Package edu.cmu.tetrad.search.test

Class IndTestTrekSep

java.lang.Object
edu.cmu.tetrad.search.test.IndTestTrekSep
All Implemented Interfaces:
IndependenceTest
public final class IndTestTrekSep extends Object implements IndependenceTest
Checks d-separations in structural model using t-separations over indicators.
Author:
Adam Brodie

  • Constructor Details

    • IndTestTrekSep

      public IndTestTrekSep(ICovarianceMatrix covMatrix, double alpha, List<List<Node>> clustering, List<Node> latents)
      Constructs a new independence test that will determine conditional independence facts using the given correlation matrix and the given significance level.
      Parameters:
      covMatrix - The covariance over the measures.
      alpha - The significance level.
      clustering - The clustering of the measured variables. In each cluster, all measured variable in the cluster are explained by a single latent.
      latents - The list of latent variables for the clusters, in order.

  • Method Details

    • indTestSubset

      public IndependenceTest indTestSubset(List<Node> vars)
      Creates a new independence test instance for a sublist of the variables.
      Specified by:
      indTestSubset in interface IndependenceTest
      Parameters:
      vars - The sublist.

    • checkIndependence

      public IndependenceResult checkIndependence(Node x, Node y, List<Node> z)
      Determines whether variable x is independent of variable y given a list of conditioning variables z.
      Specified by:
      checkIndependence in interface IndependenceTest
      Parameters:
      x - the one variable being compared.
      y - the second variable being compared.
      z - the list of conditioning variables.
      Returns:
      True iff x _||_ y | z.
      Throws:
      org.apache.commons.math3.linear.SingularMatrixException - if a matrix singularity is encountered.
      See Also:

    • getPValue

      public double getPValue()
      Returns the probability associated with the most recently computed independence test.
      Returns:
      This p-value.

    • setAlpha

      public void setAlpha(double alpha)
      Sets the significance level at which independence judgments should be made. Affects the cutoff for partial correlations to be considered statistically equal to zero.
      Specified by:
      setAlpha in interface IndependenceTest
      Parameters:
      alpha - This significance level.

    • getAlpha

      public double getAlpha()
      Gets the model significance level.
      Specified by:
      getAlpha in interface IndependenceTest
      Returns:
      This alpha.

    • getVariables

      public List<Node> getVariables()
      Returns the list of variables over which this independence checker is capable of determinine independence relations-- that is, all the variables in the given graph or the given data set.
      Specified by:
      getVariables in interface IndependenceTest
      Returns:
      This list.

    • getVariable

      public Node getVariable(String name)
      Returns the variable with the given name.
      Specified by:
      getVariable in interface IndependenceTest
      Returns:
      This variable.

    • determines

      public boolean determines(List<Node> z, Node x) throws UnsupportedOperationException
      If isDeterminismAllowed(), defers to IndTestFisherZD; otherwise throws UnsupportedOperationException.
      Specified by:
      determines in interface IndependenceTest
      Returns:
      True if so
      Throws:
      UnsupportedOperationException - If the above condition is not met.

    • getData

      public DataSet getData()
      Returns the data set being analyzed.
      Specified by:
      getData in interface IndependenceTest
      Returns:
      This data.
      See Also:

    • toString

      public String toString()
      Returns a string representation of this test.
      Specified by:
      toString in interface IndependenceTest
      Overrides:
      toString in class Object
      Returns:
      This string.

    • setVariables

      public void setVariables(List<Node> variables)
      Sets the varialbe to this list (of the same length). Useful is multiple test are used that need the same object-identical lists of variables.
      Parameters:
      variables - This list.

    • getCov

      public ICovarianceMatrix getCov()
      Returns the covariance matrix.
      Specified by:
      getCov in interface IndependenceTest
      Returns:
      This matrix.

    • getDataSets

      public List<DataSet> getDataSets()
      Returns a singleton list consisting just of the dataset for this test.
      Specified by:
      getDataSets in interface IndependenceTest
      Returns:
      This lsit.

    • getSampleSize

      public int getSampleSize()
      Returns the sample size.
      Specified by:
      getSampleSize in interface IndependenceTest
      Returns:
      This size.

    • getScore

      public double getScore()
      Returns alpha - p.
      Specified by:
      getScore in interface IndependenceTest
      Returns:
      This nubmer.

    • isVerbose

      public boolean isVerbose()
      Returns true if verbose output should be printed.
      Specified by:
      isVerbose in interface IndependenceTest
      Returns:
      True if so.

    • setVerbose

      public void setVerbose(boolean verbose)
      Sets whether verbose output should be printed.
      Specified by:
      setVerbose in interface IndependenceTest
      Parameters:
      verbose - True if so.