roderick garton > Statistics-Sequences-Joins-0.11 > Statistics::Sequences::Joins

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NAME ^

Statistics::Sequences::Joins Wishart-Hirshfeld statistics for number of alternations between two elements of a dichotomous sequence

SYNOPSIS ^

 use strict;
 use Statistics::Sequences::Joins 0.11; # methods/args here are not compatible with earlier versions
 my $joins = Statistics::Sequences::Joins->new();
 $joins->load(qw/1 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 1/); # dichotomous sequence (any values); or send as "data => $aref" with each stat call
 my $val = $joins->observed(); # other methods include: expected(), variance(), obsdev() and stdev()
 $val = $joins->expected(trials => 20); # by-passing need for data; also works with other methods except observed()
 $val = $joins->z_value(tails => 1, ccorr => 1); # or want an array & get back both z- and p-value
 $val = $joins->z_value(trials => 20, observed => 10, tails => 1, ccorr => 1); # by-pass need for data; also works with p_value()
 $val = $joins->p_value(tails => 1); # assuming data are loaded; alias: test()
 my $href = $joins->stats_hash(values => {observed => 1, p_value => 1}); # include any other stat-method as needed
 $joins->dump(values => {observed => 1, expected => 1, p_value => 1}, format => 'line', flag => 1, precision_s => 3, precision_p => 7);
 # prints: observed = 10.000, expected = 9.500, p_value = 1.0000000

DESCRIPTION ^

Joins are similar to runs but are counted for every alternation between dichotomous events (state, element, letter ...) whereas runs are counted for each continuous segment between alternations. For example, joins are marked out with asterisks for the following sequence:

 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0
    * *     * *       *   *     *       *

So there's a join (of 0 and 1) at indices 1 and 2, then immediately another join (of 1 and 0) at indices 2 and 3, and then another join at 5 and 6 ... for a total count of eight joins.

There are methods to get the observed and expected joincounts, and the expected variance in joincount. Counting up the observed number of joins needs some data to count through, but getting the expectation and variance for the joincount - if not sent actual data in the call, or already cached via load - can just be fed with the number of trials, and, optionally, the binomial event probability (of one of the two events occurring; default = 0.50). Note that this also differs from the way runs are counted: the expected joincount, and its variance, where the relative frequencies of the two events are counted off the given data (although this option is availabe for figuring out the binomial probability here, too).

Have non-dichotomous, continuous or multinomial data? See Statistics::Data::Dichotomize for how to prepare non-dichotomous data, whether numerical or made up of categorical events, for test of joins.

METHODS ^

Methods are those described in Statistics::Sequences, but can be used directly from this module, as follows.

new

 $joins = Statistics::Sequences::Joins->new();

Returns a new Joins object. Expects/accepts no arguments but the classname.

load

 $joins->load(@data); # anonymously
 $joins->load(\@data);
 $joins->load('sample1' => \@data); # labelled whatever

Loads data anonymously or by name - see load in the Statistics::Data manpage for details on the various ways data can be loaded and then retrieved (more than shown here).

After the load, the data are read to ensure that they contain only two unique elements - if not, carp occurs and 0 rather than 1 is returned.

Alternatively, skip this action; data don't always have to be loaded to use the stats methods here. To get the observed number of joins, data of course have to be loaded, but other stats can be got if given the observed count - otherwise, they too depend on data having been loaded.

Every load unloads all previous loads and any additions to them.

add, read, unload

See Statistics::Data for these additional operations on data that have been loaded.

observed, joincount_observed, jco

 $count = $joins->observed(); # assumes data have already been loaded
 $count = $joins->observed(data => [1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1]); # assumes window = 1

Returns the number of joins in a sequence - i.e., when, from trial 2 on, the event on trial i doesn't equal the event on trial i - 1. So the following sequence adds up to 7 joins like this:

 Sequence:  1 0 0 0 1 0 0 1 0 1 1 0 
 JoinCount: 0 1 1 1 2 3 3 4 5 6 6 7

The data to test can already have been loaded, or you send it directly keyed as data.

expected, joincount_expected, jce

 $val = $joins->expected(); # assumes data already loaded, uses default prob value (.5)
 $val = $joins->expected(data => [1, 0, 0, 0, 1, 0, 0, 1, 0, 1]); # count these data, use default prob value (.5)
 $val = $joins->expected(data => [1, 0, 0, 0, 1, 0, 0, 1, 0, 1], prob => .2); # count these data, use given prob value
 $val = $joins->expected(data => [1, 0, 0, 0, 1, 0, 0, 1, 0, 1], state => 1); # count off trial numbers and prob. of event
 $val = $joins->expected(prob => .2, trials => 10); # use this trial number and probability of one of the 2 events

Returns the expected number of joins between every element of the given data, or for data of the given attributes, using.

  E[J] = 2(N – 1)pq

where N is the number of observations/trials (width = 1 segments),

p is the expected probability of the joined event taking on its observed value, and

q is (1 - p), the expected probability of the joined event not taking on its observed value.

The data to test can already have been loaded, or you send it directly keyed as data. The data are only needed to count off the number of trials, and the proportion of 1s (or other given state of the two), if the trials and prob attributes are not defined. If state is defined, then prob is worked out from the actual data (as long as there are some, or 1/2 is assumed). If state is not defined, prob takes the value you give to it, or, if it too is not defined, then 1/2.

Counting up the observed number of joins needs some data to count through, but getting the expectation and variance for the joincount can just be fed with the number of trials, and, optionally, the probability of one of the two events.

variance, joincount_variance, jcv

 $val = $joins->variance(); # assume data already "loaded" for counting
 $val = $joins->variance(data => $aref); # use inplace array reference, will use default prob of 1/2
 $val = $joins->variance(data => [1, 0, 0, 0, 1, 0, 0, 1, 0, 1], state => 1); # count off trial numbers and prob. of event
 $val = $joins->variance(trials => number, prob => prob); # use this trial number and probability of one of the 2 events

Returns the expected variance in the number of joins for the given data.

  V[J] = 4Npq(1 – 3pq) – 2pq(3 – 10pq)

defined as above for joincount_expected.

The data to test can already have been loaded, or you send it directly keyed as data. The data are only needed to count off the number of trials, and the proportion of 1s (or other given state of the two), if the trials and prob attributes aren't defined. If state is defined, then prob is worked out from the actual data (as long as there are some, or expect a croak). If state is not defined, prob takes the value you give to it, or, if it too is not defined, then 1/2.

obsdev, observed_deviation

 $v = $joins->obsdev(); # use data already loaded - anonymously; or specify its "label" or "index" - see observed()
 $v = $joins->obsdev(data => [qw/blah bing blah blah blah/]); # use these data

Returns the deviation of (difference between) observed and expected joins for the loaded/given sequence (O - E).

stdev, standard_deviation

 $v = $joins->stdev(); # use data already loaded - anonymously; or specify its "label" or "index" - see observed()
 $v = $joins->stdev(data => [qw/blah bing blah blah blah/]);

Returns square-root of the variance.

z_value, joincount_zscore, jzs, zscore

 $val = $joins->z_value(); # data already loaded, use default windows and prob
 $val = $joins->z_value(data => $aref, prob => .5, ccorr => 1);
 ($zvalue, $pvalue) =  $joins->z_value(data => $aref, prob => .5, ccorr => 1, tails => 2); # same but wanting an array, get the p-value too

Returns the zscore from a test of joincount deviation, taking the joincount expected away from that observed and dividing by the root expected joincount variance, by default with a continuity correction to expectation. Called wanting an array, returns the z-value with its p-value for the tails (1 or 2) given.

The data to test can already have been loaded, or you send it directly keyed as data.

Other options are precision_s (for the z_value) and precision_p (for the p_value).

p_value, test, joins_test, jct

 $p = $joins->p_value(); # using loaded data and default args
 $p = $joins->p_value(ccorr => 0|1, tails => 1|2); # normal-approximation based on loaded data
 $p = $joins->p_value(data => [1, 0, 1, 1, 0], exact => 1); #  using given data (by-passing load and read)
 $p = $joins->p_value(trials => 20, observed => 10); # without using data, specifying its size and join-count

Test data for significance of the number of joins by deviation ratio (obsdev / stdev). Returns the Joins object, lumped with a z_value, p_value, and the descriptives observed, expected and variance. Data are those already loaded, or directly keyed as data

stats_hash

 $href = $joins->stats_hash(values => {observed => 1, expected => 1, variance => 1, z_value => 1, p_value => 1}, prob => .5, ccorr => 1);

Returns a hashref for the counts and stats as specified in its "values" argument, and with any options for calculating them (e.g., exact for p_value). See "stats_hash" in Statistics::Sequences for details. If calling via a "joins" object, the option "stat => 'joins'" is not needed (unlike when using the parent "sequences" object).

dump

 $joins->dump(values => { observed => 1, variance => 1, p_value => 1}, exact => 1, flag => 1,  precision_s => 3); # among other options

Print Joins-test results to STDOUT. See dump in the Statistics::Sequences manpage for details.

EXAMPLE ^

Here the problem is to assess the degree of consistency of in number of matches between target and response obtained in each of 200 runs of 25 trials each. The number of matches expected on the basis of chance is 5 per run. To test for sustained high or low scoring sequences, a join is defined as the point at which a score on one side of this value (4, 3, 2, etc.) is followed by a score on the other side (6, 7, 8, etc.). Ignoring scores equalling the expectation value (5), the probability of a join is 1/2, or 0.5 (the default value to test), assuming that, say, a score of 4 is as likely as a score of 6, and anything greater than a deviation of 5 (from 5) is improbable/impossible.

 use Statistics::Sequences;

 # Conduct pseudo identification 5 x 5 runs:
 my ($i, $hits, $stimulus, $response, @scores);
 foreach ($i = 0; $i < 200; $i++) {
    $scores[$i] = 0;
    for (0 .. 24) {
        $stimulus = (qw/circ plus rect star wave/)[int(rand(5))];
        $response = (qw/circ plus rect star wave/)[int(rand(5))];
        $scores[$i]++ if $stimulus eq $response;
    }
  }

  my $seq = Statistics::Sequences->new();
  $seq->load(@scores);
  $seq->cut(value => 5, equal => 0); # value is the expected number of matches (Np); ignoring values equal to this
  $seq->test(stat => 'joins', tails => 1, ccorr => 1)->dump(text => 1, flag => 1);
  # prints, e.g., Joins: expected = 79.00, observed = 67.00, Z = -1.91, 1p = 0.028109*

REFERENCES ^

Wishart, J. & Hirshfeld, H. O. (1936). A theorem concerning the distribution of joins between line segments. Journal of the London Mathematical Society, 11, 227.

SEE ALSO ^

Statistics::Sequences::Runs : Analogous test.

Statistics::Sequences::Pot : Another concept of sequential structure.

BUGS/LIMITATIONS ^

No computational bugs as yet identfied. Hopefully this will change, given time.

REVISION HISTORY ^

See CHANGES in installation dist for revisions.

AUTHOR/LICENSE ^

Copyright (c) 2006-2013 Roderick Garton

rgarton AT cpan DOT org

This program is free software. It may be used, redistributed and/or modified under the same terms as Perl-5.6.1 (or later) (see http://www.perl.com/perl/misc/Artistic.html).

Disclaimer

To the maximum extent permitted by applicable law, the author of this module disclaims all warranties, either express or implied, including but not limited to implied warranties of merchantability and fitness for a particular purpose, with regard to the software and the accompanying documentation.

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