BinomFit Routines

Summary

Calculate parameters for binomial distributed values.

Unit
Statistics

Declaration

Procedure BinomFit(X: TVec; out N: Integer; out p: TSample);

Declaration

Procedure BinomFit(X: TVec; n: TSample; p: TVec);

Declaration

Procedure BinomFit(X: TVec; n: TSample; p, Low, High: TVec; alpha: TSample = 0.05);

Parameter	Description
X	Stores data which is assumed to be binomial distributed.
n	Defines binomial distribution n parameter.
p	Returns binomial distribution p parameter, estimated at each X value.
Low,High	p (1-Alpha)*100 percent confidence intervals. Each pair of Low and High values defines lower and upper CI for corresponding p value.
Alpha	Confidence interval percentage.

See Also
RandomBinom
MVBinom

Example 1

The following example generates 100 random Weibull distributed values and then uses WeibullFit routine to extract used a and b parameters:

 The following example generates 100 binomial distributed values and then
    uses BinomFit routine to extract p parameter:<p>
  var vec1: Vector;
    resP: TSample;
    LowInt,HighInt: Vector;
  begin
    vec1.Size(1000);
    RandomBinom(35,0.25,vec1);
    // Now extract p-s and its 100*(1-Alpha)
    // confidence intervals.
    BinomFit(vec1,35,p,LowInt,HighInt);
    // vector p holds the p for each element in vec1,
    // vectors LowInt and HighInt hold the lower and upper conf. int. limit
    // for each element in p
  end;

  #include "StatRandom.hpp"
  #include "MtxVecCpp.h"
  #include "Statistics.hpp"
  void __fastcall Example();
  {
    Vector vec1,LowInt,HighInt;
    vec1->Size(1000,false);
    RandomBinom(35,0.25,vec1);
    // Now extract p-s and its 100*(1-Alpha)
    // confidence intervals.
    double p;
    BinomFit(vec1,35,p,LowInt,HighInt,0.05);
    // vector p holds the p for each element in vec1,
    // vectors LowInt and HighInt hold the lower and upper conf. int. limit
    // for each element in p
  }

  using Dew.Math;
  using Dew.Stats;
  using Dew.Stats.Units;
  namespace Dew.Examples;
  {
    private void Example()
    {
      Vector vec1 = new Vector(1000,false);
      Vector LowInt = new Vector(0);
      Vector HighInt = new Vector(0);
      StatRandom.RandomBinom(35,0.25,vec1);
      // Now extract p-s and its 100*(1-Alpha)
      // confidence intervals.
      double p;
      Statistics.BinomFit(vec1,35,out p,LowInt,HighInt,0.05);
      // vector p holds the p for each element in vec1,
      // vectors LowInt and HighInt hold the lower and upper conf. int. limit
      // for each element in p
    }
  }