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/*Copyright (C) 2015 Olivier Delaneau, Halit Ongen, Emmanouil T. Dermitzakis
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.*/
#ifndef full_linear_regression_h
#define full_linear_regression_h
#include <vector>
#include <iostream>
#include <cmath>
#include <cassert>
#ifndef MATHLIB_STANDALONE
#define MATHLIB_STANDALONE
#endif
#include <Rmath.h>
using namespace std;
class linReg{
public:
double pval;
double corr;
double r;
double beta;
double yIntercept;
double se;
vector < float > residuals,X,Y;
linReg(){pval=1;corr=0;beta=0; yIntercept = 0; residuals = vector < float> (0);X = vector < float> (0);Y = vector < float> (0); se = 0;r=0;}
linReg(double p ,double c, double b, double y , vector <float> &res){pval=p;corr=c;beta=b; yIntercept = y; residuals = res; r = beta < 0.0 ? sqrt(corr) * -1.0 : sqrt(corr);}
friend ostream& operator<<(ostream& out, const linReg& l){
out << "X";
for (int i =0 ; i < l.X.size(); i++) out << "\t" << l.X[i];
out << "\n";
out << "Y";
for (int i =0 ; i < l.Y.size(); i++) out << "\t" << l.Y[i];
out << "\n";
out << "Residuals";
for (int i =0 ; i < l.residuals.size(); i++) out << "\t" << l.residuals[i];
out << "\n";
out << "R2\t" << l.corr << "\n";
out << "R\t" << l.r << "\n";
out << "Beta\t" << l.beta << "\n";
out << "SE\t" << l.se << "\n";
out << "Y-int\t" << l.yIntercept << "\n";
out << "Pval\t" << l.pval;
return out;
}
linReg( vector <float > & x, vector <float > &y){
assert(x.size() == y.size());
X = x;
Y = y;
double dataSize = (double) x.size();
residuals = vector < float > ( (unsigned int) dataSize, 0.0);
double sum_x = 0.0; //sum of x values
double sum_y = 0.0; //sum of y values
double sum_xy = 0.0; //sum of x * y
double sum_xx = 0.0; //sum of x^2
double sum_res = 0.0; //sum of squared residue
double sum_Yres = 0.0; //sum of squared of the discrepancies
double AVGy = 0.0; //mean of y
double AVGx = 0.0; //mean of x
for (int i = 0 ; i < dataSize; i++){
sum_x += X[i];
sum_y += Y[i];
sum_xy += X[i] * Y[i];
sum_xx += X[i] * X[i];
}
AVGy = sum_y / dataSize;
AVGx = sum_x / dataSize;
beta = (dataSize * sum_xy - sum_x * sum_y) / (dataSize * sum_xx - sum_x*sum_x);
yIntercept = AVGy - beta * AVGx;
for (int i = 0 ; i < dataSize; i++){
residuals[i] = Y[i] - (X[i] * beta + yIntercept);
sum_Yres += residuals[i] * residuals[i];
sum_res += pow(Y[i] - AVGy,2);
}
corr = (sum_res - sum_Yres) / sum_res;
r = beta < 0 ? sqrt(corr) * -1.0 : sqrt(corr);
se = sqrt(sum_Yres / (dataSize-2));
pval = pf((dataSize-2) * corr / (1 - corr), 1, (dataSize-2), 0, 0);
}
};
#undef MATHLIB_STANDALONE
#endif /* full_linear_regression_h */
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