libopenms-dev 1.11.1-5 / usr / include / OpenMS / MATH / STATISTICS / LinearRegression.h

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// $Maintainer: Clemens Groepl $
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#ifndef OPENMS_MATH_STATISTICS_LINEARREGRESSION_H
#define OPENMS_MATH_STATISTICS_LINEARREGRESSION_H

#include <OpenMS/CONCEPT/Types.h>
#include <OpenMS/CONCEPT/Exception.h>

#include <iostream>
#include <vector>
#include <gsl/gsl_fit.h>
#include <gsl/gsl_statistics.h>
#include <gsl/gsl_cdf.h>

namespace OpenMS
{
  namespace Math
  {
    /**
            @brief This class offers functions to perform least-squares fits to a straight line model, \f$ Y(c,x) = c_0 + c_1 x \f$.

            It capsulates the GSL methods for a weighted and an unweighted linear regression.

            Next to the intercept with the y-axis and the slope of the fitted line, this class computes the:
            - squared pearson coefficient
            - value of the t-distribution
            - standard deviation of the residuals
            - standard error of the slope
            - intercept with the x-axis (useful for additive series experiments)
            - lower border of confidence interval
            - higher border of confidence interval
            - chi squared value
            - x mean

            @ingroup Math
    */
    class OPENMS_DLLAPI LinearRegression
    {
public:

      /// Constructor
      LinearRegression() :
        intercept_(0),
        slope_(0),
        x_intercept_(0),
        lower_(0),
        upper_(0),
        t_star_(0),
        r_squared_(0),
        stand_dev_residuals_(0),
        mean_residuals_(0),
        stand_error_slope_(0),
        chi_squared_(0),
        rsd_(0)
      {
      }

      /// Destructor
      virtual ~LinearRegression()
      {
      }

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

          The values in x-dimension of the dataset \f$ (x,y) \f$ 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.

          @return If an error occured during the fit.

          @exception Exception::UnableToFit is thrown if fitting cannot be performed
      */
      template <typename Iterator>
      void computeRegression(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin);

      /**
          @brief This function computes the best-fit linear regression coefficient \f$ (c_0) \f$
          of the model \f$ Y = c_1 X \f$ for the dataset \f$ (x, y) \f$.

          The values in x-dimension of the dataset \f$ (x,y) \f$ 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.

          @return If an error occured during the fit.

          @exception Exception::UnableToFit is thrown if fitting cannot be performed
      */
      template <typename Iterator>
      void computeRegressionNoIntercept(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin);

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

          The values in x-dimension of the dataset \f$ (x, y) \f$ 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.

          @return If an error occured during the fit.

          @exception Exception::UnableToFit is thrown if fitting cannot be performed
      */
      template <typename Iterator>
      void computeRegressionWeighted(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin);


      /// Non-mutable access to the y-intercept of the straight line
      DoubleReal getIntercept() const;
      /// Non-mutable access to the slope of the straight line
      DoubleReal getSlope() const;
      /// Non-mutable access to the x-intercept of the straight line
      DoubleReal getXIntercept() const;
      /// Non-mutable access to the lower border of confidence interval
      DoubleReal getLower() const;
      /// Non-mutable access to the upper border of confidence interval
      DoubleReal getUpper() const;
      /// Non-mutable access to the value of the t-distribution
      DoubleReal getTValue() const;
      /// Non-mutable access to the squared pearson coefficient
      DoubleReal getRSquared() const;
      /// Non-mutable access to the standard deviation of the residuals
      DoubleReal getStandDevRes() const;
      /// Non-mutable access to the residual mean
      DoubleReal getMeanRes() const;
      /// Non-mutable access to the standard error of the slope
      DoubleReal getStandErrSlope() const;
      /// Non-mutable access to the chi squared value
      DoubleReal getChiSquared() const;
      /// Non-mutable access to relelative standard deviation
      DoubleReal getRSD() const;

protected:

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


      /// Computes the goodness of the fitted regression line
      void computeGoodness_(double * X, double * Y, int N, double confidence_interval_P);

      /// Copies the distance(x_begin,x_end) elements starting at x_begin and y_begin into the arrays x_array and y_array
      template <typename Iterator>
      void iteratorRange2Arrays_(Iterator x_begin, Iterator x_end, Iterator y_begin, double * x_array, double * y_array);

      /// Copy the distance(x_begin,x_end) elements starting at  x_begin, y_begin and w_begin into the arrays x_array, y_array and w_array
      template <typename Iterator>
      void iteratorRange3Arrays_(Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin, double * x_array, double * y_array, double * w_array);

private:

      /// Not implemented
      LinearRegression(const LinearRegression & arg);
      /// Not implemented
      LinearRegression & operator=(const LinearRegression & arg);
    };

    template <typename Iterator>
    void LinearRegression::computeRegression(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin)
    {
      int N = int(distance(x_begin, x_end));

      double * X = new double[N];
      double * Y = new double[N];
      iteratorRange2Arrays_(x_begin, x_end, y_begin, X, Y);

      double cov00, cov01, cov11;

      // Compute the unweighted linear fit.
      // Get the intercept and the slope of the regression Y_hat=intercept_+slope_*X
      // and the value of Chi squared, the covariances of the intercept and the slope
      int error = gsl_fit_linear(X, 1, Y, 1, N, &intercept_, &slope_, &cov00, &cov01, &cov11, &chi_squared_);

      if (!error)
      {
        computeGoodness_(X, Y, N, confidence_interval_P);
      }

      delete[] X;
      delete[] Y;

      if (error)
      {
        throw Exception::UnableToFit(__FILE__, __LINE__, __PRETTY_FUNCTION__, "UnableToFit-LinearRegression", "Could not fit a linear model to the data");
      }
    }

    template <typename Iterator>
    void LinearRegression::computeRegressionNoIntercept(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin)
    {
      int N = int(distance(x_begin, x_end));

      double * X = new double[N];
      double * Y = new double[N];
      iteratorRange2Arrays_(x_begin, x_end, y_begin, X, Y);

      double cov;

      // Compute the linear fit.
      // Get the intercept and the slope of the regression Y_hat=intercept_+slope_*X
      // and the value of Chi squared, the covariances of the intercept and the slope
      int error = gsl_fit_mul(X, 1, Y, 1, N, &slope_, &cov, &chi_squared_);
      intercept_ = 0.0;

      if (!error)
      {
        computeGoodness_(X, Y, N, confidence_interval_P);
      }

      delete[] X;
      delete[] Y;

      if (error)
      {
        throw Exception::UnableToFit(__FILE__, __LINE__, __PRETTY_FUNCTION__, "UnableToFit-LinearRegression", "Could not fit a linear model to the data");
      }
    }

    template <typename Iterator>
    void LinearRegression::computeRegressionWeighted(double confidence_interval_P, Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin)
    {
      int N = int(distance(x_begin, x_end));

      double * X = new double[N];
      double * Y = new double[N];
      double * W = new double[N];
      iteratorRange3Arrays_(x_begin, x_end, y_begin, w_begin, X, Y, W);

      double cov00, cov01, cov11;

      // Compute the weighted linear fit.
      // Get the intercept and the slope of the regression Y_hat=intercept_+slope_*X
      // and the value of Chi squared, the covariances of the intercept and the slope
      int error = gsl_fit_wlinear(X, 1, W, 1, Y, 1, N, &intercept_, &slope_, &cov00, &cov01, &cov11, &chi_squared_);

      if (!error)
      {
        computeGoodness_(X, Y, N, confidence_interval_P);
      }

      delete[] X;
      delete[] Y;
      delete[] W;

      if (error)
      {
        throw Exception::UnableToFit(__FILE__, __LINE__, __PRETTY_FUNCTION__, "UnableToFit-LinearRegression", "Could not fit a linear model to the data");
      }
    }

    template <typename Iterator>
    void LinearRegression::iteratorRange2Arrays_(Iterator x_begin, Iterator x_end, Iterator y_begin, double * x_array, double * y_array)
    {
      int i = 0;
      while (x_begin < x_end)
      {
        x_array[i] = *x_begin;
        y_array[i] = *y_begin;
        ++x_begin;
        ++y_begin;
        ++i;
      }
    }

    template <typename Iterator>
    void LinearRegression::iteratorRange3Arrays_(Iterator x_begin, Iterator x_end, Iterator y_begin, Iterator w_begin, double * x_array, double * y_array, double * w_array)
    {
      int i = 0;
      while (x_begin < x_end)
      {
        x_array[i] = *x_begin;
        y_array[i] = *y_begin;
        w_array[i] = *w_begin;
        ++x_begin;
        ++y_begin;
        ++w_begin;
        ++i;
      }
    }

  } // namespace Math
} // namespace OpenMS


#endif