``````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();

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

PDL

AUTHOR

Stephan Loyd <sloyd@cpan.org>