Package edu.cmu.tetrad.search

Class MimbuildTrek

java.lang.Object

edu.cmu.tetrad.search.MimbuildTrek

public class MimbuildTrek
extends Object

Implements Mimbuild using the theory of treks and ranks.

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

Author:

adambrodie

See Also:

• Knowledge
• Mimbuild

Constructor Summary

Constructors

Constructor	Description
MimbuildTrek()

Method Summary

Modifier and Type	Method	Description
List<List<Node>>	getClustering()	The clustering used.
Graph	getFullGraph()	The full graph discovered.
ICovarianceMatrix	getLatentsCov()	The covariance matrix over the latents that is implied by the clustering.
int	getMinClusterSize()	jf Clusters smaller than this size will be tossed out.
double	getpValue()	The p-value of the model.
Graph	search(List<List<Node>> clustering, List<String> latentNames, ICovarianceMatrix measuresCov)	Does the search and returns the graph.
void	setAlpha(double alpha)	The alpha to use.
void	setEpsilon(double epsilon)	Sets the parameter convergence threshold.
void	setKnowledge(Knowledge knowledge)	The knowledge to use in the search.
void	setMinClusterSize(int minClusterSize)

Methods inherited from class java.lang.Object

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

Constructor Details

MimbuildTrek

public MimbuildTrek()

Method Details

search

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

Does the search and returns the graph.

Parameters:

clustering - A clustering of the variables, each of which is explained by a single latent.

latentNames - The names of the latents, which cannot be known by the clustering algorithm.

measuresCov - The covariance matrix over the measured variables, from the data.

Returns:

A graph over the latents.

getClustering

public List<List<Node>> getClustering()

The clustering used.

Returns:

This clustering.

setAlpha

public void setAlpha(double alpha)

The alpha to use.

Parameters:

alpha - This alpha.

setKnowledge

public void setKnowledge(Knowledge knowledge)

The knowledge to use in the search.

Parameters:

knowledge - This knowledge.

getLatentsCov

public ICovarianceMatrix getLatentsCov()

The covariance matrix over the latents that is implied by the clustering.

Returns:

This covariance matrix.

getpValue

public double getpValue()

The p-value of the model.

Returns:

This p-value.

getFullGraph

public Graph getFullGraph()

The full graph discovered.

Returns:

the allowUnfaithfulness discovered graph, with latents and indicators.

setEpsilon

public void setEpsilon(double epsilon)

Sets the parameter convergence threshold. Default = 1e-4.

Parameters:

epsilon - This threshold.

getMinClusterSize

public int getMinClusterSize()

jf Clusters smaller than this size will be tossed out.

setMinClusterSize

public void setMinClusterSize(int minClusterSize)