package Statistics::Descriptive;
use strict;
use warnings;
##This module draws heavily from perltoot v0.4 from Tom Christiansen.
use 5.006;
use vars (qw($VERSION $Tolerance $Min_samples_number));
$VERSION = '3.0612';
$Tolerance = 0.0;
$Min_samples_number = 4;
package Statistics::Descriptive::Sparse;
use vars qw($VERSION);
$VERSION = '3.0612';
use vars qw(%fields);
use Carp;
use Statistics::Descriptive::Smoother;
sub _make_accessors
{
my ($pkg, $methods) = @_;
no strict 'refs';
foreach my $method (@$methods)
{
*{$pkg."::".$method} =
do {
my $m = $method;
sub {
my $self = shift;
if (@_)
{
$self->{$m} = shift;
}
return $self->{$m};
};
};
}
return;
}
sub _make_private_accessors
{
my ($pkg, $methods) = @_;
no strict 'refs';
foreach my $method (@$methods)
{
*{$pkg."::_".$method} =
do {
my $m = $method;
sub {
my $self = shift;
if (@_)
{
$self->{$m} = shift;
}
return $self->{$m};
};
};
}
return;
}
##Define the fields to be used as methods
%fields = (
count => 0,
mean => undef,
sum => undef,
sumsq => undef,
min => undef,
max => undef,
mindex => undef,
maxdex => undef,
sample_range => undef,
variance => undef,
);
__PACKAGE__->_make_accessors( [ grep { $_ ne "variance" } keys(%fields) ] );
__PACKAGE__->_make_accessors( ["_permitted"] );
__PACKAGE__->_make_private_accessors(["variance"]);
sub new {
my $proto = shift;
my $class = ref($proto) || $proto;
my $self = {
%fields,
};
bless ($self, $class);
$self->_permitted(\%fields);
return $self;
}
sub _is_permitted
{
my $self = shift;
my $key = shift;
return exists($self->_permitted()->{$key});
}
sub add_data {
my $self = shift; ##Myself
my $oldmean;
my ($min,$mindex,$max,$maxdex,$sum,$sumsq,$count);
my $aref;
if (ref $_[0] eq 'ARRAY') {
$aref = $_[0];
}
else {
$aref = \@_;
}
##If we were given no data, we do nothing.
return 1 if (!@{ $aref });
##Take care of appending to an existing data set
if (!defined($min = $self->min()))
{
$min = $aref->[$mindex = 0];
}
else
{
$mindex = $self->mindex();
}
if (!defined($max = $self->max()))
{
$max = $aref->[$maxdex = 0];
}
else
{
$maxdex = $self->maxdex();
}
$sum = $self->sum();
$sumsq = $self->sumsq();
$count = $self->count();
##Calculate new mean, sumsq, min and max;
foreach ( @{ $aref } ) {
$sum += $_;
$sumsq += $_**2;
$count++;
if ($_ >= $max) {
$max = $_;
$maxdex = $count-1;
}
if ($_ <= $min) {
$min = $_;
$mindex = $count-1;
}
}
$self->min($min);
$self->mindex($mindex);
$self->max($max);
$self->maxdex($maxdex);
$self->sample_range($max - $min);
$self->sum($sum);
$self->sumsq($sumsq);
$self->mean($sum / $count);
$self->count($count);
##indicator the value is not cached. Variance isn't commonly enough
##used to recompute every single data add.
$self->_variance(undef);
return 1;
}
sub standard_deviation {
my $self = shift; ##Myself
return undef if (!$self->count());
return sqrt($self->variance());
}
##Return variance; if needed, compute and cache it.
sub variance {
my $self = shift; ##Myself
my $count = $self->count();
return undef if !$count;
return 0 if $count == 1;
if (!defined($self->_variance())) {
my $variance = ($self->sumsq()- $count * $self->mean()**2);
# Sometimes due to rounding errors we get a number below 0.
# This makes sure this is handled as gracefully as possible.
#
# See:
#
# https://rt.cpan.org/Public/Bug/Display.html?id=46026
$variance = $variance < 0 ? 0 : $variance / ($count - 1);
$self->_variance($variance);
# Return now to avoid re-entering this sub
# (and therefore save time when many objects are used).
return $variance;
}
return $self->_variance();
}
##Clear a stat. More efficient than destroying an object and calling
##new.
sub clear {
my $self = shift; ##Myself
my $key;
return if (!$self->count());
while (my($field, $value) = each %fields) { # could use a slice assignment here
$self->{$field} = $value;
}
}
1;
package Statistics::Descriptive::Full;
use vars qw($VERSION);
$VERSION = '3.0612';
use Carp;
use POSIX ();
use Statistics::Descriptive::Smoother;
use vars qw(@ISA $a $b %fields);
@ISA = qw(Statistics::Descriptive::Sparse);
use List::MoreUtils ();
use List::Util ();
##Create a list of fields not to remove when data is updated
%fields = (
_permitted => undef, ##Place holder for the inherited key hash
data => undef, ##Our data
samples => undef, ##Number of samples for each value of the data set
presorted => undef, ##Flag to indicate the data is already sorted
_reserved => undef, ##Place holder for this lookup hash
);
__PACKAGE__->_make_private_accessors(
[qw(data samples frequency geometric_mean harmonic_mean
least_squares_fit median mode
skewness kurtosis median_absolute_deviation
)
]
);
__PACKAGE__->_make_accessors([qw(presorted _reserved _trimmed_mean_cache)]);
sub _clear_fields
{
my $self = shift;
# Empty array ref for holding data later!
$self->_data([]);
$self->_samples([]);
$self->_reserved(\%fields);
$self->presorted(0);
$self->_trimmed_mean_cache(+{});
return;
}
##Have to override the base method to add the data to the object
##The proxy method from above is still valid
sub new {
my $proto = shift;
my $class = ref($proto) || $proto;
# Create my self re SUPER
my $self = $class->SUPER::new();
bless ($self, $class); #Re-anneal the object
$self->_clear_fields();
return $self;
}
sub _is_reserved
{
my $self = shift;
my $field = shift;
return exists($self->_reserved->{$field});
}
sub _delete_all_cached_keys
{
my $self = shift;
my %keys = %{ $self };
# Remove reserved keys for this class from the deletion list
delete @keys{keys %{$self->_reserved}};
delete @keys{keys %{$self->_permitted}};
delete $keys{_trimmed_mean_cache};
KEYS_LOOP:
foreach my $key (keys %keys) { # Check each key in the object
delete $self->{$key}; # Delete any out of date cached key
}
$self->{_trimmed_mean_cache} = {}; # just reset this one
return;
}
##Clear a stat. More efficient than destroying an object and calling
##new.
sub clear {
my $self = shift; ##Myself
my $key;
if (!$self->count())
{
return;
}
$self->_delete_all_cached_keys();
$self->SUPER::clear();
$self->_clear_fields();
}
sub add_data {
my $self = shift; ##Myself
my $aref;
if (ref $_[0] eq 'ARRAY') {
$aref = $_[0];
}
else {
$aref = \@_;
}
##If we were given no data, we do nothing.
return 1 if (!@{ $aref });
my $oldmean;
my ($min, $max, $sum, $sumsq);
my $count = $self->count;
# $count is modified lower down, but we need this flag after that
my $has_existing_data = $count;
# Take care of appending to an existing data set
if ($has_existing_data) {
$min = $self->min();
$max = $self->max();
$sum = $self->sum();
$sumsq = $self->sumsq();
}
else {
$min = $aref->[0];
$max = $aref->[0];
$sum = 0;
$sumsq = 0;
}
# need to allow for already having data
$sum += List::Util::sum (@$aref);
$sumsq += List::Util::sum (map {$_ ** 2} @$aref);
$max = List::Util::max ($max, @$aref);
$min = List::Util::min ($min, @$aref);
$count += scalar @$aref;
my $mean = $sum / $count;
$self->min($min);
$self->max($max);
$self->sample_range($max - $min);
$self->sum($sum);
$self->sumsq($sumsq);
$self->mean($mean);
$self->count($count);
##Variance isn't commonly enough
##used to recompute every single data add, so just clear its cache.
$self->_variance(undef);
push @{ $self->_data() }, @{ $aref };
# no need to clear keys if we are a newly populated object,
# and profiling shows it takes a long time when creating
# and populating many stats objects
if ($has_existing_data) {
##Clear the presorted flag
$self->presorted(0);
$self->_delete_all_cached_keys();
}
return 1;
}
sub add_data_with_samples {
my ($self,$aref_values) = @_;
return 1 if (!@{ $aref_values });
my $aref_data = [map { keys %$_ } @{ $aref_values }];
my $aref_samples = [map { values %$_ } @{ $aref_values }];
$self->add_data($aref_data);
push @{ $self->_samples() }, @{ $aref_samples };
return 1;
}
sub get_data {
my $self = shift;
return @{ $self->_data() };
}
sub get_data_without_outliers {
my $self = shift;
if ($self->count() < $Statistics::Descriptive::Min_samples_number) {
carp("Need at least $Statistics::Descriptive::Min_samples_number samples\n");
return;
}
if (!defined $self->{_outlier_filter}) {
carp("Outliers filter not defined\n");
return;
}
my $outlier_candidate_index = $self->_outlier_candidate_index;
my $possible_outlier = ($self->_data())->[$outlier_candidate_index];
my $is_outlier = $self->{_outlier_filter}->($self, $possible_outlier);
return $self->get_data unless $is_outlier;
# Removing the outlier from the dataset
my @good_indexes = grep { $_ != $outlier_candidate_index } (0 .. $self->count() - 1);
my @data = $self->get_data;
my @filtered_data = @data[@good_indexes];
return @filtered_data;
}
sub set_outlier_filter {
my ($self, $code_ref) = @_;
if (!$code_ref || ref($code_ref) ne "CODE") {
carp("Need to pass a code reference");
return;
}
$self->{_outlier_filter} = $code_ref;
return 1;
}
sub _outlier_candidate_index {
my $self = shift;
my $mean = $self->mean();
my $outlier_candidate_index = 0;
my $max_std_deviation = abs(($self->_data())->[0] - $mean);
foreach my $idx (1 .. ($self->count() - 1) ) {
my $curr_value = ($self->_data())->[$idx];
if ($max_std_deviation < abs($curr_value - $mean) ) {
$outlier_candidate_index = $idx;
$max_std_deviation = abs($curr_value - $mean);
}
}
return $outlier_candidate_index;
}
sub set_smoother {
my ($self, $args) = @_;
$args->{data} = $self->_data();
$args->{samples} = $self->_samples();
$self->{_smoother} = Statistics::Descriptive::Smoother->instantiate($args);
}
sub get_smoothed_data {
my ($self, $args) = @_;
if (!defined $self->{_smoother}) {
carp("Smoother object not defined\n");
return;
}
$self->{_smoother}->get_smoothed_data();
}
sub maxdex {
my $self = shift;
return undef if !$self->count;
my $maxdex;
if ($self->presorted) {
$maxdex = $self->count - 1;
}
else {
my $max = $self->max;
$maxdex = List::MoreUtils::first_index {$_ == $max} $self->get_data;
}
$self->{maxdex} = $maxdex;
return $maxdex;
}
sub mindex {
my $self = shift;
return undef if !$self->count;
#my $maxdex = $self->{maxdex};
#return $maxdex if defined $maxdex;
my $mindex;
if ($self->presorted) {
$mindex = 0;
}
else {
my $min = $self->min;
$mindex = List::MoreUtils::first_index {$_ == $min} $self->get_data;
}
$self->{mindex} = $mindex;
return $mindex;
}
sub sort_data {
my $self = shift;
if (! $self->presorted())
{
##Sort the data in descending order
$self->_data([ sort {$a <=> $b} @{$self->_data()} ]);
$self->presorted(1);
##Fix the maxima and minima indices - no, this is unnecessary now we have methods
#$self->mindex(0);
#$self->maxdex($#{$self->_data()});
}
return 1;
}
sub percentile {
my $self = shift;
my $percentile = shift || 0;
##Since we're returning a single value there's no real need
##to cache this.
##If the requested percentile is less than the "percentile bin
##size" then return undef. Check description of RFC 2330 in the
##POD below.
my $count = $self->count();
if ((! $count) || ($percentile < 100 / $count))
{
return; # allow for both scalar and list context
}
$self->sort_data();
my $num = $count*$percentile/100;
my $index = &POSIX::ceil($num) - 1;
my $val = $self->_data->[$index];
return wantarray
? ($val, $index)
: $val
;
}
sub _calc_new_median
{
my $self = shift;
my $count = $self->count();
##Even or odd
if ($count % 2)
{
return $self->_data->[($count-1)/2];
}
else
{
return
(
($self->_data->[($count)/2] + $self->_data->[($count-2)/2] ) / 2
);
}
}
sub median {
my $self = shift;
return undef if !$self->count;
##Cached?
if (! defined($self->_median()))
{
$self->sort_data();
$self->_median($self->_calc_new_median());
}
return $self->_median();
}
sub quantile {
my ( $self, $QuantileNumber ) = @_;
unless ( defined $QuantileNumber and $QuantileNumber =~ m/^0|1|2|3|4$/ ) {
carp("Bad quartile type, must be 0, 1, 2, 3 or 4\n");
return;
}
# check data count after the args are checked - should help debugging
return undef if !$self->count;
$self->sort_data();
return $self->_data->[0] if ( $QuantileNumber == 0 );
my $count = $self->count();
return $self->_data->[ $count - 1 ] if ( $QuantileNumber == 4 );
my $K_quantile = ( ( $QuantileNumber / 4 ) * ( $count - 1 ) + 1 );
my $F_quantile = $K_quantile - POSIX::floor($K_quantile);
$K_quantile = POSIX::floor($K_quantile);
# interpolation
my $aK_quantile = $self->_data->[ $K_quantile - 1 ];
return $aK_quantile if ( $F_quantile == 0 );
my $aKPlus_quantile = $self->_data->[$K_quantile];
# Calcul quantile
my $quantile = $aK_quantile
+ ( $F_quantile * ( $aKPlus_quantile - $aK_quantile ) );
return $quantile;
}
sub _real_calc_trimmed_mean
{
my $self = shift;
my $lower = shift;
my $upper = shift;
my $lower_trim = int ($self->count()*$lower);
my $upper_trim = int ($self->count()*$upper);
my ($val,$oldmean) = (0,0);
my ($tm_count,$tm_mean,$index) = (0,0,$lower_trim);
$self->sort_data();
while ($index <= $self->count() - $upper_trim -1)
{
$val = $self->_data()->[$index];
$oldmean = $tm_mean;
$index++;
$tm_count++;
$tm_mean += ($val - $oldmean) / $tm_count;
}
return $tm_mean;
}
sub trimmed_mean
{
my $self = shift;
my ($lower,$upper);
#upper bound is in arg list or is same as lower
if (@_ == 1)
{
($lower,$upper) = ($_[0],$_[0]);
}
else
{
($lower,$upper) = ($_[0],$_[1]);
}
# check data count after the args
return undef if !$self->count;
##Cache
my $thistm = join ':',$lower,$upper;
my $cache = $self->_trimmed_mean_cache();
if (!exists($cache->{$thistm}))
{
$cache->{$thistm} = $self->_real_calc_trimmed_mean($lower, $upper);
}
return $cache->{$thistm};
}
sub _test_for_too_small_val
{
my $self = shift;
my $val = shift;
return (abs($val) <= $Statistics::Descriptive::Tolerance);
}
sub _calc_harmonic_mean
{
my $self = shift;
my $hs = 0;
foreach my $item ( @{$self->_data()} )
{
##Guarantee that there are no divide by zeros
if ($self->_test_for_too_small_val($item))
{
return;
}
$hs += 1/$item;
}
if ($self->_test_for_too_small_val($hs))
{
return;
}
return $self->count()/$hs;
}
sub harmonic_mean
{
my $self = shift;
if (!defined($self->_harmonic_mean()))
{
$self->_harmonic_mean(scalar($self->_calc_harmonic_mean()));
}
return $self->_harmonic_mean();
}
sub mode
{
my $self = shift;
if (!defined ($self->_mode()))
{
my $mode = 0;
my $occurances = 0;
my %count;
foreach my $item (@{ $self->_data() })
{
my $count = ++$count{$item};
if ($count > $occurances)
{
$mode = $item;
$occurances = $count;
}
}
$self->_mode(
($occurances > 1)
? {exists => 1, mode => $mode}
: {exists => 0,}
);
}
my $m = $self->_mode;
return $m->{'exists'} ? $m->{mode} : undef;
}
sub geometric_mean {
my $self = shift;
return undef if !$self->count;
if (!defined($self->_geometric_mean()))
{
my $gm = 1;
my $exponent = 1/$self->count();
for my $val (@{ $self->_data() })
{
if ($val < 0)
{
return undef;
}
$gm *= $val**$exponent;
}
$self->_geometric_mean($gm);
}
return $self->_geometric_mean();
}
sub skewness {
my $self = shift;
if (!defined($self->_skewness()))
{
my $n = $self->count();
my $sd = $self->standard_deviation();
my $skew;
# skip if insufficient records
if ( $sd && $n > 2) {
my $mean = $self->mean();
my $sum_pow3;
foreach my $rec ( $self->get_data ) {
$sum_pow3 += (($rec - $mean) / $sd) ** 3;
}
my $correction = $n / ( ($n-1) * ($n-2) );
$skew = $correction * $sum_pow3;
}
$self->_skewness($skew);
}
return $self->_skewness();
}
sub kurtosis {
my $self = shift;
if (!defined($self->_kurtosis()))
{
my $kurt;
my $n = $self->count();
my $sd = $self->standard_deviation();
if ( $sd && $n > 3) {
my $mean = $self->mean();
my $sum_pow4;
foreach my $rec ( $self->get_data ) {
$sum_pow4 += ( ($rec - $mean ) / $sd ) ** 4;
}
my $correction1 = ( $n * ($n+1) ) / ( ($n-1) * ($n-2) * ($n-3) );
my $correction2 = ( 3 * ($n-1) ** 2) / ( ($n-2) * ($n-3) );
$kurt = ( $correction1 * $sum_pow4 ) - $correction2;
}
$self->_kurtosis($kurt);
}
return $self->_kurtosis();
}
sub frequency_distribution_ref
{
my $self = shift;
my @k = ();
# Must have at least two elements
if ($self->count() < 2)
{
return undef;
}
if ((!@_) && (defined $self->_frequency()))
{
return $self->_frequency()
}
my %bins;
my $partitions = shift;
if (ref($partitions) eq 'ARRAY')
{
@k = @{ $partitions };
return undef unless @k; ##Empty array
if (@k > 1) {
##Check for monotonicity
my $element = $k[0];
for my $next_elem (@k[1..$#k]) {
if ($element > $next_elem) {
carp "Non monotonic array cannot be used as frequency bins!\n";
return undef;
}
$element = $next_elem;
}
}
%bins = map { $_ => 0 } @k;
}
else
{
return undef unless $partitions >= 1;
my $interval = $self->sample_range() / $partitions;
foreach my $idx (1 .. ($partitions-1))
{
push @k, ($self->min() + $idx * $interval);
}
$bins{$self->max()} = 0;
push @k, $self->max();
}
ELEMENT:
foreach my $element (@{$self->_data()})
{
foreach my $limit (@k)
{
if ($element <= $limit)
{
$bins{$limit}++;
next ELEMENT;
}
}
}
return $self->_frequency(\%bins);
}
sub frequency_distribution {
my $self = shift;
my $ret = $self->frequency_distribution_ref(@_);
if (!defined($ret))
{
return undef;
}
else
{
return %$ret;
}
}
sub least_squares_fit {
my $self = shift;
return () if $self->count() < 2;
##Sigma sums
my ($sigmaxy, $sigmax, $sigmaxx, $sigmayy, $sigmay) = (0,0,0,0,$self->sum);
my ($xvar, $yvar, $err);
##Work variables
my ($iter,$y,$x,$denom) = (0,0,0,0);
my $count = $self->count();
my @x;
##Outputs
my ($m, $q, $r, $rms);
if (!defined $_[1]) {
@x = 1..$self->count();
}
else {
@x = @_;
if ( $self->count() != scalar @x) {
carp "Range and domain are of unequal length.";
return ();
}
}
foreach $x (@x) {
$y = $self->_data->[$iter];
$sigmayy += $y * $y;
$sigmaxx += $x * $x;
$sigmaxy += $x * $y;
$sigmax += $x;
$iter++;
}
$denom = $count * $sigmaxx - $sigmax*$sigmax;
return ()
unless abs( $denom ) > $Statistics::Descriptive::Tolerance;
$m = ($count*$sigmaxy - $sigmax*$sigmay) / $denom;
$q = ($sigmaxx*$sigmay - $sigmax*$sigmaxy ) / $denom;
$xvar = $sigmaxx - $sigmax*$sigmax / $count;
$yvar = $sigmayy - $sigmay*$sigmay / $count;
$denom = sqrt( $xvar * $yvar );
return () unless (abs( $denom ) > $Statistics::Descriptive::Tolerance);
$r = ($sigmaxy - $sigmax*$sigmay / $count )/ $denom;
$iter = 0;
$rms = 0.0;
foreach (@x) {
##Error = Real y - calculated y
$err = $self->_data->[$iter] - ( $m * $_ + $q );
$rms += $err*$err;
$iter++;
}
$rms = sqrt($rms / $count);
$self->_least_squares_fit([$q, $m, $r, $rms]);
return @{ $self->_least_squares_fit() };
}
sub median_absolute_deviation {
my ($self) = @_;
if (!defined($self->_median_absolute_deviation()))
{
my $stat = $self->new;
$stat->add_data(map { abs($_ - $self->median) } $self->get_data);
$self->_median_absolute_deviation($stat->median);
}
return $self->_median_absolute_deviation();
}
1;
package Statistics::Descriptive;
##All modules return true.
1;
__END__
=head1 NAME
Statistics::Descriptive - Module of basic descriptive statistical functions.
=head1 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);
$Statistics::Descriptive::Tolerance = 1e-10;
=head1 DESCRIPTION
This module provides basic functions used in descriptive statistics.
It has an object oriented design and supports two different types of
data storage and calculation objects: sparse and full. With the sparse
method, none of the data is stored and only a few statistical measures
are available. Using the full method, the entire data set is retained
and additional functions are available.
Whenever a division by zero may occur, the denominator is checked to be
greater than the value C<$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.
=head1 METHODS
=head2 Sparse Methods
=over 5
=item $stat = Statistics::Descriptive::Sparse->new();
Create a new sparse statistics object.
=item $stat->clear();
Effectively the same as
my $class = ref($stat);
undef $stat;
$stat = new $class;
except more efficient.
=item $stat->add_data(1,2,3);
Adds data to the statistics variable. The cached statistical values are
updated automatically.
=item $stat->count();
Returns the number of data items.
=item $stat->mean();
Returns the mean of the data.
=item $stat->sum();
Returns the sum of the data.
=item $stat->variance();
Returns the variance of the data. Division by n-1 is used.
=item $stat->standard_deviation();
Returns the standard deviation of the data. Division by n-1 is used.
=item $stat->min();
Returns the minimum value of the data set.
=item $stat->mindex();
Returns the index of the minimum value of the data set.
=item $stat->max();
Returns the maximum value of the data set.
=item $stat->maxdex();
Returns the index of the maximum value of the data set.
=item $stat->sample_range();
Returns the sample range (max - min) of the data set.
=back
=head2 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.
=over 5
=item $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.
=item $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.
I<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>
=item $stat->add_data_with_samples([{1 => 10}, {2 => 20}, {3 => 30},]);
Add data to the statistics variable and set the number of samples each value
has been built with. The data is the key of each element of the input array
ref, while the value is the number of samples: [{data1 => smaples1}, {data2 =>
samples2}, ...].
B<NOTE:> The number of samples is only used by the smoothing function and is
ignored otherwise. It is not equivalent to repeat count. In order to repeat
a certain datum more than one time call add_data() like this:
my $value = 5;
my $repeat_count = 10;
$stat->add_data(
[ ($value) x $repeat_count ]
);
=item $stat->get_data();
Returns a copy of the data array.
=item $stat->get_data_without_outliers();
Returns a copy of the data array without outliers. The number minimum of
samples to apply the outlier filtering is C<$Statistics::Descriptive::Min_samples_number>,
4 by default.
A function to detect outliers need to be defined (see C<set_outlier_filter>),
otherwise the function will return an undef value.
The filtering will act only on the most extreme value of the data set
(i.e.: value with the highest absolute standard deviation from the mean).
If there is the need to remove more than one outlier, the filtering
need to be re-run for the next most extreme value with the initial outlier removed.
This is not always needed since the test (for example Grubb's test) usually can only detect
the most exreme value. If there is more than one extreme case in a set,
then the standard deviation will be high enough to make neither case an outlier.
=item $stat->set_outlier_filter($code_ref);
Set the function to filter out the outlier.
C<$code_ref> is the reference to the subroutine implementing the filtering
function.
Returns C<undef> for invalid values of C<$code_ref> (i.e.: not defined or not a
code reference), C<1> otherwise.
=over 4
=item
Example #1: Undefined code reference
my $stat = Statistics::Descriptive::Full->new();
$stat->add_data(1, 2, 3, 4, 5);
print $stat->set_outlier_filter(); # => undef
=item
Example #2: Valid code reference
sub outlier_filter { return $_[1] > 1; }
my $stat = Statistics::Descriptive::Full->new();
$stat->add_data( 1, 1, 1, 100, 1, );
print $stat->set_outlier_filter( \&outlier_filter ); # => 1
my @filtered_data = $stat->get_data_without_outliers();
# @filtered_data is (1, 1, 1, 1)
In this example the series is really simple and the outlier filter function as well.
For more complex series the outlier filter function might be more complex
(see Grubbs' test for outliers).
The outlier filter function will receive as first parameter the Statistics::Descriptive::Full object,
as second the value of the candidate outlier. Having the object in the function
might be useful for complex filters where statistics property are needed (again see Grubbs' test for outlier).
=back
=item $stat->set_smoother({ method => 'exponential', coeff => 0, });
Set the method used to smooth the data and the smoothing coefficient.
See C<Statistics::Smoother> for more details.
=item $stat->get_smoothed_data();
Returns a copy of the smoothed data array.
The smoothing method and coefficient need to be defined (see C<set_smoother>),
otherwise the function will return an undef value.
=item $stat->sort_data();
Sort the stored data and update the mindex and maxdex methods. This
method uses perl's internal sort.
=item $stat->presorted(1);
=item $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.
=item $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.
=item $stat->kurtosis();
Returns the kurtosis of the data.
Positive is peaked, negative is flattened.
=item $x = $stat->percentile(25);
=item ($x, $index) = $stat->percentile(25);
Sorts the data and returns the value that corresponds to the
percentile as defined in RFC2330:
=over 4
=item
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.
=back
(Taken from:
I<RFC2330 - Framework for IP Performance Metrics>,
Section 11.3. Defining Statistical Distributions.
RFC2330 is available from:
L<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.
=item $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 B<type 7>).
The generic function quantile produces sample quantiles corresponding to the given probabilities.
B<$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
=item $stat->median();
Sorts the data and returns the median value of the data.
=item $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.
=item $stat->geometric_mean();
Returns the geometric mean of the data.
=item my $mode = $stat->mode();
Returns the mode of the data. The mode is the most commonly occuring datum.
See L<http://en.wikipedia.org/wiki/Mode_%28statistics%29> . If all values
occur only once, then mode() will return undef.
=item $stat->trimmed_mean(ltrim[,utrim]);
C<trimmed_mean(ltrim)> returns the mean with a fraction C<ltrim>
of entries at each end dropped. C<trimmed_mean(ltrim,utrim)>
returns the mean after a fraction C<ltrim> has been removed from the
lower end of the data and a fraction C<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.
=item $stat->frequency_distribution_ref($partitions);
=item $stat->frequency_distribution_ref(\@bins);
=item $stat->frequency_distribution_ref();
C<frequency_distribution_ref($partitions)> slices the data into
C<$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.
C<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. C<@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 C<frequency_distribution_ref()> with no arguments returns the last
distribution calculated, if such exists.
=item my %hash = $stat->frequency_distribution($partitions);
=item my %hash = $stat->frequency_distribution(\@bins);
=item my %hash = $stat->frequency_distribution();
Same as C<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.
=item $stat->least_squares_fit();
=item $stat->least_squares_fit(@x);
C<least_squares_fit()> performs a least squares fit on the data,
assuming a domain of C<@x> or a default of 1..$stat->count(). It
returns an array of four elements C<($q, $m, $r, $rms)> where
=over 4
=item C<$q and $m>
satisfy the equation C($y = $m*$x + $q).
=item C<$r>
is the Pearson linear correlation cofficient.
=item C<$rms>
is the root-mean-square error.
=back
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 C<least_squares_fit()> with no arguments defaults
to using the current range, there is no caching of the results.
=back
=head1 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:
=over 4
=item *
Your version of perl. This can be obtained by typing perl C<-v> at
the command line.
=item *
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.
=item *
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.
=back
=head1 AUTHOR
Current maintainer:
Shlomi Fish, L<http://www.shlomifish.org/> , C<shlomif@cpan.org>
Previously:
Colin Kuskie
My email address can be found at http://www.perl.com under Who's Who
or at: https://metacpan.org/author/COLINK .
=head1 CONTRIBUTORS
Fabio Ponciroli & Adzuna Ltd. team (outliers handling)
=head1 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.
=head1 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.
=head1 LICENSE
This program is free software; you can redistribute it and/or modify it
under the same terms as Perl itself.
=cut