Class AdjacencyFn

java.lang.Object

edu.cmu.tetrad.algcomparison.statistic.AdjacencyFn

All Implemented Interfaces:

Statistic, Serializable

public class AdjacencyFn
extends Object
implements Statistic

The adjacency precision. The true positives are the number of adjacencies in both the true and estimated graphs.

Version:

$Id: $Id

Author:

josephramsey

See Also:

• Serialized Form

Constructor Summary

Constructors

Constructor	Description
AdjacencyFn()	Constructs the statistic.

Method Summary

Modifier and Type	Method	Description
String	getAbbreviation()	The abbreviation for the statistic.
String	getDescription()	Returns a short one-line description of this statistic.
double	getNormValue(double value)	Returns a mapping of the statistic to the interval [0, 1], with higher being better.
double	getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)	Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.

Methods inherited from class Object

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

Methods inherited from interface Statistic

getValue, getValue, getValue, getValue, usesTruth

Constructor Details

AdjacencyFn

public AdjacencyFn()

Constructs the statistic.

Method Details

getAbbreviation

public String getAbbreviation()

The abbreviation for the statistic. This will be printed at the top of each column.

Returns the name of the statistic.

Specified by:

getAbbreviation in interface Statistic

Returns:

This abbreviation.

getDescription

public String getDescription()

Returns a short one-line description of this statistic. This will be printed at the beginning of the report.

Returns the description of the statistic.

Specified by:

getDescription in interface Statistic

Returns:

This description.

getValue

public double getValue(Graph trueDag,
 Graph trueGraph,
 Graph estGraph,
 DataModel dataModel,
 Parameters parameters)

Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.

Returns the value of the statistic, given the true graph and the estimated graph.

Specified by:

getValue in interface Statistic

Parameters:

trueDag - The true directed acyclic graph (DAG), including any latent and selection nodes, or null if no DAG is available. Statistics that ask DAG-level questions (ancestry, latent confounding, conversion to CPDAG or PAG) should read them off this graph, via Statistic.trueDagOrGraph(Graph, Graph), not off trueGraph.

trueGraph - The comparison graph, which could be a DAG, a CPDAG, or a PAG derived from the true DAG. Statistics that compare adjacencies or endpoints read them off this graph.

estGraph - The estimated graph, which corresponds to the same type as the trueGraph.

dataModel - The data model, which may be null.

parameters - The parameters for the calculation, which may be null.

Returns:

The computed value of the statistic as a double.

getNormValue

public double getNormValue(double value)

Returns a mapping of the statistic to the interval [0, 1], with higher being better. This is used for a calculation of a utility for an algorithm. If the statistic is already between 0 and 1, you can just return the statistic.

Returns a mapping of the statistic to the interval [0, 1], with higher being better. This is used for a calculation of a utility for an algorithm.If the statistic is already between 0 and 1, you can just return the statistic.

Specified by:

getNormValue in interface Statistic

Parameters:

value - The value of the statistic.

Returns:

The weight of the statistic, 0 to 1, higher is better.