Class FractionDependentUnderNull

java.lang.Object
edu.cmu.tetrad.algcomparison.statistic.FractionDependentUnderNull
All Implemented Interfaces:
Statistic, Serializable
public class FractionDependentUnderNull extends Object implements Statistic
Estimates whether the p-values under the null are Uniform usign the Markov Checker. This estimate the fraction of dependent judgements from the local Fraithfulness check, under the alternative hypothesis of dependence. This is only applicable to continuous data and really strictly only for Gaussian data.
Version:
$Id: $Id
Author:
josephramsey
See Also:

  • Constructor Details

    • FractionDependentUnderNull

      public FractionDependentUnderNull()

      Constructor for FractionDependentUnderNull.

    • FractionDependentUnderNull

      public FractionDependentUnderNull(double alpha)

      Constructor for FractionDependentUnderNull.

      Parameters:
      alpha - a double

  • Method Details

    • getAbbreviation

      public String getAbbreviation()
      The abbreviation for the statistic. This will be printed at the top of each column.
      Specified by:
      getAbbreviation in interface Statistic
      Returns:
      This abbreviation.

    • getDescription

      public String getDescription()
      Returns a short one-line description of this statistic. This will be printed at the beginning of the report.
      Specified by:
      getDescription in interface Statistic
      Returns:
      This description.

    • getValue

      public double getValue(Graph trueDag, Graph trueGraph, Graph estGraph, DataModel dataModel, Parameters parameters)
      Calculates and returns the value of the statistic based on the provided graphs, data model, and parameters.
      Specified by:
      getValue in interface Statistic
      Parameters:
      trueDag - The true directed acyclic graph (DAG), including any latent and selection nodes, or null if no DAG is available. Statistics that ask DAG-level questions (ancestry, latent confounding, conversion to CPDAG or PAG) should read them off this graph, via Statistic.trueDagOrGraph(Graph, Graph), not off trueGraph.
      trueGraph - The comparison graph, which could be a DAG, a CPDAG, or a PAG derived from the true DAG. Statistics that compare adjacencies or endpoints read them off this graph.
      estGraph - The estimated graph, which corresponds to the same type as the trueGraph.
      dataModel - The data model, which may be null.
      parameters - The parameters for the calculation, which may be null.
      Returns:
      The computed value of the statistic as a double.

    • getNormValue

      public double getNormValue(double value)
      Returns a mapping of the statistic to the interval [0, 1], with higher being better. This is used for a calculation of a utility for an algorithm. If the statistic is already between 0 and 1, you can just return the statistic.
      Specified by:
      getNormValue in interface Statistic
      Parameters:
      value - The value of the statistic.
      Returns:
      The weight of the statistic, 0 to 1, higher is better.

    • setAlpha

      public void setAlpha(double alpha)

      Setter for the field alpha.

      Parameters:
      alpha - a double

    • usesTruth

      public boolean usesTruth()
      This method does not use the truth so is suitable for analyzing empirical data.
      Specified by:
      usesTruth in interface Statistic
      Returns:
      True if this statistic uses the true graph, false otherwise.