Package edu.cmu.tetrad.search

Class Mimbuild

java.lang.Object

edu.cmu.tetrad.search.Mimbuild

public class Mimbuild
extends Object

Provides an implementation of Mimbuild, an algorithm that takes a clustering of variables, each of which is explained by a single latent, then forms the implied covariance matrix over the latent variables, then runs a CPDAG search to in the structure over the latent themselves.

Specifically, the search will first infer the covariance matrix over the latents and then will use the GRaSP algorithm (see) to infer the structure graph over the latents, using the SEM Bic score with the given penalty discount (default 2).

One may wish to obtain the implied correlation matrix over the latents and run one's own choice of CPDAG algorithm on it with one's own test or score; a method is available to return this covariance matrix.

A suitable clustering for Mimbuild may be obtained using the BPC or FOFC algorithm (see).

This algorithm is described in Spirtes et al., Causation, Prediction, and Search.

This class is configured to respect the knowledge of forbidden and required edges, including knowledge of temporal tiers.

Version:

$Id: $Id

Author:

josephramsey

See Also:

• Bpc
• Fofc
• getLatentsCov()
• Grasp
• Knowledge

Constructor Summary

Constructors

Constructor	Description
Mimbuild()	Constructs a new Mimbuild search.

Method Summary

Modifier and Type	Method	Description
List<List<Node>>	getClustering()	Returns the clustering of measured variables, each of which is explained by a single latent.
Graph	getFullGraph()	The full graph inferred, including the edges from latents to measures.
ICovarianceMatrix	getLatentsCov()	Returns the inferred covariance matrix over the latent variables.
int	getMinClusterSize()	jf Clusters smaller than this size will be tossed out.
double	getMinimum()	Getter for the field minimum.
double	getpValue()	Getter for the field pValue.
Graph	search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measuresCov)	Does a Mimbuild search.
void	setKnowledge(Knowledge knowledge)	Sets the knowledge to be used in the search.
void	setMinClusterSize(int minClusterSize)	Setter for the field minClusterSize.
void	setPenaltyDiscount(double penaltyDiscount)	Sets the penalty discount of the score used to infer the structure graph.
void	setSeed(long seed)	Setter for the field seed.

Methods inherited from class java.lang.Object

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

Constructor Details

Mimbuild

public Mimbuild()

Constructs a new Mimbuild search.

Method Details

search

public Graph search(List<List<Node>> clustering,
 List<String> latentNames,
 ICovarianceMatrix measuresCov)
             throws InterruptedException

Does a Mimbuild search.

Parameters:

clustering - The clustering to use--this clusters the measured variables in such a way that each cluster is explained by a single latent variables.

latentNames - The names of the latent variables corresponding in order ot each cluster in the clustering.

measuresCov - The covariance matrix over the measured variables.

Returns:

The inferred graph over the latent variables.

Throws:

InterruptedException

getClustering

public List<List<Node>> getClustering()

Returns the clustering of measured variables, each of which is explained by a single latent.

Returns:

This clustering.

setKnowledge

public void setKnowledge(Knowledge knowledge)

Sets the knowledge to be used in the search.

Parameters:

knowledge - This knowledge.

getLatentsCov

public ICovarianceMatrix getLatentsCov()

Returns the inferred covariance matrix over the latent variables.

Returns:

This covariance matrix.

getMinimum

public double getMinimum()

Getter for the field minimum.

Returns:

The minimum function (Fgsl) value achieved.

getpValue

public double getpValue()

Getter for the field pValue.

Returns:

The p value of the optimization.

getFullGraph

public Graph getFullGraph()

The full graph inferred, including the edges from latents to measures. And all fo the edges inferred among latents.

Returns:

This full graph.

setPenaltyDiscount

public void setPenaltyDiscount(double penaltyDiscount)

Sets the penalty discount of the score used to infer the structure graph.

Parameters:

penaltyDiscount - The penalty discount.

getMinClusterSize

public int getMinClusterSize()

jf Clusters smaller than this size will be tossed out.

Returns:

a int

setMinClusterSize

public void setMinClusterSize(int minClusterSize)

Setter for the field minClusterSize.

Parameters:

minClusterSize - a int

setSeed

public void setSeed(long seed)

Setter for the field seed.

Parameters:

seed - a long