BoxCoxExample.cs (.NET, C#, CSharp)

using System;

using CenterSpace.NMath.Core;
using CenterSpace.NMath.Stats;

namespace BoxCoxExample
{
  class BoxCoxExample
  {
    static void Main( string[] args )
    {
      // The data to be transformed.
      DoubleVector data = new DoubleVector( "[.15	 .09	 .18	 .10	 .05	 .12	 .08 .05	 .08	" +
        " .10	 .07	 .02	 .01	 .10 .10	 .10	 .02	 .10	 .01	 .40	 .10 .05	 .03	 .05  " +
        " .15	 .10	 .15	 .09 .08	 .18	 .10	 .20	 .11	 .30	 .02 .20	 .20	 .30	 .30	 .40	 .30	 .05]" );

      // The interval to search for the value of lambda which maximizes the log-likelihood
      // function.
      Interval lambdaInterval = new Interval( -3, 3, Interval.Type.Closed );
      // We want lambda values less than 0.01 to be considered equal to zero.
      double lambdaEpsilon = .01;

      // Construct a BoxCox for the given data and lambd information.
      BoxCox bc = new BoxCox( data, lambdaInterval, lambdaEpsilon );

      // Write the optimal lambda and the transformed data using that lambda value.
      Console.WriteLine( "Optimal lambda value in {0} is {1}", bc.LambdaInterval, bc.Lambda );
      Console.WriteLine( "\nTransformed data - " );
      Console.WriteLine( "  {0}", bc.TransformedData );

      // Print out the log-likelihood function for the data at the optimal lambda value.
      Console.WriteLine( "\nValue of the log-likelihood function at lambda = {0} is {1}", 
        bc.Lambda, BoxCox.LogLikelihood( data, bc.Lambda ) );

     // Transform some other data using a lambda value of 0.27.
      double lambda = 0.27;
      DoubleVector y = new DoubleVector( 10, new RandGenBeta( 2.2 ) );
      DoubleVector transformedY = BoxCox.Transform( y, lambda );
      Console.WriteLine( "\ny transformed with lambda = {0} - ", lambda );
      Console.WriteLine( "  {0}", transformedY );
    }
  }
}