public static RealType QuantileIP <RealType>(
IAlgebraReal <RealType> algebra,
IList <RealType> list,
float quantile)
{
list = ToolsCollection.Sort(list, algebra);
return(QuantileSorted(algebra, list, quantile));
}
public static double TestStatic(IList <double> sample)
{
// as in http://nl.mathworks.com/matlabcentral/fileexchange/13964-shapiro-wilk-and-shapiro-francia-normality-tests/content/swtest.m
//% SWTEST Shapiro - Wilk parametric hypothesis test of composite normality.
//% [H, pValue, SWstatistic] = SWTEST(X, ALPHA) performs the
//% Shapiro - Wilk test to determine if the null hypothesis of
//% composite normality is a reasonable assumption regarding the
//% population distribution of a random sample X. The desired significance
//% level, ALPHA, is an optional scalar input(default = 0.05).
//%
//% The Shapiro - Wilk and Shapiro-Francia null hypothesis is:
//% "X is normal with unspecified mean and variance."
//%
//% This is an omnibus test, and is generally considered relatively
//% powerful against a variety of alternatives.
//% Shapiro - Wilk test is better than the Shapiro - Francia test for
//% Platykurtic sample. Conversely, Shapiro - Francia test is better than the
//% Shapiro - Wilk test for Leptokurtic samples.
//%
//% If the sample is Leptokurtic performs the Shapiro - Francia
//% If the sample is Platykurtic performs the Shapiro - Wilk test.
//%
//%
//% Inputs:
//% X - a vector of deviates from an unknown distribution.The observation
//% number must exceed 3 and less than 5000.
//%
//% Outputs:
//% pValue - is the p - value, or the probability of observing the given
//% result by chance given that the null hypothesis is true. Small values
//% of pValue cast doubt on the validity of the null hypothesis.
//%
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
//% Copyright(c) 17 March 2009 by Ahmed Ben Sada %
//% Department of Finance, IHEC Sousse - Tunisia %
//% Email: [email protected] %
//% $ Revision 3.0 $ Date: 18 Juin 2014 $ %
//%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
//%
//% References:
//%
//% -Royston P. "Remark AS R94", Applied Statistics(1995), Vol. 44,
//% No. 4, pp. 547 - 551.
//% AS R94-- calculates Shapiro - Wilk normality test and P - value
//% for sample sizes 3 <= n <= 5000.Handles censored or uncensored data.
//% Corrects AS 181, which was found to be inaccurate for n > 50.
//% Subroutine can be found at: http://lib.stat.cmu.edu/apstat/R94
//%
//% -Royston P. "A pocket-calculator algorithm for the Shapiro-Francia test
//% for non - normality: An application to medicine", Statistics in Medecine
//% (1993a), Vol. 12, pp. 181 - 184.
//%
//% -Royston P. "A Toolkit for Testing Non-Normality in Complete and
//% Censored Samples", Journal of the Royal Statistical Society Series D
//% (1993b), Vol. 42, No. 1, pp. 37 - 43.
//%
//% -Royston P. "Approximating the Shapiro-Wilk W-test for non-normality",
//% Statistics and Computing (1992), Vol. 2, pp. 117 - 119.
//%
//% -Royston P. "An Extension of Shapiro and Wilk's W Test for Normality
//% to Large Samples", Journal of the Royal Statistical Society Series C
//% (1982a), Vol. 31, No. 2, pp. 115 - 124.
//%
if (sample.Count < 3)
{
throw new Exception("Sample vector must have at least 3 valid observations.");
}
if (5000 < sample.Count)
{
throw new Exception("Shapiro-Wilk test might be inaccurate due to large sample size ( > 5000).");
}
// % First, calculate the a's for weights as a function of the m's
// % See Royston(1992, p. 117) and Royston (1993b, p. 38) for details
// % in the approximation.
ToolsCollection.Sort(sample);
//% Sort the vector X in ascending order.
int n = sample.Count;
double[] mtilde = new double[n];
for (int index = 0; index < n; index++)
{
mtilde[index] = Normal.InvCDF(0.0, 1.0, ((index + 1) - (3.0 / 8.0)) / (n + (1.0 / 4.0)));
}
double mtilde_in_product = 0.0;
for (int index = 0; index < n; index++)
{
mtilde_in_product += mtilde[index] * mtilde[index];
}
double[] weights = new double[n]; //% Preallocate the weights.
for (int index = 0; index < n; index++)
{
//sould say weights = 1 / sqrt(mtilde'*mtilde) * mtilde;
weights[index] = 1.0 / Math.Sqrt(mtilde_in_product) * mtilde[index];
}
double sample_mean = ToolsMathStatistics.Mean(sample);
double kurtosis = ToolsMathStatistics.KurtosisPlain(sample);
if (kurtosis > 3)
{
//% The Shapiro - Francia test is better for leptokurtic samples.
//% The Shapiro - Francia statistic W' is calculated to avoid excessive
//% rounding errors for W' close to 1 (a potential problem in very
//% large samples).
double sf_nom = Inproduct(sample, weights);
double sf_denom = 0.0;
for (int index = 0; index < n; index++)
{
sf_denom += (sample[index] - sample_mean) * (sample[index] - sample_mean);
}
double W = (sf_nom * sf_nom) / sf_denom;
//% Royston(1993a, p. 183):
double nu = Math.Log(n);
double u1 = Math.Log(nu) - nu;
double u2 = Math.Log(nu) + 2 / nu;
double mu = -1.2725 + (1.0521 * u1);
double sigma = 1.0308 - (0.26758 * u2);
double newSFstatistic = Math.Log(1 - W);
//% Compute the normalized Shapiro - Francia statistic and its p-value.
double NormalSFstatistic = (newSFstatistic - mu) / sigma;
//% Computes the p-value, Royston(1993a, p. 183).
double pValue = 1 - Normal.CDF(0, 1, NormalSFstatistic);
return(pValue);
}
else
{
//% The Shapiro - Wilk test is better for platykurtic samples.
double u = 1 / Math.Sqrt(n);
//% Royston(1992, p. 117) and Royston(1993b, p. 38):
double[] PolyCoef_1 = new double [] { -2.706056, 4.434685, -2.071190, -0.147981, 0.221157, weights[n - 1] }; //TODO check was weights[n]
double[] PolyCoef_2 = new double [] { -3.582633, 5.682633, -1.752461, -0.293762, 0.042981, weights[n - 2] }; //TODO check was weights[n - 1]
//% Royston(1992, p. 118) and Royston (1993b, p. 40, Table 1)
double[] PolyCoef_3 = new double [] { -0.0006714, 0.0250540, -0.39978, 0.54400 };
double[] PolyCoef_4 = new double [] { -0.0020322, 0.0627670, -0.77857, 1.38220 };
double[] PolyCoef_5 = new double [] { 0.00389150, -0.083751, -0.31082, -1.5861 };
double[] PolyCoef_6 = new double [] { 0.00303020, -0.082676, -0.48030 };
double[] PolyCoef_7 = new double[] { 0.459, -2.273 };
weights[n - 1] = Polyval(PolyCoef_1, u);
weights[1] = -weights[n - 1];
int count = 0;
double phi = 0.0;
if (n > 5)
{
weights[n - 2] = Polyval(PolyCoef_2, u);
weights[2] = -weights[n - 2]; //TODO check n - 1
count = 3;
phi = (Inproduct(mtilde, mtilde) - 2 * Math.Pow(mtilde[n - 1], 2) - 2 * Math.Pow(mtilde[n - 2], 2)) / (1 - 2 * Math.Pow(weights[n - 1], 2) - 2 * Math.Pow(weights[n - 2], 2));
}
else
{
count = 2;
phi = (Inproduct(mtilde, mtilde) - 2 * Math.Pow(mtilde[n - 1], 2)) / (1 - 2 * Math.Pow(weights[n - 1], 2));
}
//% Special attention when n = 3(this is a special case).
if (n == 3)
{
//% Royston(1992, p. 117)
weights[1] = 1 / Math.Sqrt(2);
weights[n - 1] = -weights[1];
phi = 1;
}
// % The vector 'WEIGHTS' obtained next corresponds to the same coefficients
// % listed by Shapiro-Wilk in their original test for small samples.
for (int index = count; index < n - count; index++)
{
weights[index] = mtilde[index] / Math.Sqrt(phi);
}
//% The Shapiro - Wilk statistic W is calculated to avoid excessive rounding
//% errors for W close to 1(a potential problem in very large samples).
double[] residual = ToolsMathCollectionDouble.Subtract(sample, sample_mean);
double W = Math.Pow(Inproduct(weights, sample), 2) / Inproduct(residual, residual);
//%
//% Calculate the normalized W and its significance level(exact for
//% n = 3).Royston(1992, p. 118) and Royston (1993b, p. 40, Table 1).
//%
double newn = Math.Log(n);
double mu = 0.0;
double sigma = 0.0;
double gam = 0.0;
double newSWstatistic = 0.0;
if (n > 11)
{
mu = Polyval(PolyCoef_5, newn);
sigma = Math.Exp(Polyval(PolyCoef_6, newn));
newSWstatistic = Math.Log(1 - W);
}
else if ((n >= 4) && (n <= 11))
{
mu = Polyval(PolyCoef_3, n);
sigma = Math.Exp(Polyval(PolyCoef_4, n));
gam = Polyval(PolyCoef_7, n);
newSWstatistic = -Math.Log(gam - Math.Log(1 - W));
}
else if (n == 3)
{
mu = 0;
sigma = 1;
newSWstatistic = 0;
}
//% Compute the normalized Shapiro - Wilk statistic and its p-value.
double NormalSWstatistic = (newSWstatistic - mu) / sigma;
//% NormalSWstatistic is referred to the upper tail of N(0, 1),
//% Royston(1992, p. 119).
double pValue = 1 - Normal.CDF(mu, sigma, newSWstatistic);
//% Special attention when n = 3(this is a special case).
if (n == 3)
{
pValue = 6 / Math.PI * (Math.Asin(Math.Sqrt(W)) - Math.Asin(Math.Sqrt(3.0 / 4.0)));
//% Royston(1982a, p. 121)
}
return(pValue);
}
}
