NAME
Math::LOESS - Perl wrapper of the Locally-Weighted Regression package
originally written by Cleveland, et al.
VERSION
version 0.001000
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.
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
COPYRIGHT AND LICENSE
This software is copyright (c) 2019-2023 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.