Package edu.cmu.tetrad.search.test

Class IndTestTrekSep

java.lang.Object

edu.cmu.tetrad.search.test.IndTestTrekSep

All Implemented Interfaces:

IndependenceTest

public final class IndTestTrekSep
extends Object
implements IndependenceTest

Checks d-separations in a structural model using t-separations over indicators.

Version:

$Id: $Id

Author:

Adam Brodie

Constructor Summary

Constructors

Constructor	Description
IndTestTrekSep(ICovarianceMatrix covMatrix, double alpha, List<List<Node>> clustering, List<Node> latents)	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, Set<Node> z)	Determines independence between variables x and y, given the set of variables z.
boolean	determines(List<Node> z, Node x)	Determines the independence between a set of variables z and a variable x.
double	getAlpha()	Gets the model significance level.
ICovarianceMatrix	getCov()	Returns the covariance matrix.
DataSet	getData()	Gets the data set used for the independence test.
List<DataSet>	getDataSets()	Returns the data sets used for the independence test.
int	getSampleSize()	Returns the sample size used in the covariance matrix.
Node	getVariable(String name)	Gets the variable with the given name.
List<Node>	getVariables()	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.
IndependenceTest	indTestSubset(List<Node> vars)	Determines independence between variables in a given subset.
boolean	isVerbose()	Checks whether verbose output is enabled.
void	setAlpha(double alpha)	Sets the significance level for the independence test.
void	setVariables(List<Node> variables)	Sets the varialbe to this list (of the same length).
void	setVerbose(boolean verbose)	Sets the verbose output flag.
String	toString()	Returns a string representation of this test.

Methods inherited from class java.lang.Object

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

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

checkIndependence, determines, getVariableNames

Constructor Details

IndTestTrekSep

public IndTestTrekSep(ICovarianceMatrix covMatrix,
 double alpha,
 List<List<Node>> clustering,
 List<Node> latents)

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

Parameters:

covMatrix - The covariance over the measures.

alpha - The significance level.

clustering - The clustering of the measured variables. In each cluster, all measured variable in the cluster are explained by a single latent.

latents - The list of latent variables for the clusters, in order.

Method Details

indTestSubset

public IndependenceTest indTestSubset(List<Node> vars)

Determines independence between variables in a given subset.

Specified by:

indTestSubset in interface IndependenceTest

Parameters:

vars - The sublist of variables to test for independence.

Returns:

An IndependenceTest object representing the result of the independence test.

Throws:

IllegalArgumentException - If the subset of variables is empty or contains variables that are not part of the original variables.

checkIndependence

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

Determines independence between variables x and y, given the set of variables z.

Specified by:

checkIndependence in interface IndependenceTest

Parameters:

x - The first variable.

y - The second variable.

z - The set of variables.

Returns:

An IndependenceResult object representing the result of the independence test.

See Also:

• IndependenceResult

getAlpha

public double getAlpha()

Gets the model significance level.

Specified by:

getAlpha in interface IndependenceTest

Returns:

This alpha.

setAlpha

public void setAlpha(double alpha)

Sets the significance level for the independence test.

Specified by:

setAlpha in interface IndependenceTest

Parameters:

alpha - The significance level. Must be between 0.0 and 1.0 (inclusive).

Throws:

IllegalArgumentException - If the significance level is out of range.

getVariables

public List<Node> getVariables()

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.

Specified by:

getVariables in interface IndependenceTest

Returns:

This list.

setVariables

public void setVariables(List<Node> variables)

Sets the varialbe to this list (of the same length). Useful is multiple test are used that need the same object-identical lists of variables.

Parameters:

variables - This list.

getVariable

public Node getVariable(String name)

Gets the variable with the given name.

Specified by:

getVariable in interface IndependenceTest

Parameters:

name - The name of the variable to get.

Returns:

The Node object representing the variable.

determines

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

Determines the independence between a set of variables z and a variable x.

Parameters:

z - The set of variables to determine independence with x.

x - The variable to determine independence with z.

Returns:

true if the variable x is conditionally independent from the set of variables z, false otherwise.

Throws:

UnsupportedOperationException - if the covariance matrix is singular or not invertible.

getData

public DataSet getData()

Gets the data set used for the independence test.

Specified by:

getData in interface IndependenceTest

Returns:

The data set used for the independence test.

See Also:

• DataSet
• ICovarianceMatrix
• DataModel

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.

getCov

public ICovarianceMatrix getCov()

Returns the covariance matrix.

Specified by:

getCov in interface IndependenceTest

Returns:

This matrix.

getDataSets

public List<DataSet> getDataSets()

Returns the data sets used for the independence test.

Specified by:

getDataSets in interface IndependenceTest

Returns:

The list of data sets used for the independence test.

getSampleSize

public int getSampleSize()

Returns the sample size used in the covariance matrix.

Specified by:

getSampleSize in interface IndependenceTest

Returns:

The sample size.

isVerbose

public boolean isVerbose()

Checks whether verbose output is enabled.

Specified by:

isVerbose in interface IndependenceTest

Returns:

true if verbose output is enabled, false otherwise.

setVerbose

public void setVerbose(boolean verbose)

Sets the verbose output flag.

Specified by:

setVerbose in interface IndependenceTest

Parameters:

verbose - True, if verbose output is enabled. False otherwise.