File Coverage

blib/lib/Statistics/ANOVA/EffectSize.pm

Criterion	Covered	Total	%
statement	34	47	72.3
branch	4	14	28.5
condition			n/a
subroutine	14	20	70.0
pod	7	7	100.0
total	59	88	67.0

package Statistics::ANOVA::EffectSize;

use 5.006;
use strict;
use warnings;
use base qw(Statistics::Data);
use Carp qw(croak);
use List::AllUtils qw(any);
$Statistics::ANOVA::EffectSize::VERSION = '0.01';

=head1 NAME

Statistics::ANOVA::EffectSize - Calculate effect-sizes from ANOVAs incl. eta-squared and omega-squared

=head1 VERSION

This is documentation for B of Statistics::ANOVA::EffectSize.

=head1 SYNOPSIS

 use Statistics::ANOVA::EffectSize;
 my $es = Statistics::ANOVA::EffectSize->new();
 $es->load(HOA); # a hash of arefs, or other, as in Statistics::Data
 my $etasq = $es->eta_squared(independent => BOOL, partial => 1); # or give data => HOA here
 my $omgsq = $es->omega_squared(independent => BOOL);
 # or calculate not from loaded data but directly:

=head2 DESCRIPTION

Calculates effect-sizes from ANOVAs.

For I-squared, values range from 0 to 1, 0 indicating no effect, 1 indicating difference between at least two DV means. Generally indicates the proportion of variance in the DV related to an effect.

For I-squared, size is conventionally described as small where omega_sq = .01, medium if omega_sq = .059, and strong if omega_sq = .138 (Cohen, 1969).

=head1 SUBROUTINES/METHODS

Rather than working from raw data, these methods are given the statistics, like sums-of-squares, needed to calculate the effect-sizes.

=head2 eta_sq_partial_by_ss, r_squared

 $es->eta_sq_partial_by_ss(ss_b => NUM, ss_w => NUM);

Returns partial I-squared given between- and within-group sums-of-squares (I):

=for html   η2P = SSb / ( SSb + SSw )

This is also what is commonly designated as I-squared (Maxwell & Delaney, 1990, Eq. 90).

=cut

sub eta_sq_partial_by_ss {
    my ($self, %args) = @_;
    croak 'Undefined values needed to calculate partial eta-squared by sums-of-squares' if any { ! defined $args{$_} } (qw/ss_b ss_w/);
    return $args{'ss_b'} / ( $args{'ss_b'} + $args{'ss_w'} );
}
*r_squared = \&eta_sq_partial_by_ss;

=head2 r_squared_adj

    $es->r_squared_adj(ss_b => NUM, ss_w => NUM, df_b => NUM, df_w => NUM);

Returns adjusted I-squared.

=cut

sub r_squared_adj {
    my ($self, %args) = @_;
    my $r_squared = $self->r_squared(%args); # will check for ss_b and ss_w
    croak 'Could not obtain values to calculate adjusted r-squared' if any { ! defined $args{$_} } (qw/df_b df_w/);
    return 1 - ( ($args{'df_b'} + $args{'df_w'}) / $args{'df_w'} ) * ( 1 - $r_squared );
}

=head2 eta_sq_partial_by_f

 $es->eta_sq_partial_by_f(f_value => NUM , df_b => NUM, df_w => NUM);

Returns partial I-squared given I-value and its between- and within-groups degrees-of-freedom (I):

=for html   η2P = ( dfb . F ) / ( dfb . F + dfw )

=cut

sub eta_sq_partial_by_f {
    my ($self, %args) = @_;
    croak 'Could not obtain values to calculate partial eta-squared by F-value' if any { ! defined $args{$_} } (qw/df_b df_w f_value/);
    return ( $args{'df_b'} * $args{'f_value'} ) / ( $args{'df_b'} * $args{'f_value'} + $args{'df_w'} );
}

=head2 omega_sq_partial_by_ss

 $es->omega_sq_partial_by_ss(df_b => NUM, df_w => NUM, ss_b => NUM, ss_w => NUM);

Returns partial I-squared given the between- and within-groups sums-of-squares and degrees-of-freedom.

Essentially as given by Maxwell & Delaney (1990), Eq. 92:

=for html   ω2P = ( ssb — (dfb . SSw / dfb) ) / (( SSb + SSw ) + SSw / dfw )

=cut

sub omega_sq_partial_by_ss {
    my ($self, %args) = @_;
    croak 'Undefined values for calculating partial omega-squared by sums-of-squares' if any { ! defined $args{$_} } (qw/ss_b ss_w df_b df_w/);
    return  ( $args{'ss_b'} - ( $args{'df_b'} * $args{'ss_w'} / $args{'df_w'} ) ) / ( ( $args{'ss_b'} + $args{'ss_w'} ) + $args{'ss_w'} / $args{'df_w'} );
}

=head2 omega_sq_partial_by_ms

 $es->omega_sq_partial_by_ms(df_b => NUM, ms_b => NUM, ms_w => NUM, count => NUM);

Returns partial I-squared given between- and within-group mean sums-of-squares (I). Also needs between-groups degrees-of-freedom and sample-size (here labelled "count") I:

=for html   ω2P = dfb  ( MSb – MSw ) / ( dfb . MSb + ( N – dfb ) MSw )

=cut

sub omega_sq_partial_by_ms {
    my ($self, %args) = @_;
    croak 'Could not obtain values to calculate partial omega-squared by mean sums-of-squares' if any { ! defined $_ } values %args;
    return  $args{'df_b'} * ( $args{'ms_b'} - $args{'ms_w'} )  / ( $args{'df_b'} * $args{'ms_b'} + ( $args{'count'} - $args{'df_b'} ) * $args{'ms_w'} );
}

=head2 omega_sq_partial_by_f

 $es->omega_sq_partial_by_ms(f_value => NUM, df_b => NUM, df_w => NUM);

Returns partial I-squared given I-value and its between- and within-group degrees-of-freedom (I):

=for html   ω2P(est.) = ( F - 1 ) / ( F + ( dfw + 1 ) / dfb )

This is an estimate formulated by L that will not ordinarily agree with the method by (mean) sum-of-squares.

=cut

sub omega_sq_partial_by_f {
    my ($self, %args) = @_;
    croak 'Could not obtain values to calculate partial omega-squared by mean sums-of-squares' if any { ! defined $_ } values %args;
    return ( $args{'f_value'} - 1 ) / ( $args{'f_value'} + ( $args{'df_w'} + 1)/$args{'df_b'} );
}

=head2 eta_to_omega

 $es->eta_to_omega(df_b => NUM, df_w => NUM, eta_sq => NUM);

Returns I-squared based on I-squared and the between- and within-groups degrees-of-freedom.

=for html   ω2P = ( η2P(dfb + dfw) – dfb ) /  ( η2P(dfb + dfw) – dfb ) + ( (dfw + 1)(1 – η2P) ) )

=cut

sub eta_to_omega {
    my ($self, %args) = @_;
    croak 'Could not obtain values to calculate partial omega-squared by mean sums-of-squares' if any { ! defined $_ } values %args;
    my $num = $args{'eta_sq'} * ( $args{'df_b'} + $args{'df_w'} ) - $args{'df_b'};
    return $num / ( $num + ( ( $args{'df_w'} + 1) * ( 1 - $args{'eta_sq'} ) ) );
}

=head1 DEPENDENCIES

L : C method

L : used as base.

=head1 DIAGNOSTICS

=over 4

=item Could not obtain values to calculate ...

Ced if the sufficient statistics have not been provided.

=back

=head1 REFERENCES

Cohen, J. (1969). I. New York, US: Academic.

Lakens, D. (2015). Why you should use omega-squared instead of eta-squared, I [L].

Maxwell, S. E., & Delaney, H. D. (1990). I. Belmont, CA, US: Wadsworth.

Olejnik, S., & Algina, J. (2003). Generalized eta and omega squared statistics: Measures of effect size for some common research designs. I, I<8>, 434-447. doi: L<10.1037/1082-989X.8.4.434|http://dx.doi.org/10.1037/1082-989X.8.4.434>.

=head1 AUTHOR

Roderick Garton, C<<  >>

=head1 BUGS

Please report any bugs or feature requests to C, or through
the web interface at L.  I will be notified, and then you'll
automatically be notified of progress on your bug as I make changes.

=head1 NOTES


For independent variables only, omega-square (raw):

w2 = (SSeffect - (dfeffect)(MSerror)) / MSerror + SStotal

=head1 SUPPORT

You can find documentation for this module with the perldoc command.

    perldoc Statistics::ANOVA::EffectSize


You can also look for information at:

=over 4

=item * RT: CPAN's request tracker (report bugs here)

L

=item * AnnoCPAN: Annotated CPAN documentation

L

=item * CPAN Ratings

L

=item * Search CPAN

L

=back

=head1 LICENSE AND COPYRIGHT

Copyright 2015 Roderick Garton.

This program is free software; you can redistribute it and/or modify it
under the terms of either: the GNU General Public License as published
by the Free Software Foundation; or the Artistic License.

See L for more information.

=cut

1; # End of Statistics::ANOVA::EffectSize