diff --git a/src/Numerics/GoodnessOfFit.cs b/src/Numerics/GoodnessOfFit.cs
index 0760fc42..eb135905 100644
--- a/src/Numerics/GoodnessOfFit.cs
+++ b/src/Numerics/GoodnessOfFit.cs
@@ -25,7 +25,9 @@
// OTHER DEALINGS IN THE SOFTWARE.
//
+using System;
using System.Collections.Generic;
+using MathNet.Numerics.Properties;
using MathNet.Numerics.Statistics;
namespace MathNet.Numerics
@@ -33,7 +35,7 @@ namespace MathNet.Numerics
public static class GoodnessOfFit
{
///
- /// Calculated the R-Squared value, also known as coefficient of determination,
+ /// Calculates the R-Squared value, also known as coefficient of determination,
/// given modelled and observed values
///
/// The values expected from the modelled
@@ -46,7 +48,7 @@ namespace MathNet.Numerics
}
///
- /// Calculated the R value, also known as linear correlation coefficient,
+ /// Calculates the R value, also known as linear correlation coefficient,
/// given modelled and observed values
///
/// The values expected from the modelled
@@ -56,5 +58,54 @@ namespace MathNet.Numerics
{
return Correlation.Pearson(modelledValues, observedValues);
}
+
+ ///
+ /// Calculates the Standard Error of the regression, given a sequence of
+ /// modeled/predicted values, and a sequence of actual/observed values
+ ///
+ /// The modelled/predicted values
+ /// The observed/actual values
+ /// The Standard Error of the regression
+ public static double PopulationStandardError(IEnumerable modelledValues, IEnumerable observedValues)
+ {
+ return SampleStandardError(modelledValues, observedValues, 0);
+ }
+
+ ///
+ /// Calculates the Standard Error of the regression, given a sequence of
+ /// modeled/predicted values, and a sequence of actual/observed values
+ ///
+ /// The modelled/predicted values
+ /// The observed/actual values
+ /// The degrees of freedom by which the
+ /// number of samples is reduced for performing the Standard Error calculation
+ /// The Standard Error of the regression
+ public static double SampleStandardError(IEnumerable modelledValues, IEnumerable observedValues, int degreesOfFreedom)
+ {
+ using (IEnumerator ieM = modelledValues.GetEnumerator())
+ using (IEnumerator ieO = observedValues.GetEnumerator())
+ {
+ double n = 0;
+ double accumulator = 0;
+ while (ieM.MoveNext())
+ {
+ if (!ieO.MoveNext())
+ {
+ throw new ArgumentOutOfRangeException("modelledValues", Resources.ArgumentArraysSameLength);
+ }
+ double currentM = ieM.Current;
+ double currentO = ieO.Current;
+ var diff = currentM - currentO;
+ accumulator += diff * diff;
+ n++;
+ }
+
+ if (degreesOfFreedom >= n)
+ {
+ throw new ArgumentOutOfRangeException("degreesOfFreedom", Resources.DegreesOfFreedomMustBeLessThanSampleSize);
+ }
+ return Math.Sqrt(accumulator / (n - degreesOfFreedom));
+ }
+ }
}
-}
+}
\ No newline at end of file
diff --git a/src/Numerics/Properties/Resources.Designer.cs b/src/Numerics/Properties/Resources.Designer.cs
index d35d15d6..0aa64c61 100644
--- a/src/Numerics/Properties/Resources.Designer.cs
+++ b/src/Numerics/Properties/Resources.Designer.cs
@@ -602,6 +602,15 @@ namespace MathNet.Numerics.Properties {
}
}
+ ///
+ /// Looks up a localized string similar to The sample size must be larger than the given degrees of freedom..
+ ///
+ public static string DegreesOfFreedomMustBeLessThanSampleSize {
+ get {
+ return ResourceManager.GetString("DegreesOfFreedomMustBeLessThanSampleSize", resourceCulture);
+ }
+ }
+
///
/// Looks up a localized string similar to This feature is not implemented yet (but is planned)..
///
diff --git a/src/Numerics/Properties/Resources.resx b/src/Numerics/Properties/Resources.resx
index fe831bfe..14692289 100644
--- a/src/Numerics/Properties/Resources.resx
+++ b/src/Numerics/Properties/Resources.resx
@@ -1,17 +1,17 @@
-
@@ -445,4 +445,7 @@
All sample vectors must have the same length. However, vectors with disagreeing length {0} and {1} have been provided. A sample with index i is given by the value at index i of each provided vector.
+
+ The sample size must be larger than the given degrees of freedom.
+
\ No newline at end of file
diff --git a/src/UnitTests/GoodnessOfFit/StandardErrorTest.cs b/src/UnitTests/GoodnessOfFit/StandardErrorTest.cs
new file mode 100644
index 00000000..60245133
--- /dev/null
+++ b/src/UnitTests/GoodnessOfFit/StandardErrorTest.cs
@@ -0,0 +1,86 @@
+//
+// Math.NET Numerics, part of the Math.NET Project
+// http://numerics.mathdotnet.com
+// http://github.com/mathnet/mathnet-numerics
+//
+// Copyright (c) 2009-2013 Math.NET
+//
+// Permission is hereby granted, free of charge, to any person
+// obtaining a copy of this software and associated documentation
+// files (the "Software"), to deal in the Software without
+// restriction, including without limitation the rights to use,
+// copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the
+// Software is furnished to do so, subject to the following
+// conditions:
+//
+// The above copyright notice and this permission notice shall be
+// included in all copies or substantial portions of the Software.
+//
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+// EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+// OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+// NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+// HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY,
+// WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+// OTHER DEALINGS IN THE SOFTWARE.
+//
+
+using System;
+using System.Linq;
+using NUnit.Framework;
+
+namespace MathNet.Numerics.UnitTests.GoodnessOfFit
+{
+ [TestFixture, Category("Regression")]
+ public class StandardErrorTest
+ {
+ [Test]
+ public void ComputesPopulationStandardErrorOfTheRegression()
+ {
+ // Definition as described at: http://onlinestatbook.com/lms/regression/accuracy.html
+ var xes = new[] { 1.0, 2, 3, 4, 5 };
+ var ys = new[] { 1, 2, 1.3, 3.75, 2.25 };
+ var fit = Fit.Line(xes, ys);
+ var a = fit.Item1;
+ var b = fit.Item2;
+ var predictedYs = xes.Select(x => a + b * x);
+ var standardError = Numerics.GoodnessOfFit.PopulationStandardError(predictedYs, ys);
+
+ Assert.AreEqual(0.747, standardError, 1e-3);
+ }
+
+ [Test]
+ public void ComputesSampleStandardErrorOfTheRegression()
+ {
+ // Definition as described at: http://onlinestatbook.com/lms/regression/accuracy.html
+ var xes = new[] { 1.0, 2, 3, 4, 5 };
+ var ys = new[] { 1, 2, 1.3, 3.75, 2.25 };
+ var fit = Fit.Line(xes, ys);
+ var a = fit.Item1;
+ var b = fit.Item2;
+ var predictedYs = xes.Select(x => a + b * x);
+ var standardError = Numerics.GoodnessOfFit.SampleStandardError(predictedYs, ys, degreesOfFreedom: 2);
+
+ Assert.AreEqual(0.964, standardError, 1e-3);
+ }
+
+ [Test]
+ public void PopulationStandardErrorShouldThrowIfInputsSequencesDifferInLength()
+ {
+ var y1 = new[] { 0.0, 1 };
+ var y2 = new[] { 1.0 };
+
+ Assert.Throws(() => Numerics.GoodnessOfFit.PopulationStandardError(y1, y2));
+ }
+
+ [Test]
+ public void SampleStandardErrorShouldThrowIfSampleSizeIsSmallerThanGivenDegreesOfFreedom()
+ {
+ var modelled = new[] { 1.0 };
+ var observed = new[] { 1.0 };
+ Assert.Throws(() => Numerics.GoodnessOfFit.SampleStandardError(modelled, observed, 2));
+ }
+ }
+}
diff --git a/src/UnitTests/UnitTests.csproj b/src/UnitTests/UnitTests.csproj
index 9f2fdcd0..77a3231b 100644
--- a/src/UnitTests/UnitTests.csproj
+++ b/src/UnitTests/UnitTests.csproj
@@ -142,6 +142,7 @@
+