Package edu.cmu.tetrad.search.test

Class IndTestProbabilistic

java.lang.Object
edu.cmu.tetrad.search.test.IndTestProbabilistic
All Implemented Interfaces:
IndependenceTest
public class IndTestProbabilistic extends Object implements IndependenceTest
Uses BCInference by Cooper and Bui to calculate probabilistic conditional independence judgments.
Version:
$Id: $Id
Author:
josephramsey 3/2014

  • Constructor Details

    • IndTestProbabilistic

      public IndTestProbabilistic(DataSet dataSet)
      Initializes the test using a discrete data sets.
      Parameters:
      dataSet - a DataSet object

  • Method Details

    • indTestSubset

      public IndependenceTest indTestSubset(List<Node> vars)
      Returns an Independence test for a sublist of the variables.
      Specified by:
      indTestSubset in interface IndependenceTest
      Parameters:
      vars - The sublist of variables.
      Returns:
      a IndependenceTest object

    • checkIndependence

      public IndependenceResult checkIndependence(Node x, Node y, Set<Node> _z)
      Checks the independence between two variables x and y given a conditioning set z.

      Returns an independence result that states whether x _||_y | z and what the p-value of the test is.

      Specified by:
      checkIndependence in interface IndependenceTest
      Parameters:
      x - The first variable to test, represented as a Node object.
      y - The second variable to test, represented as a Node object.
      _z - The set of conditioning variables, represented as a Set of Node objects.
      Returns:
      An IndependenceResult object representing the outcome of the independence test.
      See Also:

    • checkIndependence

      public IndependenceResult checkIndependence(Node x, Node y, Node... z)
      Checks the independence fact in question and returns and independence result.

      Returns an independence result that states whether x _||_y | z and what the p-value of the test is.

      Specified by:
      checkIndependence in interface IndependenceTest
      Parameters:
      x - a Node object
      y - a Node object
      z - a Node object
      Returns:
      The independence result.
      See Also:

    • probConstraint

      public double probConstraint(BCInference bci, BCInference.OP op, Node x, Node y, Node[] z, Map<Node,Integer> indices)
      Returns the probability of the constraint x op y | z.
      Parameters:
      bci - The BCInference object.
      op - The operator.
      x - The first variable.
      y - The second variable.
      z - The conditioning set.
      indices - A map from nodes to their indices.
      Returns:
      The probability.

    • getVariables

      public List<Node> getVariables()
      Returns the list of variables used in this object.
      Specified by:
      getVariables in interface IndependenceTest
      Returns:
      A List of Node objects representing the variables used.

    • getVariable

      public Node getVariable(String name)
      Retrieves the Node object that matches the given name from the list of nodes.
      Specified by:
      getVariable in interface IndependenceTest
      Parameters:
      name - The name of the variable to retrieve.
      Returns:
      The Node object matching the given name, or null if no match is found.

    • determines

      public boolean determines(Set<Node> z, Node y)
      Determines whether a given set of nodes, z, determines another node, y.
      Specified by:
      determines in interface IndependenceTest
      Parameters:
      z - A Set of nodes representing the conditioning set.
      y - The node for which determination is checked.
      Returns:
      true if z determines y, false otherwise.

    • getAlpha

      public double getAlpha()
      Returns the alpha parameter for the probabilistic test.
      Specified by:
      getAlpha in interface IndependenceTest
      Returns:
      The alpha parameter.

    • setAlpha

      public void setAlpha(double alpha)
      Sets the alpha parameter for the probabilistic test.
      Specified by:
      setAlpha in interface IndependenceTest
      Parameters:
      alpha - The alpha parameter to set.

    • getData

      public DataModel getData()
      Returns the data model associated with this instance.
      Specified by:
      getData in interface IndependenceTest
      Returns:
      The data model.

    • getH

      public Map<IndependenceFact,Double> getH()
      Returns a map from independence facts to their probabilities of independence.
      Returns:
      The map.

    • getPosterior

      public double getPosterior()
      Returns the posterior probability of the last independence test.
      Returns:
      The posterior probability.

    • isVerbose

      public boolean isVerbose()
      Returns the verbose flag indicating whether verbose output should be printed.
      Specified by:
      isVerbose in interface IndependenceTest
      Returns:
      The verbose flag.

    • setVerbose

      public void setVerbose(boolean verbose)
      Sets the verbose flag indicating whether verbose output should be printed.
      Specified by:
      setVerbose in interface IndependenceTest
      Parameters:
      verbose - true if verbose output should be printed, false otherwise.

    • setThreshold

      public void setThreshold(boolean threshold)
      Sets whether the independence test should be thresholded (true) or randomized (false).
      Parameters:
      threshold - true if the independence test should be thresholded, false if it should be randomized.

    • setCutoff

      public void setCutoff(double cutoff)
      Sets the cutoff for the independence test.
      Parameters:
      cutoff - the cutoff for the independence test.

    • setPriorEquivalentSampleSize

      public void setPriorEquivalentSampleSize(double priorEquivalentSampleSize)
      Sets the prior equivalent sample size for the independence test. The prior equivalent sample size is a parameter used in the calculation of the test statistic. A higher sample size will make the test more conservative, while a lower sample size will make the test more liberal.
      Parameters:
      priorEquivalentSampleSize - the prior equivalent sample size to set