=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__ =back =head1 AUTHOR Ted Pedersen, University of Minnesota Duluth Etpederse@d.umn.eduE Satanjeev Banerjee, Carnegie Mellon University Esatanjeev@cmu.eduE Amruta Purandare, University of Pittsburgh Eamruta@cs.pitt.eduE Bridget Thomson-McInnes, University of Minnesota Twin Cities Ebthompson@d.umn.eduE Saiyam Kohli, University of Minnesota Duluth Ekohli003@d.umn.eduE =head1 HISTORY Last updated: $Id: right.pm,v 1.12 2006/06/21 11:10:52 saiyam_kohli Exp $ =head1 BUGS =head1 SEE ALSO @inproceedings{Pedersen96, author = {Pedersen, T.}, title = {Fishing For Exactness}, booktitle = {Proceedings of the South Central SAS User's Group (SCSUG-96) Conference}, year = {1996}, pages = {188--200}, month ={October}, address = {Austin, TX} url = L} L L =head1 COPYRIGHT Copyright (C) 2000-2006, Ted Pedersen, Satanjeev Banerjee, Amruta Purandare, Bridget Thomson-McInnes and Saiyam Kohli This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to The Free Software Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. Note: a copy of the GNU General Public License is available on the web at L and is included in this distribution as GPL.txt. =cut