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java.lang.Objectjhpro.stat.ConfidenceLevel
public class ConfidenceLevel
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 | |
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ConfidenceLevel()
Default constructor. |
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ConfidenceLevel(int mc)
Construct ConfLevel |
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ConfidenceLevel(int mc,
boolean onesided)
Build confidence level. |
Method Summary | |
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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()
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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 |
java.util.ArrayList<H1D> |
getResults(java.lang.String Option)
Display sort of a "canonical" -2lnQ plot. |
double |
getStatistic()
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void |
setBtot(double in)
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void |
setDtot(int in)
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void |
setLRB(double[] in)
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void |
setLRS(double[] in)
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void |
setStot(double in)
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void |
setTSB(double[] in)
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void |
setTSD(double in)
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void |
setTSS(double[] in)
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Methods inherited from class java.lang.Object |
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equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
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public ConfidenceLevel()
public ConfidenceLevel(int mc)
mc
- number of MonteCarlo experimentspublic ConfidenceLevel(int mc, boolean onesided)
mc
- is the number of Monte Carlo experimentsonesided
- specifies if the intervals are one-sided or not.Method Detail |
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public double getExpectedStatistic_b(int sigma)
sigma
- between -2 and 2
public double getExpectedStatistic_sb(int sigma)
sigma
-
public double getCLb()
public double getCLb(boolean use_sMC)
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.
use_sMC
-
public double getCLsb()
public double getCLsb(boolean use_sMC)
use_sMC
-
public double getCLs()
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.
public double getCLs(boolean use_sMC)
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.
use_sMC
- use or not MC.
public double getExpectedCLsb_b(int sigma)
sigma
-
public double getExpectedCLb_sb(int sigma)
sigma
-
public double getExpectedCLb_b(int sigma)
sigma
-
public double getAverageCLsb()
public double getAverageCLs()
public double get3sProbability()
public double get5sProbability()
public java.util.ArrayList<H1D> getResults(java.lang.String Option)
Option
-
public void setTSD(double in)
public void setLRS(double[] in)
public void setLRB(double[] in)
public void setBtot(double in)
public void setStot(double in)
public void setDtot(int in)
public double getStatistic()
public void setTSB(double[] in)
public void setTSS(double[] in)
public double getExpectedStatistic_b()
public double getExpectedCLb_sb()
public double getExpectedCLs_b(int sigma)
sigma
-
public double getExpectedCLs_b()
sigma
-
public double getExpectedCLb_b()
public double getExpectedCLsb_b()
public H1D getLNQb(int bins, double min, double max)
bins
- number of binsmin
- min valuemax
- max value
public H1D getLNQsb(int bins, double min, double max)
bins
- number of binsmin
- min valuemax
- max value
public void doc()
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