File Coverage

blib/lib/Text/NSP/Measures/2D/Fisher/right.pm

Criterion	Covered	Total	%
statement	44	51	86.2
branch	14	24	58.3
condition			n/a
subroutine	6	7	85.7
pod			n/a
total	64	82	78.0

=head1 NAME

Text::NSP::Measures::2D::Fisher::right - Perl module implementation of the right sided
                                         Fisher's exact test.

=head1 SYNOPSIS

=head3 Basic Usage

  use Text::NSP::Measures::2D::Fisher::right;

  my $npp = 60; my $n1p = 20; my $np1 = 20;  my $n11 = 10;

  $right_value = calculateStatistic( n11=>$n11,
                                      n1p=>$n1p,
                                      np1=>$np1,
                                      npp=>$npp);

  if( ($errorCode = getErrorCode()))
  {
    print STDERR $errorCode." - ".getErrorMessage();
  }
  else
  {
    print getStatisticName."value for bigram is ".$right_value;
  }


=head1 DESCRIPTION

Assume that the frequency count data associated with a bigram
 is stored in a 2x2 contingency table:

          word2   ~word2
  word1    n11      n12 | n1p
 ~word1    n21      n22 | n2p
           --------------
           np1      np2   npp

where n11 is the number of times  occur together, and
n12 is the number of times  occurs with some word other than
word2, and n1p is the number of times in total that word1 occurs as
the first word in a bigram.

The fishers exact tests are calculated by fixing the marginal totals
and computing the hypergeometric probabilities for all the possible
contingency tables,

A right sided test is calculated by adding the probabilities of all
the possible two by two contingency tables formed by fixing the
marginal totals and changing the value of n11 to greater than or
equal to the given value. A right sided Fisher's Exact Test tells us
how likely it is to randomly sample a table where n11 is greater
than observed. In other words, it tells us how likely it is to sample
an observation where the two words are more dependent than currently
observed.

=head2 Methods

=over

=cut

package Text::NSP::Measures::2D::Fisher::right;


use Text::NSP::Measures::2D::Fisher;
use strict;
use Carp;
use warnings;
no warnings 'redefine';
require Exporter;

our ($VERSION, @EXPORT, @ISA);

@ISA  = qw(Exporter);

@EXPORT = qw(initializeStatistic calculateStatistic
             getErrorCode getErrorMessage getStatisticName);

$VERSION = '0.97';


=item calculateStatistic() - This method calculates the right Fisher value

INPUT PARAMS  : $count_values       .. Reference of an hash containing
                                       the count values computed by the
                                       count.pl program.

RETURN VALUES : $right              .. Right Fisher value.

=cut

sub calculateStatistic
{
  my %values = @_;

  my $probabilities;
  my $left_flag = 0;

  # computes and returns the observed and marginal values from
  # the frequency combination values. returns 0 if there is an
  # error in the computation or the values are inconsistent.
  if( !(Text::NSP::Measures::2D::Fisher::getValues(\%values)) )
  {
    return;
  }

  my $final_limit = ($n1p < $np1) ? $n1p : $np1;
  my $n11_org = $n11;

  my $n11_start = $n1p + $np1 - $npp;
  if($n11_start < $n11)
  {
    $n11_start = $n11;
  }


  # to make the computations faster, we check which would require less computations
  # computing the leftfisher value and subtracting it from 1 or directly computing
  # the right fisher value. We do this since, generally for bigrams n11 is quite small
  # so its much faster to compute the left Fisher value.
  my $left_final_limit = $n11-1;
  my $left_n11 = $n1p + $np1 - $npp;
  if($left_n11<0)
  {
    $left_n11 = 0;
  }

  # if computing the left fisher values first will take lesser amount of time them
  # we set a flag for later reference and then compute the leftfisher score for
  # n11-1 and then subtract the total score from one to get the right fisher value.
  if(($left_final_limit - $left_n11) < ($final_limit - $n11_start))
  {
    $left_flag = 1;
    if( !($probabilities = Text::NSP::Measures::2D::Fisher::computeDistribution($left_n11, $left_final_limit)))
    {
        return;
    }
  }

  #else we compute the value normally and simply sum to get the rightfisher value.
  else
  {
    if( !($probabilities = Text::NSP::Measures::2D::Fisher::computeDistribution($n11_start, $final_limit)))
    {
        return;
    }
  }

  my $key_n11;

  my $rightfisher=0;

  foreach $key_n11 (sort { $b <=> $a } keys %$probabilities)
  {
    if($left_flag)
    {
      if($key_n11 >= $n11_org)
      {
        last;
      }
    }
    else
    {
      if($key_n11 < $n11_org)
      {
        last;
      }
    }
    $rightfisher += exp($probabilities->{$key_n11});
  }

  # if we computed the leftfisher value to get the right fisher value, we subtract
  # the sum of the probabilities for the tables from one to get the right fisher score.
  if($left_flag)
  {
    if ($rightfisher > 1)
    {
      $rightfisher = 0;
    }
    else
    {
      $rightfisher = 1 - $rightfisher;
    }
  }

  return $rightfisher;
}


=item getStatisticName() - Returns the name of this statistic

INPUT PARAMS  : none

RETURN VALUES : $name      .. Name of the measure.

=cut

sub getStatisticName
{
    return "Right Fisher";
}



1;
__END__