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Statistics-Discrete-0.05.00
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/ Statistics::Discrete

NAME

Statistics::Discrete - Perl extension for statistical analyses of discrete data. The package provides descriptive statistics, distributions and the possibility to bin the distributions requested. It uses an internal "cache" in order to be as efficient as possible when multiple statistics are computed on the same set of data.

SYNOPSIS

 use Statistics::Discrete;

 my $sd = Statistics::Discrete->new();

 # Add data
 $sd->add_data((2,5,7,2,1,7,3,3,7,333));
 $sd->add_data_from_file("./data.txt"); 

 # Descriptive Statistics
 my $count = $sd->count(); 
 my $min = $sd->minimum(); 
 my $max = $sd->maximum(); 
 my $mean = $sd->mean(); 
 my $median = $sd->median(); 
 my $variance = $sd->variance(); 
 my $standard_deviation = $sd->standard_deviation(); 
 my $percentile = $sd->percentile(90); # 90th percentile

 # Distributions
 my $fd = $sd->frequency_distribution();
 my $pmf = $sd->probability_mass_function();
 my $cdf = $sd->empirical_distribution_function();
 my $ccdf = $sd->complementary_cumulative_distribution_function();
 
 # Binning
 $sd->set_binning_type(Statistics::Discrete:NO_BINNING);
 $sd->set_binning_type(Statistics::Discrete:LOG_BINNING);
 $sd->set_binning_type(Statistics::Discrete:LIN_BINNING);
 $sd->set_optimal_binning();
 $sd->set_custom_num_bins(10);
 my $cur_bins = $sd->bins();
 my $lin_bins = $sd->compute_lin_bins($num_of_bins);
 my $log_bins = $sd->compute_log_bins($num_of_bins);

DESCRIPTION

To use Statistics::Discrete, first you need to load the module:

use Statistics::Discrete;
use strict;

Then you need to create a new object of the class Statistics::Discrete:

my $sd = Statistics::Discrete->new();

Next you need to load the data to be analyzed in the class either by providing a numerical array or a file containing the data:

$sd->add_data((2,5,7,2,1,7,3,3,7,333));

or: $sd->add_data_from_file("./data.txt");

# TODO add info on file format

Descriptive Statistics

count()

count return the number of samples.

minimum()

minimum return the minimum data value.

maximum()

maximum return the maximum data value.

mean()

mean return the mean value.

median()

median return the median value.

variance()

variance return the variance.

standard_deviation()

standard_deviation return the standard deviation.

percentile( [$perc] )

percentile return the minimum data value that satisfies the $perc percentile.

Distributions

The distributions are computed without any binning of the data provided unless a binning type is explicitely provided (see section "Advanced Features: binning").

frequency_distribution()

frequency_distribution return a hash reference containing data points as keys and their frequency as the corresponding hash value.

probability_mass_function()

probability_mass_function return a hash reference containing data points as keys and their probability mass function as the corresponding hash value (i.e. probability to extract exactly such a value, among the data set provided).

empirical_distribution_function()

empirical_distribution_function return a hash reference containing data points as keys and the empirical distribution function associated with that data point as the corresponding hash value (i.e. probability to extract a value lower or equal to the one in the key, among the data set provided). Alias Empirical CDF.

complementary_cumulative_distribution_function()

complementary_cumulative_distribution_function return a hash reference containing data points as keys and the complementary cumulative distribution function associated with that data point as the corresponding hash value (i.e. probability to extract a value greater than the one in the key, among the data set provided). Alias Empirical CCDF.

Advanced Features: binning

Binning influences the way the distributions are returned. When binning is applied, the data space is grouped into a number of intervals [b_start_i, b_end_i). Each bin contains information about data greater or equal than b_start_i and lower than b_end_i. Also, the bin is referred using its middle point, i.e.:

$ref = $bin_start + ($bin_end - $bin_start)/2;

The hash references returning when calling the distibution functions will have these reference values as keys.

When binning is applied to the frequency distribution, each bin is associated with the sum of the frequencies of each value within the bin.

When binning is applied to the other distributions, the returning hash represents a density function other than a probability distribution. In fact, the probabilities associated with each bin are divided by the width of bin.

set_binning_type( [$binning_type] )

set_binning_type sets the binning type associated with the current class and establishes the binning type for the distributions computed from this line on. $binning_type can take one of the following values:

Statistics::Discrete::NO_BINNING is the default case and it tells the class to return non binned distributions.

Statistics::Discrete::LIN_BINNING tells the class to return linearly binned distributions: i.e. the support is divided into equally spaced intervals. The default number of bins is 10, but it can be changed using the function set_custom_num_bins( [$num_bins] ).

Statistics::Discrete::LOG_BINNING tells the class to return logarithmically binned distributions: i.e. the support is divided into intervals whose width grows logarithmically (e.g. 0.1, 1, 10, 100). There is no default number of bins, in the default case the optimal number of bins is computed by the class itself: i.e. the number of bins chosen is the one that generates the sum of the resulting probability mass density closest to 1.

set_custom_num_bins( [$num_bins] )

When the linear or the logarithmic binning is set, the user can provide a custom number of bins using the above function.

set_optimal_binning()

If the logarithmic binning is set, the user can decide to switch to the optimal binning, i.e. the number of bins is automatically computed by the class.

Additional features

bins()

bins() returns a reference to a hash having the following format: keys are the bin reference point (or middle point), the $bins->{$ref}{"left"} and $bins->{$ref}{"right"} contains the interval extreme values.

compute_lin_bins( [$num_bins] )

compute_lin_bins() returns the linear bins computed on the current data set (the number of bins is provided). This does not mean the program is using this binning. The binning has to be set using the functions described in the previous section.

compute_log_bins( [$num_bins] )

compute_log_bins() returns the logarithimic bins computed on the current data set (the number of bins is provided). This does not mean the program is using this binning. The binning has to be set using the functions described in the previous section.

EXPORT

None by default.

SEE ALSO

Statistics::Descriptive

Statistics::Descriptive::Discrete

AUTHOR

Chiara Orsini, <chiara@caida.org>

COPYRIGHT AND LICENSE

Chiara Orsini, CAIDA, UC San Diego chiara@caida.org

Copyright (C) 2014 The Regents of the University of California.

Statistics::Discrete 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 3 of the License, or (at your option) any later version.

Statistics::Discrete 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 Statistics::Discrete. If not, see <http://www.gnu.org/licenses/>.