Class PvalueUniformityUnderNull

java.lang.Object
edu.cmu.tetrad.algcomparison.statistic.PvalueUniformityUnderNull
All Implemented Interfaces:
Statistic, Serializable

public class PvalueUniformityUnderNull extends Object implements Statistic
Estimates whether the p-values under the null are Uniform usign the Markov Checker. This estimates whether the p-value of the Kolmogorov-Smirnov test for distribution of p-values under the null using the Fisher Z test for the local Markov check is uniform, so is only applicable to continuous data and really strictly only for Gaussian data.
Author:
josephramsey
See Also:
  • Constructor Details

    • PvalueUniformityUnderNull

      public PvalueUniformityUnderNull(double alpha)
  • Method Details

    • getAbbreviation

      public String getAbbreviation()
      Description copied from interface: Statistic
      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()
      Description copied from interface: Statistic
      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)
      Description copied from interface: Statistic
      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)
      Description copied from interface: Statistic
      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.