public void FisherFromChiSquared()
{
// we will need a RNG
Random rng = new Random(314159);
int n1 = 1;
int n2 = 2;
// define chi squared distributions
ContinuousDistribution d1 = new ChiSquaredDistribution(n1);
ContinuousDistribution d2 = new ChiSquaredDistribution(n2);
// create a sample of chi-squared variates
Sample s = new Sample();
for (int i = 0; i < 250; i++)
{
double x1 = d1.GetRandomValue(rng);
double x2 = d2.GetRandomValue(rng);
double x = (x1 / n1) / (x2 / n2);
s.Add(x);
}
// it should match a Fisher distribution with the appropriate parameters
ContinuousDistribution f0 = new FisherDistribution(n1, n2);
TestResult t0 = s.KuiperTest(f0);
Assert.IsTrue(t0.Probability > 0.05);
// it should be distinguished from a Fisher distribution with different parameters
ContinuousDistribution f1 = new FisherDistribution(n1 + 1, n2);
TestResult t1 = s.KuiperTest(f1);
Assert.IsTrue(t1.Probability < 0.05);
}
public void FitDataToPolynomialChiSquaredTest()
{
// we want to make sure that the chi^2 values we are producing from polynomial fits are distributed as expected
// create a sample to hold chi^2 values
Sample chis = new Sample();
// define a model
Interval r = Interval.FromEndpoints(-5.0, 15.0);
Func <double, double> fv = delegate(double x) {
return(1.0 * x - 2.0 * x * x);
};
Func <double, double> fu = delegate(double x) {
return(1.0 + 0.5 * Math.Sin(x));
};
// draw 50 data sets from the model and fit year
// store the resulting chi^2 value in the chi^2 set
for (int i = 0; i < 50; i++)
{
UncertainMeasurementSample xs = CreateDataSet(r, fv, fu, 10, i);
FitResult fit = xs.FitToPolynomial(2);
double chi = fit.GoodnessOfFit.Statistic;
chis.Add(chi);
}
// sanity check the sample
Assert.IsTrue(chis.Count == 50);
// test whether the chi^2 values are distributed as expected
ContinuousDistribution chiDistribution = new ChiSquaredDistribution(7);
TestResult ks = chis.KolmogorovSmirnovTest(chiDistribution);
Assert.IsTrue(ks.LeftProbability < 0.95);
}
public void Bug2811()
{
ChiSquaredDistribution d = new ChiSquaredDistribution(1798);
double x = d.InverseLeftProbability(0.975);
Console.WriteLine(x);
}
public void ChiAndChiSquared()
{
ChiDistribution chi = new ChiDistribution(3);
ChiSquaredDistribution chi2 = new ChiSquaredDistribution(3.0);
Assert.IsTrue(chi.DegreesOfFreedom == chi2.DegreesOfFreedom);
foreach (double x in TestUtilities.GenerateRealValues(1.0E-1, 1.0E1, 4))
{
Assert.IsTrue(TestUtilities.IsNearlyEqual(chi.LeftProbability(x), chi2.LeftProbability(x * x)));
}
}
public static double ValorDeTabla(int grados, double alfa)
{
if (!(grados > 0))
{
throw new NotSupportedException("Los grados de libertad deben ser mayores a cero");
}
Distribution d = new ChiSquaredDistribution(grados);
var valor = d.InverseRightProbability(alfa);
return(valor);
}
public static double valorDeTabla(int grados, string alfa)
{
double confianza = double.Parse(alfa, System.Globalization.CultureInfo.InvariantCulture);
if (!(grados > 0))
{
throw new NotSupportedException("Los grados de libertad deben ser mayores a cero!");
}
ContinuousDistribution d = new ChiSquaredDistribution(grados);
var valor = d.InverseRightProbability(confianza);
return(valor);
}
/// <summary>
/// Performs a χ<sup>2</sup> test comparing the histogram to the given distribution.
/// </summary>
/// <param name="distribution">The distribution against which to test the histogram.</param>
/// <returns>The test result.</returns>
public TestResult ChiSquaredTest(DiscreteDistribution distribution)
{
if (distribution == null)
{
throw new ArgumentNullException(nameof(distribution));
}
double chi2 = 0.0;
int dof = 0;
//int lastObservedCounts = extraCounts;
//double lastExpectedCounts = (distribution.LeftExclusiveProbability(0) + distribution.RightExclusiveProbability(counts.Length - 1)) * total;
int lastObservedCounts = 0;
double lastExpectedCounts = 0.0;
int observedCounts = 0;
double expectedCounts = 0.0;
for (int i = 0; i < storage.Count; i++)
{
observedCounts += storage.GetCounts(i + 1);
expectedCounts += distribution.ProbabilityMass(i) * storage.Total;
if (expectedCounts > 4.0)
{
if (lastExpectedCounts > 0.0)
{
chi2 += MoreMath.Sqr(lastObservedCounts - lastExpectedCounts) / lastExpectedCounts;
dof++;
}
lastObservedCounts = observedCounts;
lastExpectedCounts = expectedCounts;
observedCounts = 0;
expectedCounts = 0.0;
}
}
lastObservedCounts += observedCounts;
lastExpectedCounts += expectedCounts;
if (lastExpectedCounts > 0.0)
{
chi2 += MoreMath.Sqr(lastObservedCounts - lastExpectedCounts) / lastExpectedCounts;
}
ContinuousDistribution nullDistribution = new ChiSquaredDistribution(dof);
return(new TestResult("χ²", chi2, nullDistribution, TestType.RightTailed));
}
