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:
      Thsi 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 trueGraph, Graph estGraph, DataModel dataModel)
      Returns the value of this statistic, given the true graph and the estimated graph.
      Specified by:
      getValue in interface Statistic
      Parameters:
      trueGraph - The true graph (DAG, CPDAG, PAG_of_the_true_DAG).
      estGraph - The estimated graph (same type).
      dataModel - The data model.
      Returns:
      The value of the statistic.
    • 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