Package edu.cmu.tetrad.search.test

Class IndTestChiSquare

java.lang.Object

edu.cmu.tetrad.search.test.IndTestChiSquare

All Implemented Interfaces:

IndependenceTest

public final class IndTestChiSquare
extends Object
implements IndependenceTest

Checks the conditional independence X _||_ Y | S, where S is a set of discrete variable, and X and Y are discrete variable not in S, by applying a conditional Chi Square test. A description of such a test is given in Fienberg, "The Analysis of Cross-Classified Categorical Data," 2nd edition. The formulas for the degrees of freedom used in this test are equivalent to the formulation on page 142 of Fienberg.

Author:

josephramsey

See Also:

• ChiSquareTest

Constructor Summary

Constructors

Constructor	Description
IndTestChiSquare(DataSet dataSet, double alpha)	Constructs a new independence checker to check conditional independence facts for discrete data using a g square test.

Method Summary

Modifier and Type	Method	Description
IndependenceResult	checkIndependence(Node x, Node y, Set<Node> _z)	Determines whether variable x is independent of variable y given a list of conditioning varNames z.
boolean	determines(List<Node> z, Node x)	Returns True if the variables z determining the variable z.
double	getAlpha()	Returns the alpha significance level of the test.
double	getChiSquare()	Returns the chi Square value.
DataSet	getData()	Returns the data being analyzed.
int	getDf()	Returns the degrees of freedom associated with the most recent call of isIndependent.
List<Node>	getVariables()	Returns the list of variables over which this independence checker is capable of determining independence relations-- that is, all the variables in the given graph or the given data set.
IndependenceTest	indTestSubset(List<Node> nodes)	Creates a new IndTestChiSquare for a subset of the nodes.
boolean	isVerbose()	Returns true if verbose output should be printed.
void	setAlpha(double alpha)	Sets the significance level at which independence judgments should be made.
void	setVerbose(boolean verbose)	Sets whether verbose output should be printed.
String	toString()	Returns a string representation of this test.

Methods inherited from class java.lang.Object

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

Methods inherited from interface edu.cmu.tetrad.search.IndependenceTest

checkIndependence, determines, getCov, getDataSets, getSampleSize, getVariable, getVariableNames

Constructor Details

IndTestChiSquare

public IndTestChiSquare(DataSet dataSet,
 double alpha)

Constructs a new independence checker to check conditional independence facts for discrete data using a g square test.

Parameters:

dataSet - the discrete data set.

alpha - the significance level of the tests.

Method Details

indTestSubset

public IndependenceTest indTestSubset(List<Node> nodes)

Creates a new IndTestChiSquare for a subset of the nodes.

Specified by:

indTestSubset in interface IndependenceTest

Parameters:

nodes - This list of nodes.

getChiSquare

public double getChiSquare()

Returns the chi Square value.

Returns:

This value.

getDf

public int getDf()

Returns the degrees of freedom associated with the most recent call of isIndependent.

Returns:

These degrees.

checkIndependence

public IndependenceResult checkIndependence(Node x,
 Node y,
 Set<Node> _z)

Determines whether variable x is independent of variable y given a list of conditioning varNames z.

Specified by:

checkIndependence in interface IndependenceTest

Returns:

True iff x _||_ y | z.

See Also:

• IndependenceResult

determines

public boolean determines(List<Node> z,
 Node x)

Returns True if the variables z determining the variable z.

Parameters:

z - The list of variables z1,...,zn with respect to which we want to know whether z determines x oir z.

x - The one variable whose determination by z we want to know.

Returns:

true if it is estimated that z determines x or z determines y.

getAlpha

public double getAlpha()

Returns the alpha significance level of the test.

Specified by:

getAlpha in interface IndependenceTest

Returns:

This level.

setAlpha

public void setAlpha(double alpha)

Sets the significance level at which independence judgments should be made. Affects the cutoff for partial correlations to be considered statistically equal to zero.

Specified by:

setAlpha in interface IndependenceTest

Parameters:

alpha - the new significance level.

getVariables

public List<Node> getVariables()

Returns the list of variables over which this independence checker is capable of determining independence relations-- that is, all the variables in the given graph or the given data set.

Specified by:

getVariables in interface IndependenceTest

Returns:

This list.

toString

public String toString()

Returns a string representation of this test.

Specified by:

toString in interface IndependenceTest

Overrides:

toString in class Object

Returns:

This string.

getData

public DataSet getData()

Returns the data being analyzed.

Specified by:

getData in interface IndependenceTest

Returns:

This data.

See Also:

• DataSet
• ICovarianceMatrix
• DataModel

isVerbose

public boolean isVerbose()

Returns true if verbose output should be printed.

Specified by:

isVerbose in interface IndependenceTest

Returns:

This.

setVerbose

public void setVerbose(boolean verbose)

Sets whether verbose output should be printed.

Specified by:

setVerbose in interface IndependenceTest

Parameters:

verbose - True, if so.