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

Author:
josephramsey
See Also:

  • Constructor Details

    • Mimbuild

      public Mimbuild()

  • Method Details

    • search

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

    • 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()
      Returns:
      The minimum function (Fgsl) value achieved.

    • getpValue

      public double getpValue()
      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.

    • setMinClusterSize

      public void setMinClusterSize(int minClusterSize)