Package edu.cmu.tetrad.search.test

Class IndTestFisherZ

java.lang.Object

edu.cmu.tetrad.search.test.IndTestFisherZ

All Implemented Interfaces:

EffectiveSampleSizeSettable, IndependenceTest, RowsSettable

public final class IndTestFisherZ
extends Object
implements IndependenceTest, EffectiveSampleSizeSettable, RowsSettable

Checks conditional independence of variable in a continuous data set using Fisher's Z test. See Spirtes, Glymour, and Scheines, "Causation, Prediction and Search," 2nd edition, page 94.

Version:

$Id: $Id

Author:

josephramsey, Frank Wimberly

Constructor Summary

Constructors

Constructor	Description
IndTestFisherZ(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).
IndTestFisherZ(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.
IndTestFisherZ(Matrix data, List<Node> variables, double alpha)	Constructs a new Fisher Z independence test with the listed arguments.

Method Summary

Modifier and Type	Method	Description
IndependenceResult	checkIndependence(Node x, Node y, Set<Node> z)	Determines whether variable x _||_ y | z given a list of conditioning variables z.
boolean	determines(List<Node> z, Node x)	Determines if a given Node x is determined by a list of Nodes z.
double	getAlpha()	Gets the model significance level.
double	getBic()	Returns the BIC score for this test.
ICovarianceMatrix	getCov()	Returns the correlation matrix being analyzed.
DataSet	getData()	Returns the data set being analyzed.
List<DataSet>	getDataSets()	Returns the (singleton) list of datasets being analyzed.
double	getPValue(Node x, Node y, Set<Node> z)	Returns the p-value for x _||_ y | z.
List<Integer>	getRows()	Returns the rows used in the test.
int	getSampleSize()	Returns the sample size.
Node	getVariable(String name)	Returns 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)	Creates a new independence test instance for a subset of the variables.
boolean	isVerbose()	Returns true iff verbose output should be printed.
void	setAlpha(double alpha)	Sets the significance level at which independence judgments should be made.
void	setEffectiveSampleSize(int effectiveSampleSize)	Sets the sample size to use for the independence test, which may be different from the sample size of the data set or covariance matrix.
void	setRows(List<Integer> rows)	Allows the user to set which rows are used in the test.
void	setUsePseudoinverse(boolean usePseudoinverse)	Sets whether or not to use the pseudoinverse method for determining independence.
void	setVariables(List<Node> variables)	Sets the variables to a new list of the same size.
void	setVerbose(boolean verbose)	Sets whether verbose output should be printed.
String	toString()	Returns a string representation of the Fisher Z independence 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

IndTestFisherZ

public IndTestFisherZ(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 containing only continuous columns.

alpha - The alpha level of the test.

IndTestFisherZ

public IndTestFisherZ(Matrix data,
 List<Node> variables,
 double alpha)

Constructs a new Fisher Z independence test with the listed arguments.

Parameters:

data - A 2D continuous data set with no missing values.

variables - A list of variables, a subset of the variables of data.

alpha - The alpha level of the test.

IndTestFisherZ

public IndTestFisherZ(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.

Parameters:

covMatrix - The covariance matrix.

alpha - The alpha level of the test.

Method Details

indTestSubset

public IndependenceTest indTestSubset(List<Node> vars)

Creates a new independence test instance for a subset of the variables.

Specified by:

indTestSubset in interface IndependenceTest

Parameters:

vars - The sublist of variables.

Returns:

a IndependenceTest object

See Also:

• IndependenceTest

checkIndependence

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

Determines whether variable x _||_ y | z given a list of conditioning variables z.

Specified by:

checkIndependence in interface IndependenceTest

Parameters:

x - a Node object

y - a Node object

z - a Set object

Returns:

a IndependenceResult object

See Also:

• IndependenceResult

getPValue

public double getPValue(Node x,
 Node y,
 Set<Node> z)
                 throws org.apache.commons.math3.linear.SingularMatrixException

Returns the p-value for x _||_ y | z.

Parameters:

x - The first node.

y - The second node.

z - The set of conditioning variables.

Returns:

The p-value.

Throws:

org.apache.commons.math3.linear.SingularMatrixException - If a singularity occurs when invering a matrix.

getBic

public double getBic()

Returns the BIC score for this test.

Returns:

The BIC score.

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 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.

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 variables to a new list of the same size. Useful if multiple independence tests are needed with object-identical sets of variables.

Parameters:

variables - The new list of variables.

getVariable

public Node getVariable(String name)

Returns the variable with the given name.

Specified by:

getVariable in interface IndependenceTest

Parameters:

name - a String object

Returns:

This variable.

getData

public DataSet getData()

Returns the data set being analyzed.

Specified by:

getData in interface IndependenceTest

Returns:

This data.

See Also:

• DataSet
• ICovarianceMatrix
• DataModel

getCov

public ICovarianceMatrix getCov()

Returns the correlation matrix being analyzed.

Specified by:

getCov in interface IndependenceTest

Returns:

This correlation matrix.

getDataSets

public List<DataSet> getDataSets()

Returns the (singleton) list of datasets being analyzed.

Specified by:

getDataSets in interface IndependenceTest

Returns:

these datasets.

getSampleSize

public int getSampleSize()

Returns the sample size.

Specified by:

getSampleSize in interface IndependenceTest

Returns:

This size.

setEffectiveSampleSize

public void setEffectiveSampleSize(int effectiveSampleSize)

Sets the sample size to use for the independence test, which may be different from the sample size of the data set or covariance matrix. If not set, the sample size of the data set or covariance matrix is used.

Specified by:

setEffectiveSampleSize in interface EffectiveSampleSizeSettable

Parameters:

effectiveSampleSize - The sample size to use.

isVerbose

public boolean isVerbose()

Returns true iff 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 should be printed.

Specified by:

setVerbose in interface IndependenceTest

Parameters:

verbose - True, if so.

toString

public String toString()

Returns a string representation of the Fisher Z independence test. The string includes the value of alpha.

Specified by:

toString in interface IndependenceTest

Overrides:

toString in class Object

Returns:

A string representing the Fisher Z independence test.

determines

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

Determines if a given Node x is determined by a list of Nodes z.

Parameters:

z - the list of Nodes

x - the Node to check if it is determined

Returns:

true if x is determined by z, false otherwise

Throws:

UnsupportedOperationException - if the operation is not supported

getRows

public List<Integer> getRows()

Returns the rows used in the test.

Specified by:

getRows in interface RowsSettable

Returns:

The rows used in the test.

setRows

public void setRows(List<Integer> rows)

Allows the user to set which rows are used in the test. Otherwise, all rows are used, except those with missing values.

Specified by:

setRows in interface RowsSettable

Parameters:

rows - The rows to use for the test. Can be null.

setUsePseudoinverse

public void setUsePseudoinverse(boolean usePseudoinverse)

Sets whether or not to use the pseudoinverse method for determining independence.

Parameters:

usePseudoinverse - true to use the pseudoinverse method, false otherwise.