jhpro.stat
Class ConfidenceLevel

java.lang.Object
  extended by jhpro.stat.ConfidenceLevel

public class ConfidenceLevel
extends 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 “five sigma,” including just one side of a Gaussian tail or both. A “three sigma” 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,


Constructor Summary
ConfidenceLevel()
          Default constructor.
ConfidenceLevel(int mc)
          Construct ConfLevel
ConfidenceLevel(int mc, boolean onesided)
          Build confidence level.
 
Method Summary
 void doc()
          Show online documentation.
 double get3sProbability()
          Get 3s probability.
 double get5sProbability()
          Get 5s probability.
 double getAverageCLs()
          Get average CLs.
 double getAverageCLsb()
          Get average CLsb.
 double getCLb()
          Get the Confidence Level for the background only.
 double getCLb(boolean use_sMC)
          Get the Confidence Level for the background only.
 double getCLs()
          Get the Confidence Level defined by CLs = CLsb/CLb.
 double getCLs(boolean use_sMC)
          Get the Confidence Level defined by CLs = CLsb/CLb.
 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)
 double getCLsb(boolean use_sMC)
          Get the Confidence Level for the signal plus background hypothesis
 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.
 double getExpectedCLb_b(int sigma)
          Get the expected Confidence Level for the background only if there is only background.
 double getExpectedCLb_sb()
          Get the expected Confidence Level for the background only if there is signal and background.
 double getExpectedCLb_sb(int sigma)
          Get the expected Confidence Level for the background only if there is signal and background.
 double getExpectedCLs_b()
          Get getExpectedCLsb_b/getExpectedCLb_b.
 double getExpectedCLs_b(int sigma)
          Get getExpectedCLsb_b/getExpectedCLb_b
 double getExpectedCLsb_b()
          Get the expected Confidence Level for the signal plus background hypothesis if there is only background.
 double getExpectedCLsb_b(int sigma)
          Get the expected Confidence Level for the signal plus background hypothesis if there is only background.
 double getExpectedStatistic_b()
           
 double getExpectedStatistic_b(int sigma)
          Get the expected statistic value in the background only hypothesis
 double getExpectedStatistic_sb(int sigma)
          Get the expected statistic value in the signal plus background hypothesis
 H1D getLNQb(int bins, double min, double max)
          Get a histogram of a canonical -2lnQ plot for background hypothesis (full)
 H1D getLNQsb(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.
 double getStatistic()
           
 void setBtot(double in)
           
 void setDtot(int in)
           
 void setLRB(double[] in)
           
 void setLRS(double[] in)
           
 void setStot(double in)
           
 void setTSB(double[] in)
           
 void setTSD(double in)
           
 void setTSS(double[] in)
           
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait
 

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 “five sigma,” including just one side of a Gaussian tail or both. A “three sigma” 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 “five sigma,” including just one side of a Gaussian tail or both. A “three sigma” 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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