LinearRegression.h

//-*-C++-*-
/***************************************************************************
 *
 *   Copyright (C) 2024 by Willem van Straten
 *   Licensed under the Academic Free License version 2.1
 *
 ***************************************************************************/
 
// psrchive/Util/stat/LinearRegression.h
 
#ifndef __Util_stat_LinearRegression_h
#define __Util_stat_LinearRegression_h
 
#include "Estimate.h"
#include <vector>
 
class LinearRegression
{
public:


  float iterative_outlier_threshold = 0.0;

  bool subtract_weighted_mean_abscissa = false;

  Estimate<double> scale;
  
  Estimate<double> offset;

  double covariance = 0.0;

  double weighted_mean_abscissa = 0.0;

  double chisq = 0.0;

  unsigned nfree = 0;
  
  void ordinary_least_squares (const std::vector<double>& yval,
                               const std::vector<double>& xval,
                               const std::vector<bool>* mask = 0);


  void weighted_least_squares (const std::vector<double>& yval,
                               const std::vector<double>& xval,
                               const std::vector<double>& weight);


  void generalized_least_squares (const std::vector<double>& yval,
                                  const std::vector<double>& xval,
                                  const std::vector<double>& weight,
                                  const std::vector<double>& alpha);
 
  /* the inner loop of generalized_least_squares, which may iteratively remove outliers */
  void least_squares_worker (const std::vector<double>& yval,
                             const std::vector<double>& xval,
                             const std::vector<double>& weight,
                             const std::vector<double>& alpha);

  std::vector<double> masked_weights;
 
  protected:
    std::vector<double> residual;
};
 
#endif