/// <summary>
/// Creates a new test for two ROC curves.
/// </summary>
///
/// <param name="curve1">The first ROC curve.</param>
/// <param name="curve2">The second ROC curve.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two areas.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoReceiverOperatingCurveTest(ReceiverOperatingCharacteristic curve1, ReceiverOperatingCharacteristic curve2,
double hypothesizedDifference = 0, TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
this.Curve1 = curve1;
this.Curve2 = curve2;
double[] Vx1 = curve1.NegativeAccuracies;
double[] Vy1 = curve1.PositiveAccuracies;
double[] Vx2 = curve2.NegativeAccuracies;
double[] Vy2 = curve2.PositiveAccuracies;
double covx = Measures.Covariance(Vx1, Vx2);
double covy = Measures.Covariance(Vy1, Vy2);
double cov = covx / Vx1.Length + covy / Vy1.Length;
this.EstimatedValue1 = curve1.Area;
this.EstimatedValue2 = curve2.Area;
this.ObservedDifference = EstimatedValue1 - EstimatedValue2;
this.HypothesizedDifference = hypothesizedDifference;
this.Variance1 = curve1.Variance;
this.Variance2 = curve2.Variance;
this.OverallVariance = Variance1 + Variance2 - 2 * cov;
this.StandardError = System.Math.Sqrt(OverallVariance);
// Compute Z statistic
double z = (ObservedDifference - HypothesizedDifference) / StandardError;
Compute(z, alternate);
}
public TwoSampleHypothesisTestResult TestHypothesis(IEnumerable <double> sample1, IEnumerable <double> sample2, double hypothesizedDifference,
TwoSampleHypothesis alternateHypothesis, double alpha)
{
var sample1Collection = sample1 as ICollection <double> ?? sample1.ToArray();
var sample2Collection = sample2 as ICollection <double> ?? sample2.ToArray();
ITwoSampleNormalDistributionHypothesisTest test;
if (sample1Collection.Count < 30 ||
sample2Collection.Count < 30)
{
test = new TwoSampleTTestHypothesisTest();
}
else
{
test = new TwoSampleZTestHypothesisTest();
}
return(test
.TestHypothesis(
sample1Collection,
sample2Collection,
hypothesizedDifference,
alternateHypothesis,
alpha));
}
public static bool RunConcurrentPerformanceComparison(int numIterations, int degreeParallelism,
Action firstOperation, Action secondOperation, double hypothesizedDifference = 0,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent, bool outputDetails = false)
{
return RunConcurrentPerformanceComparison(numIterations, degreeParallelism, "first", firstOperation,
"second", secondOperation, hypothesizedDifference, hypothesis, outputDetails);
}
/// <summary>
/// Tests whether two samples comes from the
/// same distribution without assuming normality.
/// </summary>
///
/// <param name="sample1">The first sample.</param>
/// <param name="sample2">The second sample.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
/// <param name="exact">True to compute the exact distribution. May require a significant
/// amount of processing power for large samples (n > 12). If left at null, whether to
/// compute the exact or approximate distribution will depend on the number of samples.
/// Default is null.</param>
/// <param name="adjustForTies">Whether to account for ties when computing the
/// rank statistics or not. Default is true.</param>
///
public MannWhitneyWilcoxonTest(double[] sample1, double[] sample2,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent,
bool?exact = null, bool adjustForTies = true)
{
this.NumberOfSamples1 = sample1.Length;
this.NumberOfSamples2 = sample2.Length;
int n = NumberOfSamples1 + NumberOfSamples2;
// Concatenate both samples and rank them
double[] samples = sample1.Concatenate(sample2);
double[] rank = samples.Rank(adjustForTies: true);
// Split the rankings back and sum
Rank1 = rank.Get(0, NumberOfSamples1);
Rank2 = rank.Get(NumberOfSamples1, 0);
// Compute rank sum statistic
this.RankSum1 = Rank1.Sum();
this.RankSum2 = Rank2.Sum();
// It is not necessary to compute the sum for both ranks. The sum of ranks in the second
// sample can be determined from the first, since the sum of all the ranks equals n(n+1)/2
Accord.Diagnostics.Debug.Assert(RankSum2 == n * (n + 1) / 2 - RankSum1);
// The U statistic can be obtained from the sum of the ranks in the sample,
// minus the smallest value it can take (i.e. minus (n1 * (n1 + 1)) / 2.0),
// meaning there is an wasy way to convert from W to U:
// Compute Mann-Whitney's U statistic from the rank sum
// as in Zar, Jerrold H. Biostatistical Analysis, 1998:
this.Statistic1 = RankSum1 - (NumberOfSamples1 * (NumberOfSamples1 + 1)) / 2.0; // U1
this.Statistic2 = RankSum2 - (NumberOfSamples2 * (NumberOfSamples2 + 1)) / 2.0; // U2
// Again, it would not be necessary to compute U2 due the relation:
Accord.Diagnostics.Debug.Assert(Statistic1 + Statistic2 == NumberOfSamples1 * NumberOfSamples2);
Accord.Diagnostics.Debug.Assert(Statistic1 == MannWhitneyDistribution.MannWhitneyU(Rank1));
Accord.Diagnostics.Debug.Assert(Statistic2 == MannWhitneyDistribution.MannWhitneyU(Rank2));
// http://users.sussex.ac.uk/~grahamh/RM1web/WilcoxonHandoout2011.pdf
// https://onlinecourses.science.psu.edu/stat464/node/38
// http://www.real-statistics.com/non-parametric-tests/wilcoxon-rank-sum-test/
// http://personal.vu.nl/R.Heijungs/QM/201516/stat/bs/documents/Supplement%2016B%20-%20Wilcoxon%20Mann-Whitney%20Small%20Sample%20Test.pdf
// http://www.real-statistics.com/non-parametric-tests/wilcoxon-rank-sum-test/wilcoxon-rank-sum-exact-test/
// The smaller value of U1 and U2 is the one used when using significance tables
this.Statistic = (NumberOfSamples1 < NumberOfSamples2) ? Statistic1 : Statistic2;
this.Hypothesis = alternate;
this.Tail = (DistributionTail)alternate;
this.StatisticDistribution = new MannWhitneyDistribution(Rank1, Rank2, exact)
{
Correction = (Tail == DistributionTail.TwoTail) ? ContinuityCorrection.Midpoint : ContinuityCorrection.KeepInside
};
this.PValue = StatisticToPValue(Statistic);
this.OnSizeChanged();
}
/// <summary>
/// Constructs a Z test.
/// </summary>
///
/// <param name="sample1">The first data sample.</param>
/// <param name="sample2">The second data sample.</param>
/// <param name="hypothesizedDifference">The hypothesized sample difference.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoSampleZTest(double[] sample1, double[] sample2, double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
int samples1 = sample1.Length;
int samples2 = sample2.Length;
if (samples1 < 30 || samples2 < 30)
{
System.Diagnostics.Trace.TraceWarning(
"Warning: running a Z test for less than 30 samples. Consider running a Student's T Test instead.");
}
double mean1 = Tools.Mean(sample1);
double mean2 = Tools.Mean(sample2);
double var1 = Tools.Variance(sample1, mean1);
double var2 = Tools.Variance(sample2, mean2);
double sqStdError1 = var1 / sample1.Length;
double sqStdError2 = var2 / sample2.Length;
this.Compute(mean1, mean2, sqStdError1, sqStdError2, hypothesizedDifference, alternate);
this.power(var1, var2, sample1.Length, sample2.Length);
}
/// <summary>
/// Tests whether two samples comes from the
/// same distribution without assuming normality.
/// </summary>
///
/// <param name="sample1">The first sample.</param>
/// <param name="sample2">The second sample.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public MannWhitneyWilcoxonTest(double[] sample1, double[] sample2,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
int n1 = sample1.Length;
int n2 = sample2.Length;
int n = n1 + n2;
// Concatenate both samples and rank them
double[] samples = sample1.Concatenate(sample2);
double[] rank = Accord.Statistics.Tools.Rank(samples);
// Split the rankings back and sum
Rank1 = rank.Submatrix(0, n1 - 1);
Rank2 = rank.Submatrix(n1, n - 1);
double t1 = RankSum1 = Rank1.Sum();
double t2 = RankSum2 = Rank2.Sum();
// Estimated values for t under the null
double t1max = n1 * n2 + (n1 * (n1 + 1)) / 2.0;
double t2max = n1 * n2 + (n2 * (n2 + 1)) / 2.0;
// Diff in observed t and estimated t
double u1 = Statistic1 = t1max - t1;
double u2 = Statistic2 = t2max - t2;
double hypothesizedValue = (n1 * n2) / 2.0;
Compute(u1, rank, n1, n2, alternate);
}
/// <summary>
/// Tests whether the means of two samples are different.
/// </summary>
/// <param name="mean1">The first sample's mean.</param>
/// <param name="mean2">The second sample's mean.</param>
/// <param name="var1">The first sample's variance.</param>
/// <param name="var2">The second sample's variance.</param>
/// <param name="samples1">The number of observations in the first sample.</param>
/// <param name="samples2">The number of observations in the second sample.</param>
/// <param name="assumeEqualVariances">True assume equal variances, false otherwise. Default is true.</param>
/// <param name="hypothesizedDifference">The hypothesized sample difference.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
public TwoSampleTTest(double mean1, double var1, int samples1, double mean2, double var2, int samples2,
bool assumeEqualVariances = true, double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
Compute(mean1, var1, samples1, mean2, var2, samples2,
hypothesizedDifference, assumeEqualVariances, alternate);
}
public static bool RunConcurrentPerformanceComparison(int numIterations, int degreeParallelism,
Action firstOperation, Action secondOperation, double hypothesizedDifference = 0,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent, bool outputDetails = false)
{
return(RunConcurrentPerformanceComparison(numIterations, degreeParallelism, "first", firstOperation,
"second", secondOperation, hypothesizedDifference, hypothesis, outputDetails));
}
/// <summary>
/// Computes the two sample sign test.
/// </summary>
///
protected void Compute(int positive, int total, TwoSampleHypothesis alternate)
{
this.Hypothesis = alternate;
// The underlying test is to check whether the probability
// value from the samples are higher than or lesser than 0.5,
// thus the actual Binomial test hypothesis is inverted:
OneSampleHypothesis binomialHypothesis;
switch (alternate)
{
case TwoSampleHypothesis.ValuesAreDifferent:
binomialHypothesis = OneSampleHypothesis.ValueIsDifferentFromHypothesis; break;
case TwoSampleHypothesis.FirstValueIsGreaterThanSecond:
binomialHypothesis = OneSampleHypothesis.ValueIsSmallerThanHypothesis; break;
case TwoSampleHypothesis.FirstValueIsSmallerThanSecond:
binomialHypothesis = OneSampleHypothesis.ValueIsGreaterThanHypothesis; break;
default: throw new InvalidOperationException();
}
base.Compute(positive, total, 0.5, binomialHypothesis);
}
/// <summary>
/// Creates a new sign test for two samples.
/// </summary>
///
/// <param name="sample1">The first sample of observations.</param>
/// <param name="sample2">The second sample of observations.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoSampleSignTest(double[] sample1, double[] sample2, TwoSampleHypothesis alternate)
{
if (sample1.Length != sample2.Length)
{
throw new DimensionMismatchException("sample2", "Both samples should be of the same size.");
}
int positive = 0;
int negative = 0;
for (int i = 0; i < sample1.Length; i++)
{
double d = sample2[i] - sample1[i];
if (d > 0)
{
positive++;
}
else if (d < 0)
{
negative++;
}
}
Compute(positive, positive + negative, alternate);
}
/// <summary>
/// Creates a new test for two ROC curves.
/// </summary>
///
/// <param name="curve1">The first ROC curve.</param>
/// <param name="curve2">The second ROC curve.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two areas.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoReceiverOperatingCurveTest(ReceiverOperatingCharacteristic curve1, ReceiverOperatingCharacteristic curve2,
double hypothesizedDifference = 0, TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
this.Curve1 = curve1;
this.Curve2 = curve2;
double[] Vx1 = curve1.NegativeAccuracies;
double[] Vy1 = curve1.PositiveAccuracies;
double[] Vx2 = curve2.NegativeAccuracies;
double[] Vy2 = curve2.PositiveAccuracies;
double covx = Measures.Covariance(Vx1, Vx2);
double covy = Measures.Covariance(Vy1, Vy2);
double cov = covx / Vx1.Length + covy / Vy1.Length;
this.EstimatedValue1 = curve1.Area;
this.EstimatedValue2 = curve2.Area;
this.ObservedDifference = EstimatedValue1 - EstimatedValue2;
this.HypothesizedDifference = hypothesizedDifference;
this.Variance1 = curve1.Variance;
this.Variance2 = curve2.Variance;
this.OverallVariance = Variance1 + Variance2 - 2 * cov;
this.StandardError = System.Math.Sqrt(OverallVariance);
// Compute Z statistic
double z = (ObservedDifference - HypothesizedDifference) / StandardError;
Compute(z, alternate);
}
/// <summary>
/// Creates a new paired t-test.
/// </summary>
///
/// <param name="sample1">The observations in the first sample.</param>
/// <param name="sample2">The observations in the second sample.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public PairedTTest(double[] sample1, double[] sample2,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
if (sample1.Length != sample2.Length)
{
throw new DimensionMismatchException("sample2", "Paired samples must have the same size.");
}
int n = sample1.Length;
double[] delta = new double[sample1.Length];
for (int i = 0; i < delta.Length; i++)
{
delta[i] = sample1[i] - sample2[i];
}
double mean = delta.Mean();
double std = delta.StandardDeviation();
StandardError = std / Math.Sqrt(n);
ObservedDifference = mean;
Mean1 = sample1.Mean();
Mean2 = sample2.Mean();
SampleSize = n;
Statistic = ObservedDifference / StandardError;
StatisticDistribution = new TDistribution(n - 1);
this.Hypothesis = alternate;
this.Tail = (DistributionTail)alternate;
this.PValue = StatisticToPValue(Statistic);
this.OnSizeChanged();
}
/// <summary>
/// Creates a new Two-Table Mean Kappa test.
/// </summary>
///
/// <param name="matrices1">The first group of contingency tables.</param>
/// <param name="matrices2">The second group of contingency tables.</param>
/// <param name="assumeEqualVariances">True to assume equal variances, false otherwise. Default is true.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two average Kappa values.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoAverageKappaTest(GeneralConfusionMatrix[] matrices1, GeneralConfusionMatrix[] matrices2,
double hypothesizedDifference = 0, bool assumeEqualVariances = true,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
double[] kappas1 = new double[matrices1.Length];
for (int i = 0; i < matrices1.Length; i++)
{
kappas1[i] = matrices1[i].Kappa;
}
double[] kappas2 = new double[matrices1.Length];
for (int i = 0; i < matrices2.Length; i++)
{
kappas2[i] = matrices2[i].Kappa;
}
double meanKappa1 = kappas1.Mean();
double meanKappa2 = kappas2.Mean();
Variance1 = kappas1.Variance(meanKappa1);
Variance2 = kappas2.Variance(meanKappa2);
int kappaSamples1 = matrices1.Length;
int kappaSamples2 = matrices2.Length;
base.Compute(
meanKappa1, Variance1, kappaSamples1,
meanKappa2, Variance2, kappaSamples2,
hypothesizedDifference, assumeEqualVariances, alternate);
}
/// <summary>
/// Creates a new Z-Test for two sample proportions.
/// </summary>
///
/// <param name="proportion1">The proportion of success observations in the first sample.</param>
/// <param name="sampleSize1">The total number of observations in the first sample.</param>
/// <param name="proportion2">The proportion of success observations in the second sample.</param>
/// <param name="sampleSize2">The total number of observations in the second sample.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoProportionZTest(double proportion1, int sampleSize1, double proportion2, int sampleSize2,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
int x1 = (int)Math.Round(proportion1 * sampleSize1);
int x2 = (int)Math.Round(proportion2 * sampleSize2);
Compute(x1, sampleSize1, x2, sampleSize2, alternate);
}
/// <summary>
/// Tests whether the means of two samples are different.
/// </summary>
/// <param name="sample1">The first sample.</param>
/// <param name="sample2">The second sample.</param>
/// <param name="hypothesizedDifference">The hypothesized sample difference.</param>
/// <param name="assumeEqualVariances">True to assume equal variances, false otherwise. Default is true.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
public TwoSampleZTest(DescriptiveResult sample1, DescriptiveResult sample2,
double hypothesizedDifference = 0, TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
// References: http://en.wikipedia.org/wiki/Student's_t-test#Worked_examples
Compute(sample1.Mean, sample2.Mean, sample1.Variance / sample1.Count, sample2.Variance / sample2.Count,
hypothesizedDifference, alternate);
}
/// <summary>
/// Creates a new Z-Test for two sample proportions.
/// </summary>
///
/// <param name="proportion1">The proportion of success observations in the first sample.</param>
/// <param name="sampleSize1">The total number of observations in the first sample.</param>
/// <param name="proportion2">The proportion of success observations in the second sample.</param>
/// <param name="sampleSize2">The total number of observations in the second sample.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoProportionZTest(double proportion1, int sampleSize1, double proportion2, int sampleSize2,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
int x1 = (int)Math.Round(proportion1 * sampleSize1);
int x2 = (int)Math.Round(proportion2 * sampleSize2);
Compute(x1, sampleSize1, x2, sampleSize2, alternate);
}
public BothLatencyValidator(
double alpha, TwoSampleHypothesis alternateHypothesis,
// TODO: P2 - per the link, appears this may be the number of standard deviations? http://accord-framework.net/docs/html/T_Accord_Statistics_Testing_Power_TwoSampleTTestPowerAnalysis.htm
double minimumDetectableDifferenceDesired = 0.00001, // Some number larger than 0
double testStatisticalPower = 0.8 // the test power that we want. 0.8 is a standard - 0.9 is more conservative -- 1 - probability of rejecting the null hypothesis when the null hypothesis is actually false
)
: this(alpha, alternateHypothesis, 0.Percent(), minimumDetectableDifferenceDesired, testStatisticalPower)
{
}
/// <summary>
/// Computes the Z test.
/// </summary>
protected void Compute(double statistic, TwoSampleHypothesis alternate)
{
Statistic = statistic;
StatisticDistribution = NormalDistribution.Standard;
Hypothesis = alternate;
PValue = StatisticToPValue(Statistic);
}
/// <summary>
/// Computes the F-test.
/// </summary>
///
protected void Compute(double statistic, int d1, int d2, TwoSampleHypothesis alternate)
{
this.Statistic = statistic;
this.StatisticDistribution = new FDistribution(d1, d2);
this.Hypothesis = alternate;
this.Tail = (DistributionTail)alternate;
this.PValue = StatisticToPValue(Statistic);
this.OnSizeChanged();
}
/// <summary>
/// Computes the Mann-Whitney-Wilcoxon test.
/// </summary>
///
protected void Compute(double statistic, double[] ranks, int n1, int n2, TwoSampleHypothesis alternate)
{
this.Statistic = statistic;
this.StatisticDistribution = new MannWhitneyDistribution(ranks, n1, n2);
this.Hypothesis = alternate;
this.Tail = (DistributionTail)alternate;
this.PValue = StatisticToPValue(Statistic);
this.OnSizeChanged();
}
/// <summary>
/// Computes the Z test.
/// </summary>
///
protected void Compute(double observedDifference, double hypothesizedDifference,
double standardError, TwoSampleHypothesis alternate)
{
this.ObservedDifference = observedDifference;
this.HypothesizedDifference = hypothesizedDifference;
this.StandardError = standardError;
// Compute Z statistic
double z = (ObservedDifference - HypothesizedDifference) / StandardError;
Compute(z, alternate);
}
public GeneralHypothesisTest(string firstLabel, DescriptiveResult sample1, string secondLabel,
DescriptiveResult sample2, double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
int samples1 = sample1.Count;
int samples2 = sample2.Count;
HypothesizedDifference = Math.Abs(hypothesizedDifference);
var s1 = new SampleInfo
{
Name = firstLabel,
Count = sample1.Count,
Mean = sample1.Mean,
StdDev = sample1.StdDev
};
var s2 = new SampleInfo
{
Name = secondLabel,
Count = sample2.Count,
Mean = sample2.Mean,
StdDev = sample2.StdDev
};
Result = new ComparisonResult
{
FirstSample = s1,
SecondSample = s2,
Hypothesis = alternate,
HypothesizedDifference = HypothesizedDifference
};
if (samples1 < 30 || samples2 < 30)
{
_tTest = new TwoSampleTTest(sample1, sample2, false, HypothesizedDifference, alternate);
Result.Confidence = _tTest.Confidence;
Result.ObservedDifference = _tTest.ObservedDifference;
Result.Significant = _tTest.Significant;
Result.Size = _tTest.Size;
Result.StandardError = _tTest.StandardError;
}
else
{
_zTest = new TwoSampleZTest(sample1, sample2, HypothesizedDifference, alternate);
Result.Confidence = _zTest.Confidence;
Result.ObservedDifference = _zTest.ObservedDifference;
Result.Significant = _zTest.Significant;
Result.Size = _zTest.Size;
Result.StandardError = _zTest.StandardError;
}
}
/// <summary>
/// Computes the Z test.
/// </summary>
///
protected void Compute(double statistic, TwoSampleHypothesis alternate)
{
this.Statistic = statistic;
this.StatisticDistribution = NormalDistribution.Standard;
this.Hypothesis = alternate;
this.Tail = (DistributionTail)alternate;
this.PValue = StatisticToPValue(Statistic);
this.OnSizeChanged();
}
/// <summary>
/// Computes the Z test.
/// </summary>
///
protected void Compute(double value1, double value2,
double squareStdError1, double squareStdError2,
double hypothesizedDifference, TwoSampleHypothesis alternate)
{
this.EstimatedValue1 = value1;
this.EstimatedValue2 = value2;
double diff = value1 - value2;
double stdError = Math.Sqrt(squareStdError1 + squareStdError2);
Compute(diff, hypothesizedDifference, stdError, alternate);
}
/// <summary>
/// Creates a new Two-Table Mean Kappa test.
/// </summary>
///
/// <param name="meanKappa1">The average kappa value for the first group of contingency tables.</param>
/// <param name="meanKappa2">The average kappa value for the second group of contingency tables.</param>
/// <param name="varKappa1">The kappa's variance in the first group of tables.</param>
/// <param name="varKappa2">The kappa's variance in the first group of tables.</param>
/// <param name="kappaSamples1">The number of contingency tables averaged in the first group.</param>
/// <param name="kappaSamples2">The number of contingency tables averaged in the second group.</param>
/// <param name="assumeEqualVariances">True to assume equal variances, false otherwise. Default is true.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two Kappa values.</param>
///
public TwoAverageKappaTest(double meanKappa1, double varKappa1, int kappaSamples1,
double meanKappa2, double varKappa2, int kappaSamples2,
double hypothesizedDifference = 0, bool assumeEqualVariances = true,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
Variance1 = varKappa1;
Variance2 = varKappa2;
base.Compute(
meanKappa1, varKappa1, kappaSamples1,
meanKappa2, varKappa2, kappaSamples2,
hypothesizedDifference, assumeEqualVariances, alternate);
}
/// <summary>
/// Creates a new Two-Table Mean Kappa test.
/// </summary>
///
/// <param name="meanKappa1">The average kappa value for the first group of contingency tables.</param>
/// <param name="meanKappa2">The average kappa value for the second group of contingency tables.</param>
/// <param name="varKappa1">The kappa's variance in the first group of tables.</param>
/// <param name="varKappa2">The kappa's variance in the first group of tables.</param>
/// <param name="kappaSamples1">The number of contingency tables averaged in the first group.</param>
/// <param name="kappaSamples2">The number of contingency tables averaged in the second group.</param>
/// <param name="assumeEqualVariances">True to assume equal variances, false otherwise. Default is true.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two Kappa values.</param>
///
public TwoAverageKappaTest(double meanKappa1, double varKappa1, int kappaSamples1,
double meanKappa2, double varKappa2, int kappaSamples2,
double hypothesizedDifference = 0, bool assumeEqualVariances = true,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
Variance1 = varKappa1;
Variance2 = varKappa2;
base.Compute(
meanKappa1, varKappa1, kappaSamples1,
meanKappa2, varKappa2, kappaSamples2,
hypothesizedDifference, assumeEqualVariances, alternate);
}
public GeneralHypothesisTest(string firstLabel, DescriptiveResult sample1, string secondLabel,
DescriptiveResult sample2, double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
int samples1 = sample1.Count;
int samples2 = sample2.Count;
HypothesizedDifference = Math.Abs(hypothesizedDifference);
var s1 = new SampleInfo
{
Name = firstLabel,
Count = sample1.Count,
Mean = sample1.Mean,
StdDev = sample1.StdDev
};
var s2 = new SampleInfo
{
Name = secondLabel,
Count = sample2.Count,
Mean = sample2.Mean,
StdDev = sample2.StdDev
};
Result = new ComparisonResult
{
FirstSample = s1,
SecondSample = s2,
Hypothesis = alternate,
HypothesizedDifference = HypothesizedDifference
};
if (samples1 < 30 || samples2 < 30)
{
_tTest = new TwoSampleTTest(sample1, sample2, false, HypothesizedDifference, alternate);
Result.Confidence = _tTest.Confidence;
Result.ObservedDifference = _tTest.ObservedDifference;
Result.Significant = _tTest.Significant;
Result.Size = _tTest.Size;
Result.StandardError = _tTest.StandardError;
}
else
{
_zTest = new TwoSampleZTest(sample1, sample2, HypothesizedDifference, alternate);
Result.Confidence = _zTest.Confidence;
Result.ObservedDifference = _zTest.ObservedDifference;
Result.Significant = _zTest.Significant;
Result.Size = _zTest.Size;
Result.StandardError = _zTest.StandardError;
}
}
public static TwoSampleHypothesisTestResult TestHypothesis(
this ITwoSampleNormalDistributionHypothesisTest source,
BenchmarkResults.BeforeAndAfter resultMeasurement,
double hypothesizedDifference,
TwoSampleHypothesis alternateHypothesis,
double alpha)
{
return(source.TestHypothesis(
resultMeasurement.Baseline.GetResultRuns().Select(run => MeasurementExtensions.GetAverageNanoseconds(run)),
resultMeasurement.Treatment.GetResultRuns().Select(run => run.GetAverageNanoseconds()),
hypothesizedDifference,
alternateHypothesis,
alpha));
}
public TwoSampleHypothesisTestResult TestHypothesis(IEnumerable <double> sample1, IEnumerable <double> sample2, double hypothesizedDifference,
TwoSampleHypothesis alternateHypothesis, double alpha)
{
var test = new TwoSampleZTest(
sample1.ToArray(),
sample2.ToArray(),
hypothesizedDifference: hypothesizedDifference,
alternate: alternateHypothesis);
test.Size = alpha;
return(new TwoSampleHypothesisTestResult(
test.Significant,
test.GetConfidenceInterval(1 - alpha),
test.ObservedDifference));
}
/// <summary>
/// Estimates the number of samples necessary to attain the
/// required power level for the given effect size.
/// </summary>
///
/// <param name="delta">The minimum detectable difference.</param>
/// <param name="standardDeviation">The difference standard deviation.</param>
/// <param name="power">The desired power level. Default is 0.8.</param>
/// <param name="alpha">The desired significance level. Default is 0.05.</param>
/// <param name="hypothesis">The alternative hypothesis (research hypothesis) to be tested.</param>
/// <param name="proportion">The proportion of observations in the second group
/// when compared to the first group. A proportion of 2:1 results in twice more
/// samples in the second group than in the first. Default is 1.</param>
///
/// <returns>The required number of samples.</returns>
///
public static TwoSampleZTestPowerAnalysis GetSampleSize(double delta,
double standardDeviation = 1, double proportion = 1.0, double power = 0.8, double alpha = 0.05,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent)
{
var analysis = new TwoSampleZTestPowerAnalysis(hypothesis)
{
Effect = (delta) / standardDeviation,
Size = alpha,
Power = power,
};
analysis.ComputeSamples(proportion);
return(analysis);
}
public static ComparisonResult RunConcurrentPerformanceComparison(int numIterations, int degreeParallelism,
string firstLabel,
Action firstOperation, string secondLabel, Action secondOperation, double hypothesizedDifference = 0,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent)
{
var result1 = RunConcurrentPerformanceTest(numIterations, degreeParallelism, firstOperation);
var result2 = RunConcurrentPerformanceTest(numIterations, degreeParallelism, secondOperation);
var comparison = new GeneralHypothesisTest(firstLabel, result1.DescriptiveResult, secondLabel,
result2.DescriptiveResult,
hypothesizedDifference, hypothesis);
comparison.Result.FirstSample.Details = result1;
comparison.Result.SecondSample.Details = result2;
return(comparison.Result);
}
/// <summary>
/// Creates a new Two-Table Kappa test.
/// </summary>
///
/// <param name="kappa1">The kappa value for the first contingency table to test.</param>
/// <param name="kappa2">The kappa value for the second contingency table to test.</param>
/// <param name="var1">The variance of the kappa value for the first contingency table to test.</param>
/// <param name="var2">The variance of the kappa value for the second contingency table to test.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two Kappa values.</param>
///
public TwoMatrixKappaTest(double kappa1, double var1, double kappa2, double var2, double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
this.EstimatedValue1 = kappa1;
this.EstimatedValue2 = kappa2;
this.Variance1 = var1;
this.Variance2 = var2;
this.OverallVariance = Variance1 + Variance2;
double diff = Math.Abs(EstimatedValue1 - EstimatedValue2);
double stdError = Math.Sqrt(OverallVariance);
Compute(diff, hypothesizedDifference, stdError, alternate);
}
/// <summary>
/// Creates a new Two-Table Kappa test.
/// </summary>
///
/// <param name="matrix1">The first contingency table to test.</param>
/// <param name="matrix2">The second contingency table to test.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two Kappa values.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoMatrixKappaTest(GeneralConfusionMatrix matrix1, GeneralConfusionMatrix matrix2, double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
this.EstimatedValue1 = matrix1.Kappa;
this.EstimatedValue2 = matrix2.Kappa;
this.Variance1 = matrix1.Variance;
this.Variance2 = matrix2.Variance;
this.OverallVariance = Variance1 + Variance2;
double diff = Math.Abs(EstimatedValue1 - EstimatedValue2);
double stdError = Math.Sqrt(OverallVariance);
Compute(diff, hypothesizedDifference, stdError, alternate);
}
/// <summary>
/// Creates a new Two-Table Kappa test.
/// </summary>
///
/// <param name="kappa1">The kappa value for the first contingency table to test.</param>
/// <param name="kappa2">The kappa value for the second contingency table to test.</param>
/// <param name="var1">The variance of the kappa value for the first contingency table to test.</param>
/// <param name="var2">The variance of the kappa value for the second contingency table to test.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two Kappa values.</param>
///
public TwoMatrixKappaTest(double kappa1, double var1, double kappa2, double var2, double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
this.EstimatedValue1 = kappa1;
this.EstimatedValue2 = kappa2;
this.Variance1 = var1;
this.Variance2 = var2;
this.OverallVariance = Variance1 + Variance2;
double diff = Math.Abs(EstimatedValue1 - EstimatedValue2);
double stdError = Math.Sqrt(OverallVariance);
Compute(diff, hypothesizedDifference, stdError, alternate);
}
/// <summary>
/// Estimates the number of samples necessary to attain the
/// required power level for the given effect size.
/// </summary>
///
/// <param name="sampleSize1">The number of observations in the first sample.</param>
/// <param name="sampleSize2">The number of observations in the second sample.</param>
/// <param name="power">The desired power level. Default is 0.8.</param>
/// <param name="alpha">The desired significance level. Default is 0.05.</param>
/// <param name="hypothesis">The alternative hypothesis (research hypothesis) to be tested.</param>
///
/// <returns>The required number of samples.</returns>
///
public static TwoSampleZTestPowerAnalysis GetEffectSize(int sampleSize1, int sampleSize2,
double power = 0.8, double alpha = 0.05,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent)
{
var analysis = new TwoSampleZTestPowerAnalysis(hypothesis)
{
Samples1 = sampleSize1,
Samples2 = sampleSize2,
Size = alpha,
Power = power,
};
analysis.ComputeEffect();
return(analysis);
}
public void WilcoxonSignedRankTestConstructorTest()
{
// Example from http://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test
double[] sample1 = { 125, 115, 130, 140, 140, 115, 140, 125, 140, 135 };
double[] sample2 = { 110, 122, 125, 120, 140, 124, 123, 137, 135, 145 };
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent;
var target = new TwoSampleWilcoxonSignedRankTest(sample1, sample2, alternate);
// Wikipedia uses an alternate definition for the W statistic. The framework
// uses the positive W statistic. There is no difference, if the proper
// respective statistical tables (or distributions) are followed.
Assert.AreEqual(27, target.Statistic);
Assert.IsFalse(target.Significant);
}
public static ComparisonResult RunConcurrentPerformanceComparison(int numIterations, int degreeParallelism,
string firstLabel,
Action firstOperation, string secondLabel, Action secondOperation, double hypothesizedDifference = 0,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent)
{
var result1 = RunConcurrentPerformanceTest(numIterations, degreeParallelism, firstOperation);
var result2 = RunConcurrentPerformanceTest(numIterations, degreeParallelism, secondOperation);
var comparison = new GeneralHypothesisTest(firstLabel, result1.DescriptiveResult, secondLabel,
result2.DescriptiveResult,
hypothesizedDifference, hypothesis);
comparison.Result.FirstSample.Details = result1;
comparison.Result.SecondSample.Details = result2;
return comparison.Result;
}
/// <summary>
/// Creates a new sign test for two samples.
/// </summary>
///
/// <param name="sample1">The first sample of observations.</param>
/// <param name="sample2">The second sample of observations.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoSampleSignTest(double[] sample1, double[] sample2, TwoSampleHypothesis alternate)
{
if (sample1.Length != sample2.Length)
throw new DimensionMismatchException("sample2", "Both samples should be of the same size.");
int positive = 0;
int negative = 0;
for (int i = 0; i < sample1.Length; i++)
{
double d = sample2[i] - sample1[i];
if (d > 0) positive++;
else if (d < 0) negative++;
}
Compute(positive, positive + negative, alternate);
}
/// <summary>
/// Computes the Z-test for two sample proportions.
/// </summary>
///
protected void Compute(
int successes1, int trials1,
int successes2, int trials2,
TwoSampleHypothesis alternate)
{
double p1 = successes1 / (double)trials1;
double p2 = successes2 / (double)trials2;
double p = (successes1 + successes2) / (double)(trials1 + trials2);
EstimatedValue1 = p1;
EstimatedValue2 = p2;
ObservedDifference = p1 - p2;
StandardError = Math.Sqrt(p * (1 - p) * (1.0 / trials1 + 1.0 / trials2));
double z = ObservedDifference / StandardError;
Compute(z, alternate);
}
public static bool RunConcurrentPerformanceComparison(int numIterations, int degreeParallelism,
string firstLabel,
Action firstOperation, string secondLabel, Action secondOperation, double hypothesizedDifference = 0,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent, bool outputDetails = false)
{
var comparison = RunConcurrentPerformanceComparison(numIterations, degreeParallelism, firstLabel, firstOperation,
secondLabel, secondOperation, hypothesizedDifference, hypothesis);
if (outputDetails)
{
Console.WriteLine(comparison.ToString());
Console.WriteLine("-----{0}-----", firstLabel);
Console.WriteLine(comparison.FirstSample.Details.ToString());
Console.WriteLine("-----{0}-----", secondLabel);
Console.WriteLine(comparison.SecondSample.Details.ToString());
}
return comparison.Significant;
}
/// <summary>
/// Tests whether the medians of two paired samples are different.
/// </summary>
///
/// <param name="sample1">The first sample.</param>
/// <param name="sample2">The second sample.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoSampleWilcoxonSignedRankTest(double[] sample1, double[] sample2,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
if (sample1.Length != sample2.Length)
throw new DimensionMismatchException("sample2", "Both samples should be of the same size.");
int[] signs = new int[sample1.Length];
double[] diffs = new double[sample1.Length];
// 1. Compute absolute difference and sign function
for (int i = 0; i < sample1.Length; i++)
{
double d = sample1[i] - sample2[i];
signs[i] = Math.Sign(d);
diffs[i] = Math.Abs(d);
}
this.Hypothesis = alternate;
Compute(signs, diffs, (DistributionTail)alternate);
}
public GeneralHypothesisTest(DescriptiveResult sample1, DescriptiveResult sample2,
double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
: this("first", sample1, "second", sample2, hypothesizedDifference, alternate)
{
}
/// <summary>
/// Estimates the number of samples necessary to attain the
/// required power level for the given effect size.
/// </summary>
///
/// <param name="delta">The minimum detectable difference.</param>
/// <param name="variance1">The first sample variance.</param>
/// <param name="variance2">The second sample variance.</param>
/// <param name="power">The desired power level. Default is 0.8.</param>
/// <param name="alpha">The desired significance level. Default is 0.05.</param>
/// <param name="proportion">The proportion of observations in the second group
/// when compared to the first group. A proportion of 2:1 results in twice more
/// samples in the second group than in the first. Default is 1.</param>
/// <param name="hypothesis">The alternative hypothesis (research hypothesis) to be tested.</param>
///
/// <returns>The required number of samples.</returns>
///
public static TwoSampleTTestPowerAnalysis GetSampleSize(double delta,
double variance1, double variance2, double proportion = 1.0, double power = 0.8,
double alpha = 0.05, TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent)
{
double standardDeviation = Math.Sqrt((variance1 + variance2) / 2.0);
var analysis = new TwoSampleTTestPowerAnalysis(hypothesis)
{
Effect = delta / standardDeviation,
Size = alpha,
Power = power,
};
analysis.ComputeSamples(proportion);
return analysis;
}
/// <summary>
/// Creates a new Two-Table Mean Kappa test.
/// </summary>
///
/// <param name="matrices1">The first group of contingency tables.</param>
/// <param name="matrices2">The second group of contingency tables.</param>
/// <param name="assumeEqualVariances">True to assume equal variances, false otherwise. Default is true.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two average Kappa values.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoAverageKappaTest(GeneralConfusionMatrix[] matrices1, GeneralConfusionMatrix[] matrices2,
double hypothesizedDifference = 0, bool assumeEqualVariances = true,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
double[] kappas1 = new double[matrices1.Length];
for (int i = 0; i < matrices1.Length; i++)
kappas1[i] = matrices1[i].Kappa;
double[] kappas2 = new double[matrices1.Length];
for (int i = 0; i < matrices2.Length; i++)
kappas2[i] = matrices2[i].Kappa;
double meanKappa1 = kappas1.Mean();
double meanKappa2 = kappas2.Mean();
Variance1 = kappas1.Variance(meanKappa1);
Variance2 = kappas2.Variance(meanKappa2);
int kappaSamples1 = matrices1.Length;
int kappaSamples2 = matrices2.Length;
base.Compute(
meanKappa1, Variance1, kappaSamples1,
meanKappa2, Variance2, kappaSamples2,
hypothesizedDifference, assumeEqualVariances, alternate);
}
/// <summary>
/// Computes the two sample sign test.
/// </summary>
///
protected void Compute(int positive, int total, TwoSampleHypothesis alternate)
{
this.Hypothesis = alternate;
// The underlying test is to check whether the probability
// value from the samples are higher than or lesser than 0.5,
// thus the actual Binomial test hypothesis is inverted:
OneSampleHypothesis binomialHypothesis;
switch (alternate)
{
case TwoSampleHypothesis.ValuesAreDifferent:
binomialHypothesis = OneSampleHypothesis.ValueIsDifferentFromHypothesis; break;
case TwoSampleHypothesis.FirstValueIsGreaterThanSecond:
binomialHypothesis = OneSampleHypothesis.ValueIsSmallerThanHypothesis; break;
case TwoSampleHypothesis.FirstValueIsSmallerThanSecond:
binomialHypothesis = OneSampleHypothesis.ValueIsGreaterThanHypothesis; break;
default: throw new InvalidOperationException();
}
base.Compute(positive, total, 0.5, binomialHypothesis);
}
/// <summary>
/// Creates a new sign test for two samples.
/// </summary>
///
/// <param name="positiveSamples">The number of positive samples (successes).</param>
/// <param name="totalSamples">The total number of samples (trials).</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoSampleSignTest(int positiveSamples, int totalSamples, TwoSampleHypothesis alternate)
{
Compute(positiveSamples, totalSamples, alternate);
}
/// <summary>
/// Estimates the number of samples necessary to attain the
/// required power level for the given effect size.
/// </summary>
///
/// <param name="delta">The minimum detectable difference.</param>
/// <param name="standardDeviation">The difference standard deviation.</param>
/// <param name="power">The desired power level. Default is 0.8.</param>
/// <param name="alpha">The desired significance level. Default is 0.05.</param>
/// <param name="hypothesis">The alternative hypothesis (research hypothesis) to be tested.</param>
/// <param name="proportion">The proportion of observations in the second group
/// when compared to the first group. A proportion of 2:1 results in twice more
/// samples in the second group than in the first. Default is 1.</param>
///
/// <returns>The required number of samples.</returns>
///
public static TwoSampleZTestPowerAnalysis GetSampleSize(double delta,
double standardDeviation = 1, double proportion = 1.0, double power = 0.8, double alpha = 0.05,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent)
{
var analysis = new TwoSampleZTestPowerAnalysis(hypothesis)
{
Effect = (delta) / standardDeviation,
Size = alpha,
Power = power,
};
analysis.ComputeSamples(proportion);
return analysis;
}
/// <summary>
/// Estimates the number of samples necessary to attain the
/// required power level for the given effect size.
/// </summary>
///
/// <param name="sampleSize1">The number of observations in the first sample.</param>
/// <param name="sampleSize2">The number of observations in the second sample.</param>
/// <param name="power">The desired power level. Default is 0.8.</param>
/// <param name="alpha">The desired significance level. Default is 0.05.</param>
/// <param name="hypothesis">The alternative hypothesis (research hypothesis) to be tested.</param>
///
/// <returns>The required number of samples.</returns>
///
public static TwoSampleZTestPowerAnalysis GetEffectSize(int sampleSize1, int sampleSize2,
double power = 0.8, double alpha = 0.05,
TwoSampleHypothesis hypothesis = TwoSampleHypothesis.ValuesAreDifferent)
{
var analysis = new TwoSampleZTestPowerAnalysis(hypothesis)
{
Samples1 = sampleSize1,
Samples2 = sampleSize2,
Size = alpha,
Power = power,
};
analysis.ComputeEffect();
return analysis;
}
/// <summary>
/// Creates a new <see cref="ZTestPowerAnalysis"/>.
/// </summary>
///
/// <param name="hypothesis">The hypothesis tested.</param>
///
public TwoSampleZTestPowerAnalysis(TwoSampleHypothesis hypothesis)
: base((DistributionTail)hypothesis)
{
}
/// <summary>
/// Creates a new Two-Table Kappa test.
/// </summary>
///
/// <param name="matrix1">The first contingency table to test.</param>
/// <param name="matrix2">The second contingency table to test.</param>
/// <param name="hypothesizedDifference">The hypothesized difference between the two Kappa values.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoMatrixKappaTest(GeneralConfusionMatrix matrix1, GeneralConfusionMatrix matrix2, double hypothesizedDifference = 0,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
this.EstimatedValue1 = matrix1.Kappa;
this.EstimatedValue2 = matrix2.Kappa;
this.Variance1 = matrix1.Variance;
this.Variance2 = matrix2.Variance;
this.OverallVariance = Variance1 + Variance2;
double diff = Math.Abs(EstimatedValue1 - EstimatedValue2);
double stdError = Math.Sqrt(OverallVariance);
Compute(diff, hypothesizedDifference, stdError, alternate);
}
/// <summary>
/// Creates a new Z-Test for two sample proportions.
/// </summary>
///
/// <param name="successes1">The number of successes in the first sample.</param>
/// <param name="trials1">The total number of trials (observations) in the first sample.</param>
/// <param name="successes2">The number of successes in the second sample.</param>
/// <param name="trials2">The total number of trials (observations) in the second sample.</param>
/// <param name="alternate">The alternative hypothesis (research hypothesis) to test.</param>
///
public TwoProportionZTest(int successes1, int trials1, int successes2, int trials2,
TwoSampleHypothesis alternate = TwoSampleHypothesis.ValuesAreDifferent)
{
Compute(successes1, trials1, successes2, trials2, alternate);
}
