コード例 #1
0
        ///<summary>
        ///This subroutine determines the best fit line by minimizing the sum of the squares
        ///of the perpendicular distances of the points to the line.
        ///This was initially reported by Kermack and Haldane (1950) Biometrika, 37, 30.
        ///However I found it in York, D. (1966) Canadian Journal of Physics, vol 44, p 1079.
        ///</summary>
        public static LinearRegression LinearRegressionWithErrorsInBothCoordinates(Statistics a, Statistics b)
        {
            double meanA = a.Mean();
            double meanB = b.Mean();
            double sA2   = 0;
            double sB2   = 0;
            double sAb   = 0;

            for (int i = 0; i < a.Length; i++)
            {
                double dA = a._list[i] - meanA;
                double dB = b._list[i] - meanB;

                sA2 += dA * dA;
                sB2 += dB * dB;
                sAb += dA * dB;
            }
            LinearRegression result = new LinearRegression();

            if (sA2 > 0 && sB2 > 0 && sAb > 0)
            {
                result.Correlation = sAb / Math.Sqrt(sA2 * sB2);
                result.Slope       = (sB2 - sA2 + Math.Sqrt((sB2 - sA2) * (sB2 - sA2)
                                                            + 4 * (sAb * sAb))) / 2 / sAb;
                result.Intercept = meanB - result.Slope * meanA;
                if (result.Correlation < 1)
                {
                    result.SlopeError = (result.Slope / result.Correlation) * Math.Sqrt((1 - (result.Correlation * result.Correlation)) / a.Length);
                }
                else
                {
                    result.SlopeError = 0;
                }
            }
            else
            {
                result.Correlation = 0;
                result.Slope       = 0;
                result.SlopeError  = 0;
                result.Intercept   = 0;
            }
            return(result);
        }
コード例 #2
0
 public LinearRegressionEntry(double startTime, double endTime, LinearRegression linearRegression)
 {
     StartTime = startTime;
     EndTime = endTime;
     LinearRegression = linearRegression;
 }
コード例 #3
0
ファイル: Statistics.cs プロジェクト: lgatto/proteowizard
        ///<summary>
        ///This subroutine determines the best fit line by minimizing the sum of the squares
        ///of the perpendicular distances of the points to the line.
        ///This was initially reported by Kermack and Haldane (1950) Biometrika, 37, 30.
        ///However I found it in York, D. (1966) Canadian Journal of Physics, vol 44, p 1079.
        ///</summary>
        public static LinearRegression LinearRegressionWithErrorsInBothCoordinates(Statistics a, Statistics b)
        {
            double meanA = a.Mean();
            double meanB = b.Mean();
            double sA2 = 0;
            double sB2 = 0;
            double sAb = 0;

            for (int i = 0; i < a.Length; i++)
            {
                double dA = a._list[i] - meanA;
                double dB = b._list[i] - meanB;

                sA2 += dA * dA;
                sB2 += dB * dB;
                sAb += dA * dB;
            }
            LinearRegression result = new LinearRegression();
            if (sA2 > 0 && sB2 > 0 && sAb > 0)
            {
                result.Correlation = sAb / Math.Sqrt(sA2 * sB2);
                result.Slope = (sB2 - sA2 + Math.Sqrt((sB2 - sA2) * (sB2 - sA2)
                                               + 4 * (sAb * sAb))) / 2 / sAb;
                result.Intercept = meanB - result.Slope * meanA;
                if (result.Correlation < 1)
                {
                    result.SlopeError = (result.Slope / result.Correlation) * Math.Sqrt((1 - (result.Correlation * result.Correlation)) / a.Length);
                }
                else
                {
                    result.SlopeError = 0;
                }
            }
            else
            {
                result.Correlation = 0;
                result.Slope = 0;
                result.SlopeError = 0;
                result.Intercept = 0;
            }
            return result;
        }