NAME
    Math::LOESS - Perl wrapper of the Locally-Weighted Regression package
    originally written by Cleveland, et al.
VERSION
    version 0.0000_04
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);
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.
 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.