Package org.apache.commons.math4.legacy.stat.regression

Interface UpdatingMultipleLinearRegression

All Known Implementing Classes:

MillerUpdatingRegression, SimpleRegression

public interface UpdatingMultipleLinearRegression

An interface for regression models allowing for dynamic updating of the data. That is, the entire data set need not be loaded into memory. As observations become available, they can be added to the regression model and an updated estimate regression statistics can be calculated.

Since:

3.0

Method Summary

Modifier and Type	Method	Description
void	addObservation(double[] x, double y)	Adds one observation to the regression model.
void	addObservations(double[][] x, double[] y)	Adds a series of observations to the regression model.
void	clear()	Clears internal buffers and resets the regression model.
long	getN()	Returns the number of observations added to the regression model.
boolean	hasIntercept()	Returns true if a constant has been included false otherwise.
RegressionResults	regress()	Performs a regression on data present in buffers and outputs a RegressionResults object.
RegressionResults	regress(int[] variablesToInclude)	Performs a regression on data present in buffers including only regressors.

Method Detail

hasIntercept

boolean hasIntercept()

Returns true if a constant has been included false otherwise.

Returns:

true if constant exists, false otherwise

getN

long getN()

Returns the number of observations added to the regression model.

Returns:

Number of observations

addObservation

void addObservation(double[] x,
                    double y)
             throws ModelSpecificationException

Adds one observation to the regression model.

Parameters:

x - the independent variables which form the design matrix

y - the dependent or response variable

Throws:

ModelSpecificationException - if the length of x does not equal the number of independent variables in the model

addObservations

void addObservations(double[][] x,
                     double[] y)
              throws ModelSpecificationException

Adds a series of observations to the regression model. The lengths of x and y must be the same and x must be rectangular.

Parameters:

x - a series of observations on the independent variables

y - a series of observations on the dependent variable The length of x and y must be the same

Throws:

ModelSpecificationException - if x is not rectangular, does not match the length of y or does not contain sufficient data to estimate the model

clear

void clear()

Clears internal buffers and resets the regression model. This means all data and derived values are initialized

regress

RegressionResults regress()
                   throws ModelSpecificationException,
                          NoDataException

Performs a regression on data present in buffers and outputs a RegressionResults object.

Returns:

RegressionResults acts as a container of regression output

Throws:

ModelSpecificationException - if the model is not correctly specified

NoDataException - if there is not sufficient data in the model to estimate the regression parameters

regress

RegressionResults regress(int[] variablesToInclude)
                   throws ModelSpecificationException,
                          MathIllegalArgumentException

Performs a regression on data present in buffers including only regressors. indexed in variablesToInclude and outputs a RegressionResults object

Parameters:

variablesToInclude - an array of indices of regressors to include

Returns:

RegressionResults acts as a container of regression output

Throws:

ModelSpecificationException - if the model is not correctly specified

MathIllegalArgumentException - if the variablesToInclude array is null or zero length