LOESS - Locally Weighted Scatterplot Smoothing (Local regression) More...

Inheritance diagram for MathPHP\Statistics\Regression\LOESS:

Collaboration diagram for MathPHP\Statistics\Regression\LOESS:

Public Member Functions
	__construct ($points, float $α, int $λ)

	evaluate (float $x)
	Evaluate for x Use the smoothness parameter α to determine the subset of data to consider for local regression. More...

Public Member Functions inherited from MathPHP\Statistics\Regression\Regression
	__construct (array $points)
	Constructor - Prepares the data arrays for regression analysis. More...

	evaluate (float $x)
	Evaluate the regression equation at x Uses the instance model's evaluateModel method. More...

	getPoints ()
	Get points. More...

	getXs ()
	Get Xs (x values of each point) More...

	getYs ()
	Get Ys (y values of each point) More...

	getSampleSize ()
	Get sample size (number of points) More...

	yHat ()
	Ŷ (yhat) A list of the predicted values of Y given the regression. More...

Protected Attributes
	$α

	$λ

	$number_of_points

Protected Attributes inherited from MathPHP\Statistics\Regression\Regression
	$points

	$xs

	$ys

	$n

Detailed Description

LOESS - Locally Weighted Scatterplot Smoothing (Local regression)

A non-parametric method for fitting a smooth curve between two variables. https://en.wikipedia.org/wiki/Local_regression

Constructor & Destructor Documentation

MathPHP\Statistics\Regression\LOESS::__construct	(		$points,
		float	$α,
		int	$λ
	)

Parameters

array	$points	[ [x, y], [x, y], ... ]
float	$α	Smoothness parameter (bandwidth) Determines how much of the data is used to fit each local polynomial ((λ + 1) / n, 1]
int	$λ	Order of the polynomial to fit

Exceptions

Exception

MathPHP\Statistics\Regression\LOESS::evaluate ( float $x )

Evaluate for x Use the smoothness parameter α to determine the subset of data to consider for local regression.

Perform a weighted least squares regression and evaluate x.

Parameters

float $x

Exceptions

Exception

The documentation for this class was generated from the following file: