org.apache.commons.math.stat.regression
Class AbstractMultipleLinearRegression

java.lang.Object
  org.apache.commons.math.stat.regression.AbstractMultipleLinearRegression

All Implemented Interfaces:

MultipleLinearRegression

Direct Known Subclasses:

GLSMultipleLinearRegression, OLSMultipleLinearRegression

public abstract class AbstractMultipleLinearRegression
extends Object
implements MultipleLinearRegression

Abstract base class for implementations of MultipleLinearRegression.

Since:

2.0

Constructor Summary

AbstractMultipleLinearRegression()

Method Summary

double	estimateErrorVariance() Estimates the variance of the error.
double	estimateRegressandVariance() Returns the variance of the regressand, ie Var(y).
double[]	estimateRegressionParameters() Estimates the regression parameters b.
double[]	estimateRegressionParametersStandardErrors() Returns the standard errors of the regression parameters.
double[][]	estimateRegressionParametersVariance() Estimates the variance of the regression parameters, ie Var(b).
double	estimateRegressionStandardError() Estimates the standard error of the regression.
double[]	estimateResiduals() Estimates the residuals, ie u = y - X*b.
boolean	isNoIntercept()
void	newSampleData(double[] data, int nobs, int nvars) Loads model x and y sample data from a flat input array, overriding any previous sample.
void	setNoIntercept(boolean noIntercept)

Methods inherited from class java.lang.Object

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

Constructor Detail

AbstractMultipleLinearRegression

public AbstractMultipleLinearRegression()

Method Detail

isNoIntercept

public boolean isNoIntercept()

Returns:

true if the model has no intercept term; false otherwise

Since:

2.2

setNoIntercept

public void setNoIntercept(boolean noIntercept)

Parameters:

noIntercept - true means the model is to be estimated without an intercept term

Since:

2.2

newSampleData

public void newSampleData(double[] data,
                          int nobs,
                          int nvars)

Loads model x and y sample data from a flat input array, overriding any previous sample.

Assumes that rows are concatenated with y values first in each row. For example, an input data array containing the sequence of values (1, 2, 3, 4, 5, 6, 7, 8, 9) with nobs = 3 and nvars = 2 creates a regression dataset with two independent variables, as below:

   y   x[0]  x[1]
   --------------
   1     2     3
   4     5     6
   7     8     9
 

Note that there is no need to add an initial unitary column (column of 1's) when specifying a model including an intercept term. If isNoIntercept() is true, the X matrix will be created without an initial column of "1"s; otherwise this column will be added.

Throws IllegalArgumentException if any of the following preconditions fail:

• data cannot be null
• data.length = nobs * (nvars + 1)
• nobs > nvars

Parameters:

data - input data array

nobs - number of observations (rows)

nvars - number of independent variables (columns, not counting y)

Throws:

IllegalArgumentException - if the preconditions are not met

estimateRegressionParameters

public double[] estimateRegressionParameters()

Estimates the regression parameters b.

Specified by:

estimateRegressionParameters in interface MultipleLinearRegression

Returns:

The [k,1] array representing b

estimateResiduals

public double[] estimateResiduals()

Estimates the residuals, ie u = y - X*b.

Specified by:

estimateResiduals in interface MultipleLinearRegression

Returns:

The [n,1] array representing the residuals

estimateRegressionParametersVariance

public double[][] estimateRegressionParametersVariance()

Estimates the variance of the regression parameters, ie Var(b).

Specified by:

estimateRegressionParametersVariance in interface MultipleLinearRegression

Returns:

The [k,k] array representing the variance of b

estimateRegressionParametersStandardErrors

public double[] estimateRegressionParametersStandardErrors()

Returns the standard errors of the regression parameters.

Specified by:

estimateRegressionParametersStandardErrors in interface MultipleLinearRegression

Returns:

standard errors of estimated regression parameters

estimateRegressandVariance

public double estimateRegressandVariance()

Returns the variance of the regressand, ie Var(y).

Specified by:

estimateRegressandVariance in interface MultipleLinearRegression

Returns:

The double representing the variance of y

estimateErrorVariance

public double estimateErrorVariance()

Estimates the variance of the error.

Returns:

estimate of the error variance

Since:

2.2

estimateRegressionStandardError

public double estimateRegressionStandardError()

Estimates the standard error of the regression.

Returns:

regression standard error

Since:

2.2