org.apache.commons.math.stat.inference

Class OneWayAnovaImpl

java.lang.Object

org.apache.commons.math.stat.inference.OneWayAnovaImpl

All Implemented Interfaces:

OneWayAnova

public class OneWayAnovaImpl
extends java.lang.Object
implements OneWayAnova

Implements one-way ANOVA statistics defined in the OneWayAnovaImpl interface.

Uses the commons-math F Distribution implementation to estimate exact p-values.

This implementation is based on a description at http://faculty.vassar.edu/lowry/ch13pt1.html

 Abbreviations: bg = between groups,
                wg = within groups,
                ss = sum squared deviations
 

Since:

1.2

Constructor Summary

Constructors

Constructor and Description
OneWayAnovaImpl() Default constructor.

Method Summary

Modifier and Type	Method and Description
double	anovaFValue(java.util.Collection<double[]> categoryData) Computes the ANOVA F-value for a collection of double[] arrays.
double	anovaPValue(java.util.Collection<double[]> categoryData) Computes the ANOVA P-value for a collection of double[] arrays.
boolean	anovaTest(java.util.Collection<double[]> categoryData, double alpha) Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.

Methods inherited from class java.lang.Object

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

Constructor Detail

OneWayAnovaImpl

public OneWayAnovaImpl()

Default constructor.

Method Detail

anovaFValue

public double anovaFValue(java.util.Collection<double[]> categoryData)
                   throws java.lang.IllegalArgumentException,
                          MathException

Computes the ANOVA F-value for a collection of double[] arrays.

Preconditions:

• The categoryData Collection must contain double[] arrays.
• There must be at least two double[] arrays in the categoryData collection and each of these arrays must contain at least two values.

This implementation computes the F statistic using the definitional formula

   F = msbg/mswg

where

  msbg = between group mean square
  mswg = within group mean square

are as defined here

Specified by:

anovaFValue in interface OneWayAnova

Parameters:

categoryData - Collection of double[] arrays each containing data for one category

Returns:

Fvalue

Throws:

java.lang.IllegalArgumentException - if the preconditions are not met

MathException - if the statistic can not be computed do to a convergence or other numerical error.

anovaPValue

public double anovaPValue(java.util.Collection<double[]> categoryData)
                   throws java.lang.IllegalArgumentException,
                          MathException

Computes the ANOVA P-value for a collection of double[] arrays.

Preconditions:

• The categoryData Collection must contain double[] arrays.
• There must be at least two double[] arrays in the categoryData collection and each of these arrays must contain at least two values.

This implementation uses the commons-math F Distribution implementation to estimate the exact p-value, using the formula

   p = 1 - cumulativeProbability(F)

where F is the F value and cumulativeProbability is the commons-math implementation of the F distribution.

Specified by:

anovaPValue in interface OneWayAnova

Parameters:

categoryData - Collection of double[] arrays each containing data for one category

Returns:

Pvalue

Throws:

java.lang.IllegalArgumentException - if the preconditions are not met

MathException - if the statistic can not be computed do to a convergence or other numerical error.

anovaTest

public boolean anovaTest(java.util.Collection<double[]> categoryData,
                         double alpha)
                  throws java.lang.IllegalArgumentException,
                         MathException

Performs an ANOVA test, evaluating the null hypothesis that there is no difference among the means of the data categories.

Preconditions:

• The categoryData Collection must contain double[] arrays.
• There must be at least two double[] arrays in the categoryData collection and each of these arrays must contain at least two values.
• alpha must be strictly greater than 0 and less than or equal to 0.5.

This implementation uses the commons-math F Distribution implementation to estimate the exact p-value, using the formula

   p = 1 - cumulativeProbability(F)

where F is the F value and cumulativeProbability is the commons-math implementation of the F distribution.

True is returned iff the estimated p-value is less than alpha.

Specified by:

anovaTest in interface OneWayAnova

Parameters:

categoryData - Collection of double[] arrays each containing data for one category

alpha - significance level of the test

Returns:

true if the null hypothesis can be rejected with confidence 1 - alpha

Throws:

java.lang.IllegalArgumentException - if the preconditions are not met

MathException - if the statistic can not be computed do to a convergence or other numerical error.