Statistics::Descriptive - Module of basic descriptive statistical functions.

NAME

Statistics::Descriptive - Module of basic descriptive statistical functions.

SUPPORTED PLATFORMS

This module is not included with the standard ActivePerl distribution. It is available as a separate download using PPM.

SYNOPSIS

  use Statistics::Descriptive;
  $stat = Statistics::Descriptive::Full->new();
  $stat->add_data(1,2,3,4); $mean = $stat->mean();
  $var  = $stat->variance();
  $tm   = $stat->trimmed_mean(.25);
  $Statistics::Descriptive::Tolerance = 1e-10;

DESCRIPTION

This module provides basic functions used in descriptive statistics. It has an object oriented design and supports two different types of data storage and calculation objects: sparse and full. With the sparse method, none of the data is stored and only a few statistical measures are available. Using the full method, the entire data set is retained and additional functions are available.

Whenever a division by zero may occur, the denominator is checked to be greater than the value $Statistics::Descriptive::Tolerance, which defaults to 0.0. You may want to change this value to some small positive value such as 1e-24 in order to obtain error messages in case of very small denominators.

METHODS

Sparse Methods

$stat = Statistics::Descriptive::Sparse->new();
Create a new sparse statistics object.

$stat->add_data(1,2,3);
Adds data to the statistics variable. The cached statistical values are updated automatically.

$stat->count();
Returns the number of data items.

$stat->mean();
Returns the mean of the data.

$stat->sum();
Returns the sum of the data.

$stat->variance();
Returns the variance of the data. Division by n-1 is used.

$stat->standard_deviation();
Returns the standard deviation of the data. Division by n-1 is used.

$stat->min();
Returns the minimum value of the data set.

$stat->mindex();
Returns the index of the minimum value of the data set.

$stat->max();
Returns the maximum value of the data set.

$stat->maxdex();
Returns the index of the maximum value of the data set.

$stat->sample_range();
Returns the sample range (max - min) of the data set.

Full Methods

$stat = Statistics::Descriptive::Full->new();
Create a new statistics object that inherits from Statistics::Descriptive::Sparse so that it contains all the methods described above.

$stat->add_data(1,2,4,5);
Adds data to the statistics variable. All of the sparse statistical values are updated and cached. Cached values from Full methods are deleted since they are no longer valid.

Note: Calling add_data with an empty array will delete all of your Full method cached values!

$stat->get_data();
Returns a copy of the data array.

$stat->sort_data();
Sort the stored data and update the mindex and maxdex methods. This method uses perl's internal sort.

$stat->presorted(1);
$stat->presorted();
If called with a non-zero argument, this method sets a flag that says the data is already sorted and need not be sorted again. Since some of the methods in this class require sorted data, this saves some time. If you supply sorted data to the object, call this method to prevent the data from being sorted again. The flag is cleared whenever add_data is called. Calling the method without an argument returns the value of the flag.

$x = $stat->percentile(25);
($x, $index) = $stat->percentile(25);
Sorts the data and returns the value that corresponds to the percentile as defined in RFC2330: 
   For example, given the 6 measurements:
   -2, 7, 7, 4, 18, -5
   Then F(-8) = 0, F(-5) = 1/6, F(-5.0001) = 0, F(-4.999) = 1/6, F(7) =
   5/6, F(18) = 1, F(239) = 1.
   Note that we can recover the different measured values and how many
   times each occurred from F(x) -- no information regarding the range
   in values is lost.  Summarizing measurements using histograms, on the
   other hand, in general loses information about the different values
   observed, so the EDF is preferred.
   Using either the EDF or a histogram, however, we do lose information
   regarding the order in which the values were observed.  Whether this
   loss is potentially significant will depend on the metric being
   measured.
   We will use the term "percentile" to refer to the smallest value of x
   for which F(x) >= a given percentage.  So the 50th percentile of the
   example above is 4, since F(4) = 3/6 = 50%; the 25th percentile is
   -2, since F(-5) = 1/6 < 25%, and F(-2) = 2/6 >= 25%; the 100th
   percentile is 18; and the 0th percentile is -infinity, as is the 15th
   percentile.
   Care must be taken when using percentiles to summarize a sample,
   because they can lend an unwarranted appearance of more precision
   than is really available.  Any such summary must include the sample
   size N, because any percentile difference finer than 1/N is below the
   resolution of the sample.

taken from: RFC2330 - Framework for IP Performance Metrics, Section 11.3. Defining Statistical Distributions

rfc2330 is available from: http://www.cis.ohio-state.edu/htbin/rfc/rfc2330.html

If the percentile method is called in a list context then it will also return the index of the percentile.

$stat->median();
Sorts the data and returns the median value of the data.

$stat->harmonic_mean();
Returns the harmonic mean of the data. Since the mean is undefined if any of the data are zero or if the sum of the reciprocals is zero, it will return undef for both of those cases.

$stat->geometric_mean();
Returns the geometric mean of the data.

$stat->mode();
Returns the mode of the data.

$stat->trimmed_mean(ltrim[,utrim]);
trimmed_mean(ltrim) returns the mean with a fraction ltrim of entries at each end dropped. trimmed_mean(ltrim,utrim) returns the mean after a fraction ltrim has been removed from the lower end of the data and a fraction utrim has been removed from the upper end of the data. This method sorts the data before beginning to analyze it.

$stat->frequency_distribution();
frequency_distribution(partitions) slices the data into partition sets (where partition is greater than 1) and counts the number of items that fall into each partition. It returns an associative array where the keys are the numerical values of the partitions used. The minimum value of the data set is not a key and the maximum value of the data set is always a key. The number of entries for a particular partition key are the number of items which are greater than the previous partition key and less then or equal to the current partition key. As an example, 
   $stat->add_data(1,1.5,2,2.5,3,3.5,4);
   %f = $stat->frequency_distribution(2);
   for (sort {$a <=> $b} keys %f) {
      print "key = $_, count = $f{$_}\n";
   }

prints

   key = 2.5, count = 4
   key = 4, count = 3

since there are four items less than or equal to 2.5, and 3 items greater than 2.5 and less than 4.

$stat->least_squares_fit();
$stat->least_squares_fit(@x);
least_squares_fit() performs a least squares fit on the data, assuming a domain of @x or a default of 1..$stat->count(); It returns an array of four elements ($q, $m, $r, $rms) where
$q and $m
satisfy the equation C($y = $m*$x + $q).

$r
is the Pearson linear correlation cofficient.

$rms
is the root-mean-square error.

If case of error or division by zero, the empty list is returned.

The array that is returned can be ``coerced'' into a hash structure by doing the following:

  my %hash = ();
  @hash{'q', 'm', 'r', 'err'} = $stat->least_squares_fit();

REPORTING ERRORS

I read 4 of the 5 perl newsgroups comp.lang.perl.{misc,moderated,modules,announce} and check my email at work frequently, so please feel free to post errors to either or both of those places. However, realize that if you post to the newsgroup it has the benefit of alerting other users of the problem. When reporting errors, please include the following to help me out:

My email address can be found at www.perl.com under Who's Who.

REFERENCES

RFC2330, Framework for IP Performance Metrics

The Art of Computer Programming, Volume 2, Donald Knuth.

Handbook of Mathematica Functions, Milton Abramowitz and Irene Stegun.

Probability and Statistics for Engineering and the Sciences, Jay Devore.

COPYRIGHT

Copyright (c) 1997,1998 Colin Kuskie. All rights reserved. This program is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

Copyright (c) 1998 Andrea Spinelli. All rights reserved. This program is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

Copyright (c) 1994,1995 Jason Kastner. All rights reserved. This program is free software; you can redistribute it and/or modify it under the same terms as Perl itself.

REVISION HISTORY

v2.3
Rolled into November 1998

Code provided by Andrea Spinelli to prevent division by zero and to make consistent return values for undefined behavior. Andrea also provided a test bench for the module.

A bug fix for the calculation of frequency distributions. Thanks to Nick Tolli for alerting this to me.

Added 4 lines of code to Makefile.PL to make it easier for the ActiveState installation tool to use. Changes work fine in perl5.004_04, haven't tested them under perl5.005xx yet.

v2.2
Rolled into March 1998.

Fixed problem with sending 0's and -1's as data. The old 0 : true ? false thing. Use defined to fix.

Provided a fix for AUTOLOAD/DESTROY/Carp bug. Very strange.

v2.1
August 1997

Fixed errors in statistics algorithms caused by changing the interface.

v2.0
August 1997

Fixed errors in removing cached values (they weren't being removed!) and added sort_data and presorted methods.

June 1997

Transferred ownership of the module from Jason to Colin.

Rewrote OO interface, modified function distribution, added mindex, maxdex.

v1.1
April 1995

Added LeastSquaresFit and FrequencyDistribution.

v1.0
March 1995

Released to comp.lang.perl and placed on archive sites.

v.20
December 1994

Complete rewrite after extensive and invaluable e-mail correspondence with Anno Siegel.

v.10
December 1994

Initital concept, released to perl5-porters list.

 Statistics::Descriptive - Module of basic descriptive statistical functions.