NAME

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

VERSION

version 0.0001

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|Piddle2D) :$x, Piddle1D :$y, Piddle1D :$weights=undef,
        Num :$span=0.75, Str :$family='gaussian')

Arguments:

  • $x

    A ($n, $p) piddle for x data, where $p is number of predictors. It's possible to have at most 8 predictors.

  • $y

    A ($n, 1) piddle for y data.

  • $weights

    Optional ($n, 1) piddle for weights to be given to individual observations. By default, an unweighted fit is carried out (all the weights are one).

  • $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 for,

        $loess->model->span($span);
  • $family

    If "gaussian" fitting is by least-squares, and if "symmetric" a re-descending M estimator is used with Tukey's biweight function.

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

        $loess->model->family($family);

Bad values in $x, $y, $weights are removed.

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|Piddle2D) $newdata, Bool $stderr=false)

Returns a Math::LOESS::Prediction object.

Bad values in $newdata are removed.

summary

    summary()

Returns a summary string. For example,

    print $loess->summary();

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.