ConfidenceLevel
jhpro.stat

Class ConfidenceLevel



  • public class ConfidenceLevelextends Object
    Confidence level calculations.

    For discoveries, 1-CLb indicates the probability that the background fluctuates to produce a distribution of candidates at least as signal-like as those observed in the data. For discovery, 1-CLb is required to be no more than 2.87x10-7, or twice that, depending on how one interprets what is meant by \xe2\x80\x9cfive sigma,\xe2\x80\x9d including just one side of a Gaussian tail or both. A \xe2\x80\x9cthree sigma\xe2\x80\x9d excess is defined to be 1-CLb = 1.3x10-3 or twice that. But forming discovery p-values, we must compute 1-CLb values of the order of 10-7. This computation involves generating of the order of 10^8 pseudoexperiments, just to be on the safe side.

    Read Reference: HEP-EX/9902006. see: Tom Junk,NIM A434, p. 435-443,

    • Method Summary

      Methods 
      Modifier and TypeMethod and Description
      voiddoc()
      Show online documentation.
      doubleget3sProbability()
      Get 3s probability.
      doubleget5sProbability()
      Get 5s probability.
      doublegetAverageCLs()
      Get average CLs.
      doublegetAverageCLsb()
      Get average CLsb.
      doublegetCLb()
      Get the Confidence Level for the background only.
      doublegetCLb(boolean use_sMC)
      Get the Confidence Level for the background only.
      doublegetCLs()
      Get the Confidence Level defined by CLs = CLsb/CLb.
      doublegetCLs(boolean use_sMC)
      Get the Confidence Level defined by CLs = CLsb/CLb.
      doublegetCLsb()
      Get the Confidence Level for the signal plus background hypothesis The confidence level for excluding the possibility of simultaneous presence of new particle production and background (the s + b hypothesis)
      doublegetCLsb(boolean use_sMC)
      Get the Confidence Level for the signal plus background hypothesis
      doublegetExpectedCLb_b()
      Get the expected Confidence Level for the background only if there is only background.These are indications of how well an experiment would do on average in excluding a signal if the signal truly is not present, and are the important figures of merit when optimizing an analysis for exclusion.
      doublegetExpectedCLb_b(int sigma)
      Get the expected Confidence Level for the background only if there is only background.
      doublegetExpectedCLb_sb()
      Get the expected Confidence Level for the background only if there is signal and background.
      doublegetExpectedCLb_sb(int sigma)
      Get the expected Confidence Level for the background only if there is signal and background.
      doublegetExpectedCLs_b()
      Get getExpectedCLsb_b/getExpectedCLb_b.
      doublegetExpectedCLs_b(int sigma)
      Get getExpectedCLsb_b/getExpectedCLb_b
      doublegetExpectedCLsb_b()
      Get the expected Confidence Level for the signal plus background hypothesis if there is only background.
      doublegetExpectedCLsb_b(int sigma)
      Get the expected Confidence Level for the signal plus background hypothesis if there is only background.
      doublegetExpectedStatistic_b() 
      doublegetExpectedStatistic_b(int sigma)
      Get the expected statistic value in the background only hypothesis
      doublegetExpectedStatistic_sb(int sigma)
      Get the expected statistic value in the signal plus background hypothesis
      H1DgetLNQb(int bins, double min, double max)
      Get a histogram of a canonical -2lnQ plot for background hypothesis (full)
      H1DgetLNQsb(int bins, double min, double max)
      Get a histogram of a canonical -2lnQ plot for for signal and background hypothesis
      ArrayList<H1D>getResults(String Option)
      Display sort of a "canonical" -2lnQ plot.
      doublegetStatistic() 
      voidsetBtot(double in) 
      voidsetDtot(int in) 
      voidsetLRB(double[] in) 
      voidsetLRS(double[] in) 
      voidsetStot(double in) 
      voidsetTSB(double[] in) 
      voidsetTSD(double in) 
      voidsetTSS(double[] in) 
    • Constructor Detail

      • ConfidenceLevel

        public ConfidenceLevel()
        Default constructor.
      • ConfidenceLevel

        public ConfidenceLevel(int mc)
        Construct ConfLevel
        Parameters:
        mc - number of MonteCarlo experiments
      • ConfidenceLevel

        public ConfidenceLevel(int mc,               boolean onesided)
        Build confidence level.
        Parameters:
        mc - is the number of Monte Carlo experiments
        onesided - specifies if the intervals are one-sided or not.
    • Method Detail

      • getExpectedStatistic_b

        public double getExpectedStatistic_b(int sigma)
        Get the expected statistic value in the background only hypothesis
        Parameters:
        sigma - between -2 and 2
        Returns:
      • getExpectedStatistic_sb

        public double getExpectedStatistic_sb(int sigma)
        Get the expected statistic value in the signal plus background hypothesis
        Parameters:
        sigma -
        Returns:
      • getCLb

        public double getCLb()
        Get the Confidence Level for the background only. This confidence level quantifies the confidence of a potential discovery, as it expresses the probability that background processes would give fewer than or equal to the number of candidates observed. 1-CLb is the probability that the null hypothesis will give an outcome that looks at least as signal-like as the one observed. For discovery, 1-CLb is required to be no more than 2.87x10-7, or twice that, depending on how one interprets what is meant by \xe2\x80\x9cfive sigma,\xe2\x80\x9d including just one side of a Gaussian tail or both. A \xe2\x80\x9cthree sigma\xe2\x80\x9d excess is defined to be 1-CLb=1.3x10-3 or twice that. But forming discovery p-values, we must compute 1-CLb values of the order of 10-7. This computation involves generating of the order of 10^8 pseudoexperiments, just to be on the safe side.
        Returns:
        Confidence Level for the background only.
      • getCLb

        public double getCLb(boolean use_sMC)
        Get the Confidence Level for the background only. This confidence level quantifies the confidence of a potential discovery, as it expresses the probability that background processes would give fewer than or equal to the number of candidates observed. 1-CLb is the probability that the null hypothesis will give an outcome that looks at least as signal-like as the one observed. For discovery, 1-CLb is required to be no more than 2.87*10-7, or twice that, depending on how one interprets what is meant by \xe2\x80\x9cfive sigma,\xe2\x80\x9d including just one side of a Gaussian tail or both. A \xe2\x80\x9cthree sigma\xe2\x80\x9d excess is defined to be 1-CLb = 1.3*10^-3 or twice that.

        But forming discovery p-values, we must compute 1-CLb values of the order of 10-7. This computation involves generating of the order of 10^8 pseudoexperiments, just to be on the safe side.

        Parameters:
        use_sMC -
        Returns:
        Confidence Level for the background only.
      • getCLsb

        public double getCLsb()
        Get the Confidence Level for the signal plus background hypothesis The confidence level for excluding the possibility of simultaneous presence of new particle production and background (the s + b hypothesis)
        Returns:
      • getCLsb

        public double getCLsb(boolean use_sMC)
        Get the Confidence Level for the signal plus background hypothesis
        Parameters:
        use_sMC -
        Returns:
      • getCLs

        public double getCLs()
        Get the Confidence Level defined by CLs = CLsb/CLb. This quantity is stable w.r.t. background fluctuations.

        This hypothesis is excluded at the 95% CL if CLs = 0.05, and at more than the 95% CL if CLs < 0.05, assuming that signal is present.

        Returns:
      • getCLs

        public double getCLs(boolean use_sMC)
        Get the Confidence Level defined by CLs = CLsb/CLb. This quantity is stable w.r.t. background fluctuations.

        This hypothesis is excluded at the 95% CL if CLs = 0.05, and at more than the 95% CL if CLs < 0.05, assuming that signal is present.

        Parameters:
        use_sMC - use or not MC.
        Returns:
      • getExpectedCLsb_b

        public double getExpectedCLsb_b(int sigma)
        Get the expected Confidence Level for the signal plus background hypothesis if there is only background.
        Parameters:
        sigma -
        Returns:
      • getExpectedCLb_sb

        public double getExpectedCLb_sb(int sigma)
        Get the expected Confidence Level for the background only if there is signal and background.
        Parameters:
        sigma -
        Returns:
      • getExpectedCLb_b

        public double getExpectedCLb_b(int sigma)
        Get the expected Confidence Level for the background only if there is only background.
        Parameters:
        sigma -
        Returns:
      • getAverageCLsb

        public double getAverageCLsb()
        Get average CLsb.
        Returns:
      • getAverageCLs

        public double getAverageCLs()
        Get average CLs.
      • get3sProbability

        public double get3sProbability()
        Get 3s probability.
      • get5sProbability

        public double get5sProbability()
        Get 5s probability.
      • getResults

        public ArrayList<H1D> getResults(String Option)
        Display sort of a "canonical" -2lnQ plot. This results in a plot with 2 elements: // - The histogram of -2lnQ for background hypothesis (full) - The histogram of -2lnQ for signal and background hypothesis (dashed) The 2 histograms are respectively named b_hist and sb_hist.
        Parameters:
        Option -
        Returns:
      • setTSD

        public void setTSD(double in)
      • setLRS

        public void setLRS(double[] in)
      • setLRB

        public void setLRB(double[] in)
      • setBtot

        public void setBtot(double in)
      • setStot

        public void setStot(double in)
      • setDtot

        public void setDtot(int in)
      • getStatistic

        public double getStatistic()
      • setTSB

        public void setTSB(double[] in)
      • setTSS

        public void setTSS(double[] in)
      • getExpectedStatistic_b

        public double getExpectedStatistic_b()
      • getExpectedCLb_sb

        public double getExpectedCLb_sb()
        Get the expected Confidence Level for the background only if there is signal and background.
        Returns:
      • getExpectedCLs_b

        public double getExpectedCLs_b(int sigma)
        Get getExpectedCLsb_b/getExpectedCLb_b
        Parameters:
        sigma -
        Returns:
      • getExpectedCLs_b

        public double getExpectedCLs_b()
        Get getExpectedCLsb_b/getExpectedCLb_b. These are indications of how well an experiment would do on average in excluding a signal if the signal truly is not present, and are the important figures of merit when optimizing an analysis for exclusion.
        Parameters:
        sigma -
        Returns:
      • getExpectedCLb_b

        public double getExpectedCLb_b()
        Get the expected Confidence Level for the background only if there is only background.These are indications of how well an experiment would do on average in excluding a signal if the signal truly is not present, and are the important figures of merit when optimizing an analysis for exclusion.
        Returns:
      • getExpectedCLsb_b

        public double getExpectedCLsb_b()
        Get the expected Confidence Level for the signal plus background hypothesis if there is only background.
        Returns:
      • getLNQb

        public H1D getLNQb(int bins,          double min,          double max)
        Get a histogram of a canonical -2lnQ plot for background hypothesis (full)
        Parameters:
        bins - number of bins
        min - min value
        max - max value
        Returns:
        histogram for -2lnQ plot for background
      • getLNQsb

        public H1D getLNQsb(int bins,           double min,           double max)
        Get a histogram of a canonical -2lnQ plot for for signal and background hypothesis
        Parameters:
        bins - number of bins
        min - min value
        max - max value
        Returns:
        histogram for -2lnQ plot for signal and background hypothesis
      • doc

        public void doc()
        Show online documentation.

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