public static double TestStatic(IList <IList <double> > samples)
{
int sample_size = samples[0].Count;
foreach (IList <double> sample in samples)
{
if (sample.Count != sample_size)
{
throw new Exception("samples must be of equal size");
}
}
double[,] measurement_ranks = ToolsMathStatistics.Ranks1(samples);
double[] rank_sums = ToolsMathCollection.Sums0(measurement_ranks);
double chi_square_statistic_part = 0.0;
for (int treatment_index = 0; treatment_index < rank_sums.Length; treatment_index++)
{
chi_square_statistic_part += ToolsMath.Sqr(rank_sums[treatment_index]);
}
double treatment_count = samples.Count;
double block_count = sample_size;
double chi_square_statistic = ((12.0 / (block_count * treatment_count * (treatment_count + 1.0))) * chi_square_statistic_part) - (3 * block_count * (treatment_count + 1.0));
return(ChiSquared.CDF(rank_sums.Length, chi_square_statistic));
}
public static double TestStatic(IList <IList <double> > samples)
{
double sample_count = samples.Count;
double total_count = ToolsCollection.CountElements(samples);
double total_mean = ToolsMathStatistics.MeanAll(samples);
IList <double> sample_means = ToolsMathStatistics.Means0(samples);
double sstr = 0.0;
for (int sample_index = 0; sample_index < samples.Count; sample_index++)
{
sstr += samples[sample_index].Count * ToolsMath.Sqr(sample_means[sample_index] - total_mean);
}
double sse = 0.0;
for (int sample_index = 0; sample_index < samples.Count; sample_index++)
{
for (int measurement_index = 0; measurement_index < samples[sample_index].Count; measurement_index++)
{
sse += ToolsMath.Sqr(samples[sample_index][measurement_index] - sample_means[sample_index]);
}
}
//FTransform
double degrees_of_freedom_0 = (sample_count - 1.0);
double degrees_of_freedom_1 = (total_count - sample_count);
double summed_variance = sstr / degrees_of_freedom_0;
double total_varaiance = sse / degrees_of_freedom_1;
double f_statistic = summed_variance / total_varaiance;
return(FisherSnedecor.CDF(degrees_of_freedom_0, degrees_of_freedom_1, f_statistic));
}
public double GetAUC(LabelType label_value)
{
int label_index = this.Model.DataContext.GetLabelDescriptor(0).GetValueIndex(label_value);
double[] label_scores = new double[label_values.Length];
double[] second_scores = new double[label_values.Length];
bool[] labels = new bool[label_values.Length];
for (int index = 0; index < label_values.Length; index++)
{
label_scores[index] = likelihoods[index][label_index];
labels[index] = (this.Model.DataContext.GetLabelDescriptor(0).GetValueIndex(label_values[index]) == label_index);
List <int> ordering_indexes = ToolsMathCollection.Ordering(likelihoods[index]);
if (ordering_indexes[0] == label_index)
{
second_scores[index] = likelihoods[index][label_index];
}
else
{
second_scores[index] = likelihoods[index][ordering_indexes[0]];
}
}
double[] scores = ToolsMathCollection.DivideList(label_scores, second_scores);
return(ToolsMathStatistics.ComputeROCAUCTrapeziod(labels, scores));
}
public override double Test(IList <IList <double> > samples)
{
double total_count = ToolsCollection.CountElements(samples);
double pooled_mean = ToolsMathStatistics.MeanAll(samples);
double nominator_part = 0.0;
double denominator_part = 0.0;
for (int sample_index = 0; sample_index < samples.Count; sample_index++)
{
double sample_mean = ToolsMathStatistics.Mean(samples[sample_index]);
double sample_median = ToolsMathStatistics.Quantile(samples[sample_index], 0.5f);
nominator_part += (sample_mean - pooled_mean) * (sample_mean - pooled_mean) * samples[sample_index].Count;
for (int measurement_index = 0; measurement_index < samples[sample_index].Count; measurement_index++)
{
double diff = Math.Abs(sample_median - samples[sample_index][measurement_index]) - sample_mean; //This is the difference with brown forsythe test
denominator_part += diff * diff;
}
}
double degrees_of_freedom_0 = samples.Count - 1;
double degrees_of_freedom_1 = total_count - samples.Count;
double f_statistic = (degrees_of_freedom_1 * nominator_part) / (degrees_of_freedom_0 * denominator_part);
FisherSnedecor distribution = new FisherSnedecor(degrees_of_freedom_0, degrees_of_freedom_1, new Random());
return(distribution.CumulativeDistribution(f_statistic));
}
//TODO implement 3 mean variants
//http://www.itl.nist.gov/div898/handbook/eda/section3/eda35a.htm
public static double TestStatic(IList <IList <double> > samples)
{
double total_count = ToolsCollection.CountElements(samples);
double total_mean = ToolsMathStatistics.MeanAll(samples);
//double[] sample_means = ToolsMathStatistics.Means0(samples);
//double total_mean = ToolsMathStatistics.MedianAll(samples);
double[] sample_means = ToolsMathStatistics.Medians0(samples);
double summed_varriance = 0.0;
for (int sample_index = 0; sample_index < samples.Count; sample_index++)
{
summed_varriance += samples[sample_index].Count * ToolsMath.Sqr(sample_means[sample_index] - total_mean);
}
double total_variance = 0.0;
for (int sample_index = 0; sample_index < samples.Count; sample_index++)
{
for (int measurement_index = 0; measurement_index < samples[sample_index].Count; measurement_index++)
{
total_variance += ToolsMath.Sqr(samples[sample_index][measurement_index] - sample_means[sample_index]);
}
}
double degrees_of_freedom_0 = samples.Count - 1;
double degrees_of_freedom_1 = total_count - samples.Count;
double f_statistic = (degrees_of_freedom_1 * summed_varriance) / (degrees_of_freedom_0 * total_variance);
return(FisherSnedecor.CDF(degrees_of_freedom_0, degrees_of_freedom_1, f_statistic));
}
public static Tuple <double, double> TestStatic(IList <IList <double> > samples)
{
int sample_size = samples[0].Count;
foreach (IList <double> sample in samples)
{
if (sample.Count != sample_size)
{
throw new Exception("samples must be of equal size");
}
}
// Larsen Marx 4Th editiopn P779
double sample_count = samples.Count;
double total_count = ToolsCollection.CountElements(samples);
double total_mean = ToolsMathStatistics.MeanAll(samples);
double sum_squared_all = ToolsMathStatistics.SumSquaredAll(samples);
IList <double> sample_sums = ToolsMathCollection.Sums0(samples);
IList <double> measurement_sums = ToolsMathCollection.Sums1(samples);
// compute C
double c = ToolsMath.Sqr(total_mean * total_count) / (sample_size * sample_count);
double sstot = sum_squared_all - c;
double ssb = 0;
for (int measurement_index = 0; measurement_index < sample_size; measurement_index++)
{
ssb += ToolsMath.Sqr(measurement_sums[measurement_index]) / sample_count;
}
ssb -= c;
double sstr = 0.0;
for (int sample_index = 0; sample_index < samples.Count; sample_index++)
{
sstr += ToolsMath.Sqr(sample_sums[sample_index]) / sample_size;
}
sstr -= c;
double sse = sstot - ssb - sstr;
double degrees_of_freedom_0_samples = (sample_count - 1.0);
double degrees_of_freedom_0_measurements = (sample_size - 1.0);
double degrees_of_freedom_1 = degrees_of_freedom_0_samples * degrees_of_freedom_0_measurements;
//F-Transform samples
double f_statistic_samples = (sstr / degrees_of_freedom_0_samples) / (sse / degrees_of_freedom_1);
//F-Transform measurements
double f_statistic_measurements = (ssb / degrees_of_freedom_0_measurements) / (sse / degrees_of_freedom_1);
return(new Tuple <double, double>(
FisherSnedecor.CDF(degrees_of_freedom_0_samples, degrees_of_freedom_1, f_statistic_samples),
FisherSnedecor.CDF(degrees_of_freedom_0_measurements, degrees_of_freedom_1, f_statistic_measurements)));
}
public static double TestStatic(IList <double> sample)
{
double mean = ToolsMathStatistics.Mean(sample);
double standard_deviation = ToolsMathStatistics.StandardDeviation(sample, mean);
//TODO it is actually illigal to use estimated paramter for the tested distribution
return(TestGoodnessOfFitKolmogorovSmirnov.TestStatic(new DistributionNormalUnivariateFloat64(mean, standard_deviation), sample));
}
public TransformWhiteningOld(IAlgebraLinear <MatrixType> algebra,
float [,] data)
{
AMatrix <MatrixType> data_matrix = algebra.Create(data);
means = algebra.Create(ToolsMathStatistics.Means1(data));
AMatrix <MatrixType> covariance_matrix = data_matrix * data_matrix.Transpose();
matrix_backward = covariance_matrix.Algebra.ComputeRoot(covariance_matrix);
matrix_forward = matrix_backward.Invert();
}
public void TestROCHanleyMcNeilTestComputeStandardError0()
{
Random random = new Random(0);
double[] sample_0 = new double[] { 177, 177, 165, 172, 172, 179, 163, 175, 166, 182, 177, 168, 179, 177 };
bool[] labels = new bool[] { true, false, true, false, true, false, true, false, true, false, true, false, true, false };
double auc_0 = ToolsMathStatistics.ComputeROCAUCTrapeziod(labels, sample_0);
double se_0 = TestROCHanleyMcNeil.ComputeStandardError(auc_0, sample_0, labels, random, 1000);
Assert.IsTrue(0.145 < se_0);
Assert.IsTrue(se_0 < 0.146);
}
public double Compute(ISet <int> selection)
{
bool[] selected_labels = labels.Select(selection);
double error = 0;
for (int index = 0; index < values.Length; index++)
{
double auc = ToolsMathStatistics.ComputeROCAUCTrapeziod(selected_labels, values[index].Select(selection));
error += Math.Abs(desired_aucs[index] - auc) * weigths[index];
}
return(error);
}
public static double TestStatic(IList <double> sample_0, IList <double> sample_1)
{
// as in https://en.wikipedia.org/wiki/F-test_of_equality_of_variances
// and
//Larsen Marx 4th edition P569
double variance_0 = ToolsMathStatistics.Variance(sample_0);
double variance_1 = ToolsMathStatistics.Variance(sample_1);
double f_statistic = variance_0 / variance_1;
double degrees_of_freedom_0 = (sample_0.Count - 1);
double degrees_of_freedom_1 = (sample_1.Count - 1);
return(FisherSnedecor.CDF(degrees_of_freedom_0, degrees_of_freedom_1, f_statistic));
}
public override double Test(IList <double> sample_0, IList <double> sample_1)
{
double mean_1 = ToolsMathStatistics.Mean(sample_0);
double mean_2 = ToolsMathStatistics.Mean(sample_1);
double variance = ToolsMathStatistics.VariancePooled(sample_0, sample_1);
double t_statistic = (mean_1 - mean_2) / (Math.Sqrt(variance) * Math.Sqrt((1.0 / sample_0.Count) + (1.0 / sample_1.Count)));
int degrees_of_freedom = (sample_0.Count + sample_1.Count) - 2;
StudentT distribution = new StudentT(0.1, 1.0, degrees_of_freedom);
return(distribution.CumulativeDistribution(-t_statistic));
}
public void DoExperiment()
{
double commission = 7;
double spread = 5;
int lookAhead = 60;
//Load data
string[,] table = ToolsIOCSV.ReadCSVFile(@"D:\GoogleDrive\TestData\Trading\eurusddata.csv", Delimiter.Comma);
//Convert data to values
double[] ask = new double[table.GetLength(0)];
double[] bid = new double[table.GetLength(0)];
for (int i = 0; i < table.GetLength(0); i++)
{
bid[i] = 100000 * double.Parse(table[i, 2].Replace(".", ","));
ask[i] = bid[i] + spread;
}
double mean1 = ToolsMathStatistics.Mean(ask);
double mean2 = ToolsMathStatistics.Mean(bid);
double[] buyCurve = new double[table.GetLength(0) - lookAhead];
double[] sellCurve = new double[table.GetLength(0) - lookAhead];
string[] buyCurveString = new string[buyCurve.Length];
for (int i = 0; i < buyCurve.Length; i++)
{
double max = double.MinValue;
double min = double.MaxValue;
for (int j = 1; j < lookAhead; j++)
{
max = Math.Max(max, bid[i + j]);
min = Math.Min(min, ask[i + j]);
}
buyCurve[i] = max - ask[i] - commission;
buyCurveString[i] = bid[i] + ";" + buyCurve[i];
sellCurve[i] = bid[i] - min - commission;
}
double mean3 = ToolsMathStatistics.Mean(buyCurve);
double mean4 = ToolsMathStatistics.Mean(sellCurve);
File.AppendAllLines(@"D:\GoogleDrive\TestData\Trading\buyCurveOut.csv", buyCurveString);
/*
* Load data
* for each time point find optimal buy order
* create line of optimal buy orders
*/
}
public FunctionMarginalIntervalNormal(IDataContext data_context, double[][] feature_data, int[] label_data, int feature_index, int label_index, double epsilon)
{
List <double> occurances = new List <double>();
for (int instance_index = 0; instance_index < feature_data.GetLength(0); instance_index++)
{
if (label_index == label_data[instance_index])
{
occurances.Add(feature_data[instance_index][feature_index]);
}
}
this.mean = ToolsMathStatistics.Mean(occurances);
this.standard_deviation = ToolsMathStatistics.StandardDeviation(occurances, mean);
}
public IFunctionBijective <float [], float []> Generate(
IList <float []> instances)
{
float[,] array = ToolsCollection.ConvertToTable(instances);
float[,] array_transposed = ToolsCollection.Transpose(array);
float [] lower_bounds = new float [array_transposed.Length];
float [] upper_bounds = new float [array_transposed.Length];
for (int index = 0; index < array_transposed.Length; index++)
{
lower_bounds[index] = ToolsMathStatistics.QuantileSorted(array_transposed.Select1DIndex0(index), quantile);
upper_bounds[index] = ToolsMathStatistics.QuantileSorted(array_transposed.Select1DIndex0(index), 1 - quantile);
}
return(new TransformRescale(lower_bounds, upper_bounds));
}
public static double TestStatic(IList <double> sample_0, IList <double> sample_1)
{
if (sample_0.Count != sample_1.Count)
{
throw new Exception("Samples not of equal size");
}
double[] difference = ToolsMathCollection.SubtractElements(sample_0, sample_1);
double mean_difference = ToolsMathStatistics.Mean(difference);
double variance = ToolsMathStatistics.Variance(difference);
double t_statistic = mean_difference / (Math.Sqrt(variance) / Math.Sqrt(sample_0.Count));
double degrees_of_freedom = (sample_0.Count - 1);
StudentT distribution = new StudentT(0.1, 1.0, degrees_of_freedom);
return(distribution.CumulativeDistribution(t_statistic));
}
public void DoExperiment()
{
double commission = 7;
//Load data
string [,] table = ToolsIOCSV.ReadCSVFile(@"D:\GoogleDrive\TestData\Trading\spreads.csv", Delimiter.SemiColon);
//Convert data to values
double[] ask = new double [table.GetLength(0)];
double[] bid = new double [table.GetLength(0)];
for (int i = 0; i < table.GetLength(0); i++)
{
ask[i] = 10000 * double.Parse(table[i, 3].Replace(".", ","));
bid[i] = 10000 * double.Parse(table[i, 2].Replace(".", ","));
}
double mean1 = ToolsMathStatistics.Mean(ask);
double mean2 = ToolsMathStatistics.Mean(bid);
double[] buyCurve = new double[table.GetLength(0) - 3600];
double[] sellCurve = new double[table.GetLength(0) - 3600];
for (int i = 0; i < buyCurve.Length; i++)
{
double max = double.MinValue;
double min = double.MaxValue;
for (int j = 1; j < 3600; j++)
{
max = Math.Max(max, bid[i + j]);
min = Math.Min(min, ask[i + j]);
}
buyCurve[i] = max - ask[i] - commission;
sellCurve[i] = bid[i] - min - commission;
}
double mean3 = ToolsMathStatistics.Mean(buyCurve);
double mean4 = ToolsMathStatistics.Mean(sellCurve);
/*
* Load data
* for each time point find optimal buy order
* create line of optimal buy orders
*/
}
public static double TestStatic(IList <IList <double> > samples)
{
double total_size = 0.0f;
double squared_rank_mean_sum = 0.0f;
double[][] rank_samples = ToolsMathStatistics.ConvertToAccendingRanks(samples);
foreach (double[] sample in rank_samples)
{
total_size += sample.Length;
double sum = ToolsMathCollection.Sum(sample);
squared_rank_mean_sum += (sum * sum) / ((double)sample.Length);
}
double statistic = (12.0 * squared_rank_mean_sum) / (total_size * (total_size + 1)) - 3 * (total_size + 1);
return(1 - ChiSquared.CDF(samples.Count - 1, statistic));
}
private static double Evaluate(ISet <int> current, bool[] labels, double[] values, double[][] values_other)
{
// Here the actual testing happend we want the lowest p value between the values set and its competitors
bool[] selected_labels = labels.Select(current);
double auc = ToolsMathStatistics.ComputeROCAUCTrapeziod(selected_labels, values.Select(current));
double best_other_auc = ToolsMathStatistics.ComputeROCAUCTrapeziod(selected_labels, values_other[0].Select(current));
for (int index = 1; index < values_other.Length; index++)
{
//TODO Here we actually want a statistical test
double other_auc = ToolsMathStatistics.ComputeROCAUCTrapeziod(selected_labels, values_other[index].Select(current));
if (best_other_auc < other_auc)
{
best_other_auc = other_auc;
}
}
return(auc - best_other_auc);
}
public static double TestStatic(IList <bool> labels, IList <double> sample_0)
{
if (sample_0.Count != labels.Count)
{
throw new Exception("Labels not of equal size");
}
//U = AUC∗nP∗nN
int positive_count = ToolsCollection.CountOccurance(labels, true);
int negative_count = sample_0.Count - positive_count;
double auc = ToolsMathStatistics.ComputeROCAUCTrapeziod(labels, sample_0);
double u_statistic = positive_count * negative_count * auc;
//Z transform
double expected_value = (positive_count * negative_count) / 2.0;
double variance = (positive_count * negative_count * (positive_count + negative_count + 1)) / 12.0;
double z_value = (u_statistic - expected_value) / Math.Sqrt(variance);
return(1 - Normal.CDF(0.0, 1.0, z_value));
}
public static void PlotHistogram(string file_path, double[] values, int bincount, double lower_quantile, double upper_quantile)
{
Array.Sort(values);
double lowerbound = ToolsMathStatistics.QuantileSorted(values, lower_quantile);
double upperbound = ToolsMathStatistics.QuantileSorted(values, upper_quantile);
double[] selected_values = ToolsMathCollection.Select(values, lowerbound, upperbound);
double stride = (upperbound - lowerbound) / bincount;
double[] bin_limits = new double[bincount - 1];
bin_limits[0] = lowerbound + stride;
for (int bin_limit_index = 1; bin_limit_index < bin_limits.Length; bin_limit_index++)
{
bin_limits[bin_limit_index] = bin_limits[bin_limit_index - 1] + stride;
}
PlotModel model = new HistrogramPlot(selected_values, bin_limits).PlotModel;
WriteToFile(file_path, model, 800, 800);
}
public static double TestStatic(IList <IList <double> > samples)
{
double pooled_varriance = ToolsMathStatistics.VariancePooled(samples);
double total_count = ToolsCollection.CountElements(samples);
double nom_part = 0;
double denom_part = 0;
for (int sample_index = 0; sample_index < samples.Count; sample_index++)
{
nom_part += (samples[sample_index].Count - 1.0) * Math.Log(ToolsMathStatistics.Variance(samples[sample_index]));
denom_part += (1.0 / (samples[sample_index].Count - 1));
}
double nominator = (total_count - samples.Count) * Math.Log(pooled_varriance) - nom_part;
double denominator = 1 + ((1.0 / (3 * (samples.Count - 1))) * (denom_part - (1 / (total_count - samples.Count))));
double chi_square_statisic = nominator / denominator;
double degrees_of_freedom = samples.Count - 1;
return(ChiSquared.CDF(degrees_of_freedom, chi_square_statisic));;
}
public override double Test(IList <double> sample_0, IList <double> sample_1)
{
double mean_0 = ToolsMathStatistics.Mean(sample_0);
double mean_1 = ToolsMathStatistics.Mean(sample_1);
double variance_0 = ToolsMathStatistics.Variance(sample_0, mean_0);
double variance_1 = ToolsMathStatistics.Variance(sample_0, mean_1);
double t_statistic = (mean_0 - mean_1) / Math.Sqrt((variance_0 / sample_0.Count) + (variance_1 / sample_1.Count));
//Welch–Satterthwaite equation:
double dof_nominator = ToolsMath.Sqr((variance_0 / sample_0.Count) + (variance_1 / sample_1.Count));
double dof_denominator =
(Math.Pow(variance_0, 4) / (sample_0.Count * sample_0.Count * (sample_0.Count - 1))) +
(Math.Pow(variance_1, 4) / (sample_1.Count * sample_1.Count * (sample_1.Count - 1)));
double degrees_of_freedom = dof_nominator / dof_denominator;
StudentT distribution = new StudentT(0.1, 1.0, degrees_of_freedom);
return(distribution.CumulativeDistribution(-t_statistic));
}
public static double TestStatic(IList <bool> labels, IList <double> sample_0, IList <double> sample_1, Random random, int trial_count)
{
if (sample_0.Count != sample_1.Count)
{
throw new Exception("Samples not of equal size");
}
if (sample_0.Count != labels.Count)
{
throw new Exception("Labels not of equal size");
}
double auc_0 = ToolsMathStatistics.ComputeROCAUCTrapeziod(labels, sample_0);
double auc_1 = ToolsMathStatistics.ComputeROCAUCTrapeziod(labels, sample_1);
double se_0 = ComputeStandardError(auc_0, sample_0, labels, random, trial_count);
double se_1 = ComputeStandardError(auc_1, sample_1, labels, random, trial_count);
double r = ComputeKendalTauA(sample_0, sample_1);
double z_value = ZTransform(auc_0, auc_1, se_0, se_1, r);
double p_value = 1 - Normal.CDF(0.0, 1.0, z_value);
return(p_value);
}
public override bool ComputeRBA(IMarketModelIndicator market_model, double[] result)
{
if (previous_1 == 0)
{
previous_1 = market_model.CurrentBid;
previous_2 = market_model.CurrentBid;
}
double momentum = previous_1 - previous_2;
double error_now = market_model.CurrentBid - (previous_1 + (momentum_weight * momentum));
indicator_error_now[index_indicator_error_now % indicator_error_now.Length] = error_now;
index_indicator_error_now++;
double error_now_s = ToolsMathStatistics.StandardDeviation(indicator_error_now);
if (error_now_s == 0)
{
error_now_s = double.Epsilon;
}
double error_now_z = error_now / error_now_s;
double error_now_weight = max_error_weight * (1 - Math.Exp(-ToolsMath.Sqr(error_now_z) / expected_error_z));
result[0] = previous_1 + (momentum * momentum_weight) + (error_now * error_now_weight);
result[1] = result[0] + (error_now_s * 2);
result[2] = result[0] - (error_now_s * 2);
result[3] = error_now;
result[4] = error_now_s;
result[5] = error_now_z;
result[6] = error_now_weight;
result[7] = momentum;
previous_2 = previous_1;
previous_1 = result[0];
return(indicator_error_now.Length < market_model.Second1.HistoryCount);
}
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);
}
}
