Package edu.pitt.csb.mgm

Class IndTestMultinomialLogisticRegressionWald

java.lang.Object

edu.pitt.csb.mgm.IndTestMultinomialLogisticRegressionWald

All Implemented Interfaces:

IndependenceTest

public class IndTestMultinomialLogisticRegressionWald
extends Object
implements IndependenceTest

Performs a test of conditional independence X _||_ Y | Z1...Zn where all searchVariables are either continuous or discrete. This test is valid for both ordinal and non-ordinal discrete searchVariables.

This logisticRegression makes multiple assumptions: 1. IIA 2. Large sample size (multiple regressions needed on subsets of sample)

Version:

$Id: $Id

Author:

josephramsey, Augustus Mayo.

Constructor Summary

Constructors

Constructor	Description
IndTestMultinomialLogisticRegressionWald(DataSet data, double alpha, boolean preferLinear)	Constructs a new instance of IndTestMultinomialLogisticRegressionWald with the specified parameters.

Method Summary

Modifier and Type	Method	Description
IndependenceResult	checkIndependence(Node x, Node y, Set<Node> z)	Determines the independence between two variables given a set of conditioning variables.
boolean	determines(List<Node> z, Node y)	Determines the independence between a set of variables and a target variable.
double	getAlpha()	Getter for the field alpha.
DataSet	getData()	Retrieves the original dataset used for the independence test.
double	getPValue()	getPValue.
List<Node>	getVariables()	getVariables.
IndependenceTest	indTestSubset(List<Node> vars)	Tests the conditional independence between two variables given a sublist of variables.
boolean	isVerbose()	Checks if the program is in verbose mode.
void	setAlpha(double alpha)	Sets the significance level of the independence test.
void	setVerbose(boolean verbose)	Sets the verbose mode of the program.
String	toString()	Returns a string representation of the object.

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, getCov, getDataSets, getSampleSize, getVariable, getVariableNames

Constructor Details

IndTestMultinomialLogisticRegressionWald

public IndTestMultinomialLogisticRegressionWald(DataSet data,
 double alpha,
 boolean preferLinear)

Constructs a new instance of IndTestMultinomialLogisticRegressionWald with the specified parameters.

Parameters:

data - The dataset to perform the independence test on.

alpha - The significance level of the independence test. Must be in the range [0, 1].

preferLinear - Whether to prefer the linear model for the logistic regression.

Throws:

IllegalArgumentException - if the alpha value is not in the range [0, 1].

Method Details

indTestSubset

public IndependenceTest indTestSubset(List<Node> vars)

Tests the conditional independence between two variables given a sublist of variables.

Specified by:

indTestSubset in interface IndependenceTest

Parameters:

vars - The sublist of variables.

Returns:

The result of the independence test.

checkIndependence

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

Determines the independence between two variables given a set of conditioning variables.

Specified by:

checkIndependence in interface IndependenceTest

Parameters:

x - the first variable

y - the second variable

z - the set of conditioning variables

Returns:

the result of the independence test

See Also:

• IndependenceResult

getPValue

public double getPValue()

getPValue.

Returns:

the probability associated with the most recently executed independence test, of Double.NaN if p value is not meaningful for tis test.

getVariables

public List<Node> getVariables()

getVariables.

Specified by:

getVariables in interface IndependenceTest

Returns:

the list of searchVariables over which this independence checker is capable of determinining independence relations.

determines

public boolean determines(List<Node> z,
 Node y)

Determines the independence between a set of variables and a target variable.

Parameters:

z - The set of conditioning variables.

y - The target variable to test for independence.

Returns:

True if the target variable y is independent of the set of conditioning variables z, false otherwise.

getAlpha

public double getAlpha()

Getter for the field alpha.

Specified by:

getAlpha in interface IndependenceTest

Returns:

the significance level of the independence test.

Throws:

UnsupportedOperationException - if there is no significance level.

setAlpha

public void setAlpha(double alpha)

Sets the significance level of the independence test.

Specified by:

setAlpha in interface IndependenceTest

Parameters:

alpha - This level.

getData

public DataSet getData()

Retrieves the original dataset used for the independence test.

Specified by:

getData in interface IndependenceTest

Returns:

The original dataset used for the independence test.

See Also:

• DataSet
• ICovarianceMatrix
• DataModel

toString

public String toString()

Returns a string representation of the object. The returned string includes the type of the regression (Multinomial Logistic Regression) and the alpha value used.

Specified by:

toString in interface IndependenceTest

Overrides:

toString in class Object

Returns:

A string representation of the object.

isVerbose

public boolean isVerbose()

Checks if the program is in verbose mode.

Specified by:

isVerbose in interface IndependenceTest

Returns:

true if the program is in verbose mode, false otherwise

setVerbose

public void setVerbose(boolean verbose)

Sets the verbose mode of the program.

Specified by:

setVerbose in interface IndependenceTest

Parameters:

verbose - True if the program should be in verbose mode, false otherwise.