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PDL-GSL-2.101
River stage one • 1 direct dependent • 3 total dependents
/ PDL::Stats::Distr

NAME

PDL::Stats::Distr -- parameter estimations and probability density functions for distributions.

DESCRIPTION

Parameter estimate is maximum likelihood estimate when there is closed form estimate, otherwise it is method of moments estimate.

SYNOPSIS


            

              
              
use PDL::LiteF;
use PDL::Stats::Distr;
# do a frequency (probability) plot with fitted normal curve
my $data = grandom(100)->abs;
my ($xvals, $hist) = $data->hist;
  # turn frequency into probability
$hist /= $data->nelem;
  # get maximum likelihood estimates of normal curve parameters
my ($m, $v) = $data->mle_gaussian();
  # fitted normal curve probabilities
my $p = $xvals->pdf_gaussian($m, $v);
use PDL::Graphics::PGPLOT::Window;
my $win = pgwin( Dev=>"/xs" );
$win->bin( $hist );
$win->hold;
$win->line( $p, {COLOR=>2} );
$win->close;

Or, play with different distributions with plot_distr :)


            

              
              
$data->plot_distr( 'gaussian', 'lognormal' );

FUNCTIONS

mme_beta


            

              
              
Signature: (a(n); float+ [o]alpha(); float+ [o]beta())

            

          

            

              
              
my ($a, $b) = $data->mme_beta();

beta distribution. pdf: f(x; a,b) = 1/B(a,b) x^(a-1) (1-x)^(b-1)

mme_beta processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_beta


            

              
              
Signature: (x(); a(); b(); float+ [o]p())

probability density function for beta distribution. x defined on [0,1].

pdf_beta processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_binomial


            

              
              
Signature: (a(n); int [o]n_(); float+ [o]p())

            

          

            

              
              
my ($n, $p) = $data->mme_binomial;

binomial distribution. pmf: f(k; n,p) = (n k) p^k (1-p)^(n-k) for k = 0,1,2..n

mme_binomial processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_binomial


            

              
              
Signature: (ushort x(); ushort n(); p(); float+ [o]out())

probability mass function for binomial distribution.

pmf_binomial processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_exp


            

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

            

          

            

              
              
my $lamda = $data->mle_exp;

exponential distribution. mle same as method of moments estimate.

mle_exp processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_exp


            

              
              
Signature: (x(); l(); float+ [o]p())

probability density function for exponential distribution.

pdf_exp processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_gamma


            

              
              
Signature: (a(n); float+ [o]shape(); float+ [o]scale())

            

          

            

              
              
my ($shape, $scale) = $data->mme_gamma();

two-parameter gamma distribution

mme_gamma processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_gamma


            

              
              
Signature: (x(); a(); t(); float+ [o]p())

probability density function for two-parameter gamma distribution.

pdf_gamma processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_gaussian


            

              
              
Signature: (a(n); float+ [o]m(); float+ [o]v())

            

          

            

              
              
my ($m, $v) = $data->mle_gaussian();

gaussian aka normal distribution. same results as $data->average and $data->var. mle same as method of moments estimate.

mle_gaussian processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_gaussian


            

              
              
Signature: (x(); m(); v(); float+ [o]p())

probability density function for gaussian distribution.

pdf_gaussian processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_geo


            

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

geometric distribution. mle same as method of moments estimate.

mle_geo processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_geo


            

              
              
Signature: (ushort x(); p(); float+ [o]out())

probability mass function for geometric distribution. x >= 0.

pmf_geo processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_geosh


            

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

shifted geometric distribution. mle same as method of moments estimate.

mle_geosh processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_geosh


            

              
              
Signature: (ushort x(); p(); float+ [o]out())

probability mass function for shifted geometric distribution. x >= 1.

pmf_geosh processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_lognormal


            

              
              
Signature: (a(n); float+ [o]m(); float+ [o]v())

            

          

            

              
              
my ($m, $v) = $data->mle_lognormal();

lognormal distribution. maximum likelihood estimation.

mle_lognormal processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_lognormal


            

              
              
Signature: (a(n); float+ [o]m(); float+ [o]v())

            

          

            

              
              
my ($m, $v) = $data->mme_lognormal();

lognormal distribution. method of moments estimation.

mme_lognormal processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_lognormal


            

              
              
Signature: (x(); m(); v(); float+ [o]p())

probability density function for lognormal distribution. x > 0. v > 0.

pdf_lognormal processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_nbd


            

              
              
Signature: (a(n); float+ [o]r(); float+ [o]p())

            

          

            

              
              
my ($r, $p) = $data->mme_nbd();

negative binomial distribution. pmf: f(x; r,p) = (x+r-1 r-1) p^r (1-p)^x for x=0,1,2...

mme_nbd processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_nbd


            

              
              
Signature: (ushort x(); r(); p(); float+ [o]out())

probability mass function for negative binomial distribution.

pmf_nbd processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mme_pareto


            

              
              
Signature: (a(n); float+ [o]k(); float+ [o]xm())

            

          

            

              
              
my ($k, $xm) = $data->mme_pareto();

pareto distribution. pdf: f(x; k,xm) = k xm^k / x^(k+1) for x >= xm > 0.

mme_pareto processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pdf_pareto


            

              
              
Signature: (x(); k(); xm(); float+ [o]p())

probability density function for pareto distribution. x >= xm > 0.

pdf_pareto processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

mle_poisson


            

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

            

          

            

              
              
my $lamda = $data->mle_poisson();

poisson distribution. pmf: f(x;l) = e^(-l) * l^x / x!

mle_poisson processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_poisson


            

              
              
Signature: (x(); l(); float+ [o]p())

Probability mass function for poisson distribution. Uses Stirling's formula for x > 85.

pmf_poisson processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_poisson_stirling


            

              
              
Signature: (x(); l(); [o]p())

Probability mass function for poisson distribution. Uses Stirling's formula for all values of the input. See http://en.wikipedia.org/wiki/Stirling's_approximation for more info.

pmf_poisson_stirling processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

pmf_poisson_factorial


            

              
              
Signature: (ushort x(); l(); float+ [o]p())

Probability mass function for poisson distribution. Input is limited to x < 170 to avoid gsl_sf_fact() overflow.

pmf_poisson_factorial processes bad values. It will set the bad-value flag of all output ndarrays if the flag is set for any of the input ndarrays.

plot_distr

Plots data distribution. When given specific distribution(s) to fit, returns % ref to sum log likelihood and parameter values under fitted distribution(s). See FUNCTIONS above for available distributions.

Default options (case insensitive):


            

              
              
MAXBN => 20,
  # see PDL::Graphics::PGPLOT::Window for next options
WIN   => undef,   # pgwin object. not closed here if passed
                  # allows comparing multiple distr in same plot
                  # set env before passing WIN
DEV   => '/xs' ,  # open and close dev for plotting if no WIN
                  # defaults to '/png' in Windows
COLOR => 1,       # color for data distr

Usage:


            

              
              
  # yes it threads :)
my $data = grandom( 500, 3 )->abs;
  # ll on plot is sum across 3 data curves
my ($ll, $pars)
  = $data->plot_distr( 'gaussian', 'lognormal', {DEV=>'/png'} );
  # pars are from normalized data (ie data / bin_size)
print "$_\t@{$pars->{$_}}\n" for (sort keys %$pars);
print "$_\t$ll->{$_}\n" for (sort keys %$ll);

DEPENDENCIES

GSL - GNU Scientific Library

SEE ALSO

PDL::Graphics::PGPLOT

PDL::GSL::CDF

AUTHOR

Copyright (C) 2009 Maggie J. Xiong <maggiexyz users.sourceforge.net>, David Mertens

All rights reserved. There is no warranty. You are allowed to redistribute this software / documentation as described in the file COPYING in the PDL distribution.