Distr(3pm) User Contributed Perl Documentation Distr(3pm)

# 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 (\$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.

## 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:

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