org.apache.commons.math3.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

Methods
Modifier and Type Method and 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 indexed in variablesToInclude and outputs a RegressionResults object
• 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

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

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