/ 
Math-LOESS-0.0000_02
River stage zero No dependents
/ Math::LOESS

NAME

Math::LOESS - Perl wrapper of the Locally-Weighted Regression package originally written by Cleveland, et al.

VERSION

version 0.0000_02

SYNOPSIS

use Math::LOESS;

my $loess = Math::LOESS->new(x => $x, y => $y);

$loess->fit();
my $fitted_values = $loess->outputs->fitted_values;

print $loess->summary();

my $prediction = $loess->predict($new_data, 1);
my $confidence_intervals = $prediction->confidence(0.05);
print $confidence_internals->{fit};
print $confidence_internals->{upper};
print $confidence_internals->{lower};

CONSTRUCTION

new(Piddle1D :$x, Piddle1D :$y, Piddle1D :$weights=undef,
    Num :$span=0.75)

Arguments:

  • $x

    A PDL piddle for x data.

  • $y

    A PDL piddle for y data.

  • $weights

    Optional weights.

  • $span

    The parameter controls the degree of smoothing. Default is 0.75.

    For span < 1, the neighbourhood used for the fit includes proportion span of the points, and these have tricubic weighting (proportional to (1 - (dist/maxdist)^3)^3). For span > 1, all points are used, with the "maximum distance" assumed to be span^(1/p) times the actual maximum distance for p explanatory variables.

    When provided as a construction parameter, it is like a shortcut of,

    my $loess = Math::LOESS->new(...); 
$loess->model->span($span);

NAME

Math::LOESS - Perl wrapper of the Locally-Weighted Regression package originally written by Cleveland, et al.

ATTRIBUTES

model

Get an Math::LOESS::Model object.

outputs

Get an Math::LOESS::Outputs object.

x

Get input x data as a piddle.

y

Get input y data as a piddle.

weights

Get input weights data as a piddle.

activated

Returns a true value if the object's fit() method has been called.

METHODS

fit

fit()

predict

predict(Piddle1D $newdata, Bool $stderr=false)

Returns a Math:LOESS::Prediction object.

summary

summary()

Returns a summary string.

SEE ALSO

https://en.wikipedia.org/wiki/Local_regression

PDL

AUTHOR

Stephan Loyd <sloyd@cpan.org>

COPYRIGHT AND LICENSE

This software is copyright (c) 2019 by Stephan Loyd.

This is free software; you can redistribute it and/or modify it under the same terms as the Perl 5 programming language system itself.