Package edu.cmu.tetrad.search.test

Class IndTestFisherZFisherPValue

java.lang.Object

edu.cmu.tetrad.search.test.IndTestFisherZFisherPValue

All Implemented Interfaces:

IndependenceTest

public final class IndTestFisherZFisherPValue
extends Object
implements IndependenceTest

Calculates independence from multiple datasets from using the Fisher method of pooling independence results. See this paper for details:

Tillman, R. E., invalid input: '&' Eberhardt, F. (2014). Learning causal structure from multiple datasets with similar variable sets. Behaviormetrika, 41(1), 41-64.

Author:

robertillman, josephramsey

Constructor Summary

Constructors

Constructor	Description
IndTestFisherZFisherPValue(List<DataSet> dataSets, double alpha)	Constructor.

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)	Returns true if y is determined the variable in z.
double	getAlpha()	Gets the getModel significance level.
ICovarianceMatrix	getCov()	Returns the covariance matrix of the concatenated data.
DataSet	getData()	Returns the concatenated data.
double	getPValue()	Returns the probability associated with the most recently computed independence test.
double	getScore()	Returns a number that is positive when dependence holds and more positive for greater dependence.
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)	Returns an Independence test for a sublist of the variables.
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 is 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.test.IndependenceTest

checkIndependence, getDataSets, getSampleSize, getVariable, getVariableNames

Constructor Details

IndTestFisherZFisherPValue

public IndTestFisherZFisherPValue(List<DataSet> dataSets,
 double alpha)

Constructor.

Parameters:

dataSets - The continuous datasets to analyze.

alpha - The alpha significance cutoff value.

Method Details

indTestSubset

public IndependenceTest indTestSubset(List<Node> vars)

Description copied from interface: IndependenceTest

Returns an Independence test for a sublist of the variables.

Specified by:

indTestSubset in interface IndependenceTest

Parameters:

vars - The sublist of variables.

Throws:

UnsupportedOperationException - Not implemented.

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.

Returns:

This p-value.

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 - This level.

This - alpha.

getAlpha

public double getAlpha()

Gets the getModel significance level.

Specified by:

getAlpha in interface IndependenceTest

Returns:

this alpha.

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.

determines

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

Description copied from interface: IndependenceTest

Returns true if y is determined the variable in z.

Specified by:

determines in interface IndependenceTest

Returns:

True if so.

Throws:

UnsupportedOperationException - Not implemented.

getData

public DataSet getData()

Returns the concatenated data.

Specified by:

getData in interface IndependenceTest

Returns:

This data

See Also:

• DataSet
• ICovarianceMatrix
• DataModel

getCov

public ICovarianceMatrix getCov()

Returns the covariance matrix of the concatenated data.

Specified by:

getCov in interface IndependenceTest

Returns:

This covariance matrix.

getScore

public double getScore()

Returns a number that is positive when dependence holds and more positive for greater dependence.

Specified by:

getScore in interface IndependenceTest

Returns:

This number

See Also:

• Fges

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.

isVerbose

public boolean isVerbose()

Returns True if verbose output should be printed.

Specified by:

isVerbose in interface IndependenceTest

Returns:

True if so.

setVerbose

public void setVerbose(boolean verbose)

Sets whether verbose output is printed.

Specified by:

setVerbose in interface IndependenceTest

Parameters:

verbose - True if so.