Non-Parametric Tests in C# QuickStart Sample
Illustrates how to perform non-parametric tests like the Wilcoxon-Mann-Whitney test and the Kruskal-Wallis test in C#.
View this sample in: Visual Basic F# IronPython
using System;
using Extreme.Mathematics;
using Extreme.Statistics;
using Extreme.Statistics.Tests;
namespace Extreme.Numerics.Quickstart.CSharp {
/// <summary>
/// Demonstrates how to use non-parametric hypothesis tests
/// like the Mann-Whitney (Wilcoxon) rank sum test and the
/// Kruskal-Wallis test.
/// </summary>
class NonParametricTests {
static void Main(string[] args) {
// The license is verified at runtime. We're using
// a demo license here. For more information, see
// https://numerics.net/trial-key
Extreme.License.Verify("Demo license");
//
// Mann-Whitney test
//
Console.WriteLine("Mann-Whitney Test");
// The Mann-Whitney test compares to samples to see if they were
// drawn from the same distribution.
// We use an example from McDonald, et.al. (1996), who compared
// the geographic variation in oyster DNA to the variation in
// proteins. A significant difference in the samples would suggest
// that natural selection played a role in the oyster diversification.
// There are two ways to create a test with multiple samples.
// The first is to put all the data in one variable,
// and use a second variable to group the data in the first.
Console.WriteLine("\nUsing grouping variable:");
var values = Vector.Create(new double[] {
-0.005, 0.116,-0.006, 0.095, 0.053, 0.003,
-0.005, 0.016, 0.041, 0.016, 0.066,
0.163, 0.004, 0.049, 0.006, 0.058,
-0.002, 0.015, 0.044, 0.024
});
var groups = Vector.Create(new Group[] {
Group.DNA, Group.DNA, Group.DNA, Group.DNA, Group.DNA, Group.DNA,
Group.Protein, Group.Protein, Group.Protein, Group.Protein, Group.Protein,
Group.Protein, Group.Protein, Group.Protein, Group.Protein, Group.Protein,
Group.Protein, Group.Protein, Group.Protein, Group.Protein
}).AsCategorical();
// With this data, we can create the test:
var mw = new MannWhitneyTest<double>(values, groups);
// We can obtan the value of the test statistic through the Statistic property,
// and the corresponding P-value through the PValue property:
Console.WriteLine("Test statistic: {0:F4}", mw.Statistic);
Console.WriteLine("P-value: {0:F4}", mw.PValue);
// The significance level is the default value of 0.05:
Console.WriteLine("Significance level: {0:F2}", mw.SignificanceLevel);
// We can now print the test scores:
Console.WriteLine("Reject null hypothesis? {0}", mw.Reject() ? "yes" : "no");
// We can get the same scores for the 0.01 significance level by explicitly
// passing the significance level as a parameter to these methods:
Console.WriteLine("Significance level: {0:F2}", 0.01);
Console.WriteLine("Reject null hypothesis? {0}", mw.Reject(0.01) ? "yes" : "no");
// The second method is to put the data in different variables
Console.WriteLine("\nUsing multiple variables:");
var dnaValues = Vector.Create(new double[] {
-0.005, 0.116,-0.006, 0.095, 0.053, 0.003 });
var proteinValues = Vector.Create(new double[] {
-0.005, 0.016, 0.041, 0.016, 0.066,
0.163, 0.004, 0.049, 0.006, 0.058,
-0.002, 0.015, 0.044, 0.024
});
// With this data, we can create the test:
mw = new MannWhitneyTest<double>(dnaValues, proteinValues);
// We can obtan the value of the test statistic through the Statistic property,
// and the corresponding P-value through the PValue property:
Console.WriteLine("Test statistic: {0:F4}", mw.Statistic);
Console.WriteLine("P-value: {0:F4}", mw.PValue);
// The significance level is the default value of 0.05:
Console.WriteLine("Significance level: {0:F2}", mw.SignificanceLevel);
// We can now print the test scores:
Console.WriteLine("Reject null hypothesis? {0}", mw.Reject() ? "yes" : "no");
//
// Kruskal-Wallis test
//
Console.WriteLine("\nKruskal-Wallis Test\n");
// The Kruskal-Wallis test is a generalization of the Mann-Whitney test
// to more than 2 groups.
// The following example was taken from the NIST Engineering Statistics Handbook
// at http://www.itl.nist.gov/div898/handbook/prc/section4/prc41.htm
// The data represents percentage quarterly growth
// in 4 investment funds:
var aValues = Vector.Create(new double[] { 4.2, 4.6, 3.9, 4.0 });
var bValues = Vector.Create(new double[] { 3.3, 2.4, 2.6, 3.8, 2.8 });
var cValues = Vector.Create(new double[] { 1.9, 2.4, 2.1, 2.7, 1.8 });
var dValues = Vector.Create(new double[] { 3.5, 3.1, 3.7, 4.1, 4.4 });
// We simply pass these variables to the constructor:
var kw = new KruskalWallisTest(aValues, bValues, cValues, dValues);
// We can obtan the value of the test statistic through the Statistic property,
// and the corresponding P-value through the PValue property:
Console.WriteLine("Test statistic: {0:F4}", kw.Statistic);
Console.WriteLine("P-value: {0:F4}", kw.PValue);
// The significance level is the default value of 0.05:
Console.WriteLine("Significance level: {0:F2}", kw.SignificanceLevel);
// We can now print the test scores:
Console.WriteLine("Reject null hypothesis? {0}", kw.Reject() ? "yes" : "no");
//
// Runs test
//
Console.WriteLine("\nRuns Test\n");
// The runs test is a test of randomness.
// It compares the lengths of runs of the same value
// in a sample to what would be expected.
var genders = Vector.Create(new Gender[] {
Gender.Male, Gender.Male, Gender.Male, Gender.Female, Gender.Female,
Gender.Female, Gender.Male, Gender.Male, Gender.Male, Gender.Male,
Gender.Female, Gender.Female, Gender.Male, Gender.Male, Gender.Male,
Gender.Female, Gender.Female, Gender.Female, Gender.Female, Gender.Female,
Gender.Female, Gender.Female, Gender.Male, Gender.Male, Gender.Female,
Gender.Male, Gender.Male, Gender.Female, Gender.Female, Gender.Female,
Gender.Female}).AsCategorical();
var rt = new RunsTest<Gender>(genders);
// We can obtan the value of the test statistic through the Statistic property,
// and the corresponding P-value through the PValue property:
Console.WriteLine("Test statistic: {0:F4}", rt.Statistic);
Console.WriteLine("P-value: {0:F4}", rt.PValue);
// The significance level is the default value of 0.05:
Console.WriteLine("Significance level: {0:F2}", rt.SignificanceLevel);
// We can now print the test scores:
Console.WriteLine("Reject null hypothesis? {0}", rt.Reject() ? "yes" : "no");
Console.Write("Press any key to exit.");
Console.ReadLine();
}
}
enum Group {
DNA,
Protein
}
enum Gender {
Male,
Female
}
}