< double >::const_iterator x_begin, std::vector< double >::const_iterator x_end, std::vector< double >::const_iterator y_begin, std::vector< double >::const_iterator w_begin, bool compute_goodness=true)
This function computes the best-fit linear regression coefficients of the model for the weighted da...
double r_squared_
The squared correlation coefficient (Pearson)
Definition: LinearRegression.h:191
double getRSquared() const
Non-mutable access to the squared Pearson coefficient.
double getIntercept() const
Non-mutable access to the y-intercept of the straight line.
double x_intercept_
The intercept of the fitted line with the x-axis.
Definition: LinearRegression.h:183
double getUpper() const
Non-mutable access to the upper border of confidence interval.
LinearRegression()
Constructor.
Definition: LinearRegression.h:70
double lower_
The lower bound of the confidence interval.
Definition: LinearRegression.h:185
virtual ~LinearRegression()=default
Destructor.
void computeRegression(double confidence_interval_P, std::vector< double >::const_iterator x_begin, std::vector< double >::const_iterator x_end, std::vector< double >::const_iterator y_begin, bool compute_goodness=true)
This function computes the best-fit linear regression coefficients of the model for the dataset .
double getXIntercept() const
Non-mutable access to the x-intercept of the straight line.
static double computePointY(double x, double slope, double intercept)
given x compute y = slope * x + intercept
Definition: LinearRegression.h:171
double upper_
The upper bound of the confidence interval.
Definition: LinearRegression.h:187
LinearRegression & operator=(const LinearRegression &arg)
Not implemented.
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.
Definition: LinearRegression.h:240
double t_star_
The value of the t-statistic.
Definition: LinearRegression.h:189
double getRSD() const
Non-mutable access to relative standard deviation.
double computeChiSquare(Iterator x_begin, Iterator x_end, Iterator y_begin, double slope, double intercept)
Compute the chi squared of a linear fit.
Definition: LinearRegression.h:225
double getTValue() const
Non-mutable access to the value of the t-distribution.
double getStandErrSlope() const
Non-mutable access to the standard error of the slope.
double getSlope() const
Non-mutable access to the slope of the straight line.
double getChiSquared() const
Non-mutable access to the chi squared value.
double chi_squared_
The value of the Chi Squared statistic.
Definition: LinearRegression.h:199
double intercept_
The intercept of the fitted line with the y-axis.
Definition: LinearRegression.h:179
double slope_
The slope of the fitted line.
Definition: LinearRegression.h:181
double getLower() const
Non-mutable access to the lower border of confidence interval.
void computeGoodness_(const std::vector< double > &X, const std::vector< double > &Y, double confidence_interval_P)
Computes the goodness of the fitted regression line.
double mean_residuals_
Mean of residuals.
Definition: LinearRegression.h:195
double stand_dev_residuals_
The standard deviation of the residuals.
Definition: LinearRegression.h:193
LinearRegression(const LinearRegression &arg)
Not implemented.
double rsd_
the relative standard deviation
Definition: LinearRegression.h:201
double getMeanRes() const
Non-mutable access to the residual mean.
double stand_error_slope_
The standard error of the slope.
Definition: LinearRegression.h:197
double getStandDevRes() const
Non-mutable access to the standard deviation of the residuals.
Main OpenMS namespace.
Definition: FeatureDeconvolution.h:48