public void test_AdjustedR2_performance_10000_observations_2_variables()
{
Stopwatch sw = new Stopwatch();
Random rand = new Random();
List <double> y = new List <double>();
List <double> x1 = new List <double>();
List <double> x2 = new List <double>();
for (int i = 0; i < 10000; i++)
{
y.Add(rand.Next(0, 10));
x1.Add(rand.Next(0, 10));
x2.Add(rand.Next(0, 10));
}
List <List <double> > expl = new List <List <double> >()
{
x1, x2
};
sw.Start();
ModelFit.AdjustedR2(y, expl);
sw.Stop();
Console.WriteLine("Took " + sw.ElapsedMilliseconds + " milliseconds");
Assert.LessOrEqual(sw.ElapsedMilliseconds, 1000);
}
public void test_AdjustedR2_return_correct_result()
{
/*Calculated with R, lm (list ~ list2 + list3) produces an adjusted R2 value of 0.2081.*/
List <List <double> > explanatory = new List <List <double> >()
{
list2, list3
};
Assert.AreEqual(0.2081, Math.Round(ModelFit.AdjustedR2(list, explanatory), 4));
}
public void test_AdjustedR2_throws_MathError_Different_Sized_Lists()
{
/*Different sized lists throw MathError*/
List <double> y = new List <double>()
{
2, 3
};
List <List <double> > lists = new List <List <double> >()
{
list, list2, list3
};
Assert.Throws <MathError>(() => ModelFit.AdjustedR2(y, lists));
}
public void test_AdjustedR2_throws_MathError()
{
/* Trying to get coefficients for invalid matrix should produce ArgumentException.*/
List <double> y = new List <double>();
List <double> x1 = new List <double>();
List <double> x2 = new List <double>();
List <double> x3 = new List <double>();
for (int i = 0; i < 100; i++)
{
y.Add(1);
x1.Add(1);
x2.Add(1);
x3.Add(1);
}
List <List <double> > expl = new List <List <double> >()
{
x1, x2, x3
};
Assert.Throws <MathError>(() => ModelFit.AdjustedR2(y, expl));
}
/// <summary>
/// Calculates adjusted coefficient of determination value for a fitted model.
/// </summary>
/// <param name="y">List containing the response values.</param>
/// <param name="x">List containing lists for all explanatory variables.</param>
/// <returns>Coefficient of determination adjusted with number of explanatory variables
/// and observations.</returns>
/// <exception cref="MathError">Thrown when parameter lists are of different length.</exception>
/// <exception cref="ArgumentException">Thrown when matrix created from parameters is not valid to fit least square points.</exception>
public double EvaluateFitness(Model m)
{
return(ModelFit.AdjustedR2(m.getYVar().values, m.getXVariableLists()));
}