com.imsl.stat
Class UserBasisRegression

java.lang.Object
  com.imsl.stat.UserBasisRegression

public class UserBasisRegression

extends Object

Generates summary statistics using user supplied functions in a nonlinear regression model

See Also:

Example

Constructor Summary

UserBasisRegression(RegressionBasis basis, int nBasis, boolean hasIntercept)
Constructs a UserBasisRegression object

Method Summary

ANOVA	getANOVA() Get an analysis of variance table and related statistics.
double[]	getCoefficients() Returns the regression coefficients.
void	update(double x, double y, double w) Adds a new observation and associated weight to the RegressionBasis object.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

UserBasisRegression

public UserBasisRegression(RegressionBasis basis,
                           int nBasis,
                           boolean hasIntercept)

Constructs a UserBasisRegression object

Parameters:

basis - a RegressionBasis basis function supplied by the user

nBasis - an int which specifies the number of basis functions

hasIntercept - a boolean which specifies whether or not the model has an intercept

Method Detail

getANOVA

public ANOVA getANOVA()

Get an analysis of variance table and related statistics.

Returns:

an ANOVA table and related statistics

getCoefficients

public double[] getCoefficients()

Returns the regression coefficients.

Returns:

A double array containing the regression coefficients. If hasIntercept is false its length is equal to the number of variables. If hasIntercept is true then its length is the number of variables plus one and the 0-th entry is the value of the intercept.

Throws:

SingularMatrixException - is thrown when the regression matrix is singular.

update

public void update(double x,
                   double y,
                   double w)

Adds a new observation and associated weight to the RegressionBasis object.

Parameters:

x - a double containing the independent (explanatory) variable.

y - a double containing the dependent (response) variable.

w - a double representing the weight