Statistics::DescriptivUser Contributed Perl DocumentStatistics::Descriptive(3)NAMEStatistics::Descriptive - Module of basic descriptive statistical
functions.
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.
Many of the methods (both Sparse and Full) cache values so that
subsequent calls with the same arguments are faster.
METHODS
Sparse Methods
$stat = Statistics::Descriptive::Sparse->new();
Create a new sparse statistics object.
$stat->clear();
Effectively the same as
my $class = ref($stat);
undef $stat;
$stat = new $class;
except more efficient.
$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
Similar to the Sparse Methods above, any Full Method that is called
caches the current result so that it doesn't have to be recalculated.
In some cases, several values can be cached at the same time.
$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! Cached values for the sparse
methods are not changed
$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.
$stat->skewness();
Returns the skewness of the data. A value of zero is no skew,
negative is a left skewed tail, positive is a right skewed tail.
This is consistent with Excel.
$stat->kurtosis();
Returns the kurtosis of the data. Positive is peaked, negative is
flattened.
$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, which for
ease of handling and backward compatibility is returned as
undef() by the function.
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.ietf.org/rfc/rfc2330.txt> .)
If the percentile method is called in a list context then it will
also return the index of the percentile.
$x = $stat->quantile($Type);
Sorts the data and returns estimates of underlying distribution
quantiles based on one or two order statistics from the supplied
elements.
This method use the same algorithm as Excel and R language
(quantile type 7).
The generic function quantile produces sample quantiles
corresponding to the given probabilities.
$Type is an integer value between 0 to 4 :
0 => zero quartile (Q0) : minimal value
1 => first quartile (Q1) : lower quartile = lowest cut off (25%) of data = 25th percentile
2 => second quartile (Q2) : median = it cuts data set in half = 50th percentile
3 => third quartile (Q3) : upper quartile = highest cut off (25%) of data, or lowest 75% = 75th percentile
4 => fourth quartile (Q4) : maximal value
Exemple :
my @data = (1..10);
my $stat = Statistics::Descriptive::Full->new();
$stat->add_data(@data);
print $stat->quantile(0); # => 1
print $stat->quantile(1); # => 3.25
print $stat->quantile(2); # => 5.5
print $stat->quantile(3); # => 7.75
print $stat->quantile(4); # => 10
$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.
my $mode = $stat->mode();
Returns the mode of the data. The mode is the most commonly
occuring datum. See
<http://en.wikipedia.org/wiki/Mode_%28statistics%29> . If all
values occur only once, then mode() will return undef.
$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.
All calls to trimmed_mean() are cached so that they don't have to
be calculated a second time.
$stat->frequency_distribution_ref($partitions);
$stat->frequency_distribution_ref(\@bins);
$stat->frequency_distribution_ref();
"frequency_distribution_ref($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 a
reference to a hash 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 = $stat->frequency_distribution_ref(2);
for (sort {$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.
"frequency_distribution_refs(\@bins)" provides the bins that are
to be used for the distribution. This allows for non-uniform
distributions as well as trimmed or sample distributions to be
found. @bins must be monotonic and contain at least one element.
Note that unless the set of bins contains the range that the total
counts returned will be less than the sample size.
Calling "frequency_distribution_ref()" with no arguments returns
the last distribution calculated, if such exists.
my %hash = $stat->frequency_distribution($partitions);
my %hash = $stat->frequency_distribution(\@bins);
my %hash = $stat->frequency_distribution();
Same as "frequency_distribution_ref()" except that returns the
hash clobbered into the return list. Kept for compatibility
reasons with previous versions of Statistics::Descriptive and
using it is discouraged.
$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();
Because calling "least_squares_fit()" with no arguments defaults
to using the current range, there is no caching of the results.
REPORTING ERRORS
I read my email frequently, but since adopting this module I've added 2
children and 1 dog to my family, so please be patient about my response
times. When reporting errors, please include the following to help me
out:
· Your version of perl. This can be obtained by typing perl "-v" at
the command line.
· Which version of Statistics::Descriptive you're using. As you can
see below, I do make mistakes. Unfortunately for me, right now
there are thousands of CD's with the version of this module with
the bugs in it. Fortunately for you, I'm a very patient module
maintainer.
· Details about what the error is. Try to narrow down the scope of
the problem and send me code that I can run to verify and track it
down.
AUTHOR
Current maintainer:
Shlomi Fish, <http://www.shlomifish.org/> , "shlomif@cpan.org"
Previously:
Colin Kuskie
My email address can be found at http://www.perl.com under Who's Who or
at: http://search.cpan.org/author/COLINK/.
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.
LICENSE
This program is free software; you can redistribute it and/or modify it
under the same terms as Perl itself.
perl v5.14.2 2012-01-06 Statistics::Descriptive(3)
