Beispiel #1
0
        void CalcEstimates()
        {
            int n = XMat.GetLength(0), k = XMat.GetLength(1) - 1;

            // коэффициенты, необходимые далее
            double Qost = CalculatedY.Zip(RealY, (a, b) => (a - b) * (a - b)).Sum();
            double QR   = Matrix.ScalarMul(CalculatedY, CalculatedY);

            S2 = Qost / (n - k - 1);

            // точечная оценка регресии
            F         = (QR / (k + 1)) / (Qost / (n - k - 1));
            FCritical = FisherTable.GetValue(k + 1, n - k - 1);

            // точечная оценка параметров регрессии
            double[] Sb = Enumerable.Range(0, k + 1)
                          .Select(j => Math.Sqrt(S2 * XTXInvMat[j, j]))
                          .ToArray();
            CoeffsT   = Coeffs.Zip(Sb, (a, b) => Math.Abs(a / b)).ToArray();
            TCritical = StudentTable.GetValue(n - k - 1);

            // интервальная оценка регрессии
            IntervalEstimates = new double[n];
            for (int i = 0; i < n; i++)
            {
                var x0 = Enumerable.Range(0, XMat.GetLength(1)).Select(j => XMat[i, j]).ToArray();
                IntervalEstimates[i] = TCritical *
                                       Math.Sqrt(S2 * Matrix.ScalarMul(x0, Matrix.MulVect(XTXInvMat, x0)));
            }
            // интервальная оценка параметров регрессии
            CoeffsIntervalEstimates = Sb.Select(s => Math.Abs(s * TCritical)).ToArray();

            ApproximationError = RealY.Zip(CalculatedY, (a, b) => Math.Abs((a - b) / a)).Sum() / RealY.Length;
        }
        public DescriptiveStatistics(string name, double[] vals)
        {
            ParameterName = name;

            Sum               = vals.Sum();
            Count             = vals.Length;
            Average           = Sum / Count;
            Dispersion        = vals.Sum(e => Math.Pow(e - Average, 2)) / (Count - 1);
            StandardDeviation = Math.Sqrt(Dispersion);
            StandardError     = StandardDeviation / Math.Sqrt(Count);
            Min               = vals.Min();
            Max               = vals.Max();
            Interval          = Max - Min;

            Median    = GetMedian(vals);
            Mode      = GetMode(vals);
            Excess    = GetExcess(vals, Average, StandardDeviation);
            Asymmetry = GetAsymmetry(vals, Average, StandardDeviation);

            double t = StudentTable.GetValue(Count - 1);

            MarginalError = t * StandardError;
            double neededError = 0.01;

            RequiredSize = (int)(t * t * Dispersion / (neededError * neededError));
            MarginalErrorWithRequiredSize = t * StandardDeviation / Math.Sqrt(RequiredSize);
        }
 public Correlations(Table tbl, DescriptiveStatistics[] dstats)
 {
     CalcCorrMatrix(tbl, dstats);
     CalcPartialCorrMatrix(tbl);
     CalcMultipleCorrelation(tbl);
     MultipleDeterminationCoeffs = MultipleCorreletaionCoeffs.Select(a => a * a).ToArray();
     CalcMultipleSignificance(tbl);
     CalcSignificanceMatrix(tbl);
     Critical = StudentTable.GetValue(tbl.RowsCount - 2);
 }