Package edu.cmu.tetrad.search

Class IndTestCramerT

java.lang.Object

edu.cmu.tetrad.search.IndTestCramerT

All Implemented Interfaces:

IndependenceTest

public final class IndTestCramerT
extends Object
implements IndependenceTest

Checks conditional independence for continuous variables using Cramer's T-test formula (Cramer, Mathematical Methods of Statistics (1951), page 413).

Author:

Joseph Ramsey

Constructor Summary

Constructors

Constructor	Description
IndTestCramerT(CorrelationMatrix covMatrix, double alpha)	Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.
IndTestCramerT(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous).
IndTestCramerT(ICovarianceMatrix covMatrix, double alpha)	Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.

Method Summary

Modifier and Type	Method	Description
IndependenceResult	checkIndependence(Node x, Node y, List<Node> z)	Determines whether variable x is independent of variable y given a list of conditioning variables z.
boolean	determines(List<Node> z, Node x)
double	getAlpha()
DataSet	getData()
double	getPValue()
double	getScore()	A score that is higher with more likely models.
List<Node>	getVariables()
IndependenceTest	indTestSubset(List<Node> vars)	Creates a new IndTestCramerT instance for a subset of the variables.
boolean	isVerbose()
void	setAlpha(double alpha)	Sets the significance level for future tests.
void	setVerbose(boolean verbose)
String	toString()

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, getCov, getDataSets, getSampleSize, getVariable, getVariableNames

Constructor Details

IndTestCramerT

public IndTestCramerT(DataSet dataSet,
 double alpha)

Constructs a new Independence test which checks independence facts based on the correlation matrix implied by the given data set (must be continuous). The given significance level is used.

Parameters:

dataSet - A data set with all continuous columns.

alpha - the alpha level of the test.

IndTestCramerT

public IndTestCramerT(CorrelationMatrix covMatrix,
 double alpha)

Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.

IndTestCramerT

public IndTestCramerT(ICovarianceMatrix covMatrix,
 double alpha)

Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.

Method Details

indTestSubset

public IndependenceTest indTestSubset(List<Node> vars)

Creates a new IndTestCramerT instance for a subset of the variables.

Specified by:

indTestSubset in interface IndependenceTest

Returns:

an Independence test for a subset of the variables.

checkIndependence

public IndependenceResult checkIndependence(Node x,
 Node y,
 List<Node> z)

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

Specified by:

checkIndependence in interface IndependenceTest

Parameters:

x - the one variable being compared.

y - the second variable being compared.

z - the list of conditioning variables.

Returns:

true iff x _||_ y | z.

Throws:

RuntimeException - if a matrix singularity is encountered.

See Also:

• IndependenceResult

getPValue

public double getPValue()

Returns:

the probability associated with the most recently computed independence test.

setAlpha

public void setAlpha(double alpha)

Sets the significance level for future tests.

Specified by:

setAlpha in interface IndependenceTest

getAlpha

public double getAlpha()

Specified by:

getAlpha in interface IndependenceTest

Returns:

the getModel significance level.

getVariables

public List<Node> getVariables()

Specified by:

getVariables in interface IndependenceTest

Returns:

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

determines

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

Specified by:

determines in interface IndependenceTest

Returns:

the variable with the given name, or null if there is no such variable.

Throws:

UnsupportedOperationException

getData

public DataSet getData()

Specified by:

getData in interface IndependenceTest

Returns:

The data model for the independence test.

getScore

public double getScore()

Description copied from interface: IndependenceTest

A score that is higher with more likely models.

Specified by:

getScore in interface IndependenceTest

toString

public String toString()

Specified by:

toString in interface IndependenceTest

Overrides:

toString in class Object

Returns:

a string representation of this test.

isVerbose

public boolean isVerbose()

Specified by:

isVerbose in interface IndependenceTest

setVerbose

public void setVerbose(boolean verbose)

Specified by:

setVerbose in interface IndependenceTest