Package edu.cmu.tetrad.search

Class IndTestConditionalCorrelation

java.lang.Object

edu.cmu.tetrad.search.IndTestConditionalCorrelation

All Implemented Interfaces:

IndependenceTest

public final class IndTestConditionalCorrelation
extends Object
implements IndependenceTest

Checks conditional independence of variable in a continuous data set using a conditional correlation test for the nonlinear nonGaussian case.

Author:

Joseph Ramsey

Constructor Summary

Constructors

Constructor	Description
IndTestConditionalCorrelation(DataSet dataSet, double alpha)	Constructs a new Independence test which checks independence facts based on the correlation data implied by the given data set (must be continuous).

Method Summary

Modifier and Type	Method	Description
IndependenceResult	checkIndependence(Node x, Node y, List<Node> z)
boolean	determines(List<Node> z, Node x)	If isDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.
double	getAlpha()	Gets the getModel significance level.
ICovarianceMatrix	getCov()
List<Matrix>	getCovMatrices()
DataSet	getData()
List<DataSet>	getDataSets()
double	getPValue()
int	getSampleSize()
double	getScore()	A score that is higher with more likely models.
Node	getVariable(String name)
List<String>	getVariableNames()
List<Node>	getVariables()
double	getWeight()
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 at which independence judgments should be made.
void	setBasis(ConditionalCorrelationIndependence.Basis basis)
void	setKernel(ConditionalCorrelationIndependence.Kernel kernel)
void	setKernelMultiplier(double multiplier)
void	setKernelRegressionSampleSize(int i)
void	setNumFunctions(int numFunctions)
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

Constructor Details

IndTestConditionalCorrelation

public IndTestConditionalCorrelation(DataSet dataSet,
 double alpha)

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

Parameters:

dataSet - A data set containing only continuous columns.

alpha - The q level of the test.

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)

Specified by:

checkIndependence in interface IndependenceTest

Returns:

true if the given independence question is judged true, false if not. The independence question is of the form x _||_ y | z, z = <z1,...,zn>, where x, y, z1,...,zn are variables in the list returned by getVariableNames().

getPValue

public double getPValue()

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

getAlpha

public double getAlpha()

Gets the getModel significance level.

Specified by:

getAlpha in interface IndependenceTest

Returns:

the significance level of the independence test.

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.

getVariable

public Node getVariable(String name)

Specified by:

getVariable in interface IndependenceTest

Returns:

the variable with the given name.

getVariableNames

public List<String> getVariableNames()

Specified by:

getVariableNames in interface IndependenceTest

Returns:

the list of variable varNames.

determines

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

If isDeterminismAllowed(), deters to IndTestFisherZD; otherwise throws UnsupportedOperationException.

Specified by:

determines in interface IndependenceTest

Returns:

true if y is determined the variable in z.

Throws:

UnsupportedOperationException

getData

public DataSet getData()

Specified by:

getData in interface IndependenceTest

Returns:

the data set being analyzed.

getCov

public ICovarianceMatrix getCov()

Specified by:

getCov in interface IndependenceTest

getDataSets

public List<DataSet> getDataSets()

Specified by:

getDataSets in interface IndependenceTest

getSampleSize

public int getSampleSize()

Specified by:

getSampleSize in interface IndependenceTest

getCovMatrices

public List<Matrix> getCovMatrices()

Specified by:

getCovMatrices in interface IndependenceTest

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

setNumFunctions

public void setNumFunctions(int numFunctions)

getWeight

public double getWeight()

setKernelMultiplier

public void setKernelMultiplier(double multiplier)

setKernel

public void setKernel(ConditionalCorrelationIndependence.Kernel kernel)

setBasis

public void setBasis(ConditionalCorrelationIndependence.Basis basis)

setKernelRegressionSampleSize

public void setKernelRegressionSampleSize(int i)