Goodness-Of-Fit Tests in C# QuickStart Sample
Illustrates how to test for goodness-of-fit using classes in the Numerics.NET.Statistics.Tests namespace in C#.
View this sample in: Visual Basic F# IronPython
using System;
using Numerics.NET.Statistics;
using Numerics.NET.Statistics.Distributions;
using Numerics.NET.Statistics.Tests;
using Numerics.NET;
namespace Numerics.NET.QuickStart.CSharp
{
/// <summary>
/// Illustrates the Chi Square, Kolmogorov-Smirnov and Anderson-Darling
/// tests for goodness-of-fit.
/// </summary>
class GoodnessOfFitTests
{
static void Main(string[] args)
{
// The license is verified at runtime. We're using
// a 30 day trial key here. For more information, see
// https://numerics.net/trial-key
Numerics.NET.License.Verify("64542-18980-57619-62268");
// This QuickStart Sample illustrates the wide variety of goodness-of-fit
// tests available.
//
// Chi-square Test
//
Console.WriteLine("Chi-square test.");
// The Chi-square test is the simplest of the goodness-of-fit tests.
// The results follow a binomial distribution with 3 trials (rolls of the dice):
BinomialDistribution sixesDistribution = new BinomialDistribution(3, 1/6.0);
// First, create a histogram with the expected results.
var expected = sixesDistribution.GetExpectedHistogram(100);
// And a histogram with the actual results
var actual = Vector.Create(new double[] {51, 35, 12, 2});
var chiSquare = new ChiSquareGoodnessOfFitTest(actual, expected);
chiSquare.SignificanceLevel = 0.01;
// We can obtan the value of the test statistic through the Statistic property,
// and the corresponding P-value through the Probability property:
Console.WriteLine($"Test statistic: {chiSquare.Statistic:F4}");
Console.WriteLine($"P-value: {chiSquare.PValue:F4}");
// We can now print the test results:
Console.WriteLine("Reject null hypothesis? {0}",
chiSquare.Reject() ? "yes" : "no");
//
// One-sample Kolmogorov-Smirnov Test
//
Console.WriteLine("\nOne-sample Kolmogorov-Smirnov Test");
// We will investigate a sample of 25 random numbers from a lognormal distribution
// and investigate how well it matches a similar looking Weibull distribution.
// We first create the two distributions:
LognormalDistribution logNormal = new LognormalDistribution(0, 1);
WeibullDistribution weibull = new WeibullDistribution(2, 1);
// Then we generate the samples from the lognormal distribution:
var logNormalSample = logNormal.Sample(25);
// Finally, we construct the Kolmogorov-Smirnov test:
var ksTest = new OneSampleKolmogorovSmirnovTest(logNormalSample, weibull);
// We can obtan the value of the test statistic through the Statistic property,
// and the corresponding P-value through the Probability property:
Console.WriteLine($"Test statistic: {ksTest.Statistic:F4}");
Console.WriteLine($"P-value: {ksTest.PValue:F4}");
// We can now print the test results:
Console.WriteLine("Reject null hypothesis? {0}",
ksTest.Reject() ? "yes" : "no");
//
// Two-sample Kolmogorov-Smirnov Test
//
Console.WriteLine("\nTwo-sample Kolmogorov-Smirnov Test");
// We once again investigate the similarity between a lognormal and
// a Weibull distribution. However, this time, we use 25 random
// samples from each distribution.
// We already have the lognormal samples.
// Generate the samples from the Weibull distribution:
var weibullSample = weibull.Sample(25);
// Finally, we construct the Kolmogorov-Smirnov test:
var ksTest2 = new TwoSampleKolmogorovSmirnovTest(logNormalSample, weibullSample);
// We can obtan the value of the test statistic through the Statistic property,
// and the corresponding P-value through the Probability property:
Console.WriteLine($"Test statistic: {ksTest2.Statistic:F4}");
Console.WriteLine($"P-value: {ksTest2.PValue:F4}");
// We can now print the test results:
Console.WriteLine("Reject null hypothesis? {0}",
ksTest2.Reject() ? "yes" : "no");
//
// Anderson-Darling Test
//
Console.WriteLine("\nAnderson-Darling Test");
// The Anderson-Darling is defined for a small number of
// distributions. Currently, only the normal distribution
// is supported.
// We will investigate the distribution of the strength
// of polished airplane windows. The data comes from
// Fuller, e.al. (NIST, 1993) and represents the pressure
// (in psi).
// First, create a numerical variable:
var strength = Vector.Create(new double[]
{18.830, 20.800, 21.657, 23.030, 23.230, 24.050,
24.321, 25.500, 25.520, 25.800, 26.690, 26.770,
26.780, 27.050, 27.670, 29.900, 31.110, 33.200,
33.730, 33.760, 33.890, 34.760, 35.750, 35.910,
36.980, 37.080, 37.090, 39.580, 44.045, 45.290,
45.381});
// Let's print some summary statistics:
Console.WriteLine($"Number of observations: {strength.Length}");
Console.WriteLine($"Mean: {strength.Mean():F3}");
Console.WriteLine($"Standard deviation: {strength.StandardDeviation():F3}");
// The most refined test of normality is the Anderson-Darling test.
AndersonDarlingTest adTest = new AndersonDarlingTest(strength);
// We can obtan the value of the test statistic through the Statistic property,
// and the corresponding P-value through the Probability property:
Console.WriteLine($"Test statistic: {adTest.Statistic:F4}");
Console.WriteLine($"P-value: {adTest.PValue:F4}");
// We can now print the test results:
Console.WriteLine("Reject null hypothesis? {0}",
adTest.Reject() ? "yes" : "no");
Console.Write("Press any key to exit.");
Console.ReadLine();
}
}
}