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# NAME

PDL::Stats - a collection of statistics modules in Perl Data Language, with a quick-start guide for non-PDL people.

# DESCRIPTION

Loads modules named below, making the functions available in the current namespace.

Properly formatted documentations online at http://pdl-stats.sf.net

# SYNOPSIS

``````    use PDL::LiteF;        # loads less modules
use PDL::NiceSlice;    # preprocessor for easier pdl indexing syntax

use PDL::Stats;

# Is equivalent to the following:

use PDL::Stats::Basic;
use PDL::Stats::GLM;
use PDL::Stats::Kmeans;
use PDL::Stats::TS;

# and the following if installed;

use PDL::Stats::Distr;
use PDL::GSL::CDF;``````

# QUICK-START FOR NON-PDL PEOPLE

Enjoy PDL::Stats without having to dive into PDL, just wet your feet a little. Three key words two concepts and an icing on the cake, you should be well on your way there.

## pdl

The magic word that puts PDL::Stats at your disposal. pdl creates a PDL numeric data object (a pdl, pronounced "piddle" :/ ) from perl array or array ref. All PDL::Stats methods, unless meant for regular perl array, can then be called from the data object.

``````    my @y = 0..5;

my \$y = pdl @y;

# a simple function

my \$stdv = \$y->stdv;

# you can skip the intermediate \$y

my \$stdv = stdv( pdl @y );

# a more complex method, skipping intermediate \$y

my @x1 = qw( y y y n n n );
my @x2 = qw( 1 0 1 0 1 0 )

# do a two-way analysis of variance with y as DV and x1 x2 as IVs

my %result = pdl(@y)->anova( \@x1, \@x2 );
print "\$_\t\$result{\$_}\n" for (sort keys %result);``````

If you have a list of list, ie array of array refs, pdl will create a multi-dimensional data object.

``````    my @a = ( [1,2,3,4], [0,1,2,3], [4,5,6,7] );

my \$a = pdl @a;

print \$a . \$a->info;

# here's what you will get

[
[1 2 3 4]
[0 1 2 3]
[4 5 6 7]
]
PDL: Double D [4,3]``````

PDL::Stats puts observations in the first dimension and variables in the second dimension, ie pdl [obs, var]. In PDL::Stats the above example represents 4 observations on 3 variables.

``````    # you can do all kinds of fancy stuff on such a 2D pdl.

my %result = \$a->kmeans( {NCLUS=>2} );
print "\$_\t\$result{\$_}\n" for (sort keys %result);``````

Make sure the array of array refs is rectangular. If the array refs are of unequal sizes, pdl will pad it out with 0s to match the longest list.

## info

Tells you the data type (yes pdls are typed, but you shouldn't have to worry about it here*) and dimensionality of the pdl, as seen in the above example. I find it a big help for my sanity to keep track of the dimensionality of a pdl. As mentioned above, PDL::Stats uses 2D pdl with observation x variable dimensionality.

*pdl uses double precision by default. If you are working with things like epoch time, then you should probably use pdl(long, @epoch) to maintain the precision.

## list

Come back to the perl reality from the PDL wonder land. list turns a pdl data object into a regular perl list. Caveat: list produces a flat list. The dimensionality of the data object is lost.

## Signature

This is not a function, but a concept. You will see something like this frequently in the pod:

``````    stdv

Signature: (a(n); float+ [o]b())``````

The signature tells you what the function expects as input and what kind of output it produces. a(n) means it expects a 1D pdl with n elements; [o] is for output, b() means its a scalar. So stdv will take your 1D list and give back a scalar. float+ you can ignore; but if you insist, it means the output is at float or double precision. The name a or b or c is not important. What's important is the thing in the parenthesis.

``````    corr

Signature: (a(n); b(n); float+ [o]c())``````

Here the function corr takes two inputs, two 1D pdl with the same numbers of elements, and gives back a scalar.

``````    t_test

Signature: (a(n); b(m); float+ [o]t(); [o]d())``````

Here the function t_test can take two 1D pdls of unequal size (n==m is certainly fine), and give back two scalars, t-value and degrees of freedom. Yes we accommodate t-tests with unequal sample sizes.

``````    assign

Signature: (data(o,v); centroid(c,v); byte [o]cluster(o,c))``````

Here is one of the most complicated signatures in the package. This is a function from Kmeans. assign takes data of observasion x variable dimensions, and a centroid of cluster x variable dimensions, and returns an observation x cluster membership pdl (indicated by 1s and 0s).

Got the idea? Then we can see how PDL does its magic :)

Another concept. The first thing to know is that, threading is optional.

PDL threading means automatically repeating the operation on extra elements or dimensions fed to a function. For a function with a signature like this

``````    gsl_cdf_tdist_P

Signature: (double x(); double nu();  [o]out())``````

the signatures says that it takes two scalars as input, and returns a scalar as output. If you need to look up the p-values for a list of t's, with the same degrees of freedom 19,

``````    my @t = ( 1.65, 1.96, 2.56 );

my \$p = gsl_cdf_tdist_P( pdl(@t), 19 );

print \$p . "\n" . \$p->info;

# here's what you will get

[0.94231136 0.96758551 0.99042586]
PDL: Double D ``````

The same function is repeated on each element in the list you provided. If you had different degrees of freedoms for the t's,

``````    my @df = (199, 39, 19);

my \$p = gsl_cdf_tdist_P( pdl(@t), pdl(@df) );

print \$p . "\n" . \$p->info;

# here's what you will get

[0.94973979 0.97141553 0.99042586]
PDL: Double D ``````

The df's are automatically matched with the t's to give you the results.

An example of threading thru extra dimension(s):

``````    stdv

Signature: (a(n); float+ [o]b())``````

if the input is of 2D, say you want to compute the stdv for each of the 3 variables,

``````    my @a = ( [1,1,3,4], [0,1,2,3], [4,5,6,7] );

# pdl @a is pdl dim [4,3]

my \$sd = stdv( pdl @a );

print \$sd . "\n" . \$sd->info;

# this is what you will get

[ 1.2990381   1.118034   1.118034]
PDL: Double D ``````

Here the function was given an input with an extra dimension of size 3, so it repeates the stdv operation on the extra dimension 3 times, and gives back a 1D pdl of size 3.

Threading works for arbitrary number of dimensions, but it's best to refrain from higher dim pdls unless you have already decided to become a PDL wiz / witch.

Not all PDL::Stats methods thread. As a rule of thumb, if a function has a signature attached to it, it threads.

## perldl

Essentially a perl shell with "use PDL;" at start up. Comes with the PDL installation. Very handy to try out pdl operations, or just plain perl. print is shortened to p to avoid injury from exessive typing. my goes out of scope at the end of (multi)line input, so mostly you will have to drop the good practice of my here.