ConfidenceLevel

java.lang.Object
- jhplot.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

Constructors
Constructor and Description
`ConfidenceLevel()` Default constructor.
`ConfidenceLevel(int mc)` Construct ConfLevel
`ConfidenceLevel(int mc, boolean onesided)` Build confidence level.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`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.

Class ConfidenceLevel

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

ConfidenceLevel

ConfidenceLevel

ConfidenceLevel

Method Detail

getExpectedStatistic_b

getExpectedStatistic_sb

getCLb

getCLb

getCLsb

getCLsb

getCLs

getCLs

getExpectedCLsb_b

getExpectedCLb_sb

getExpectedCLb_b

getAverageCLsb

getAverageCLs

get3sProbability

get5sProbability

getResults

setTSD

setLRS

setLRB

setBtot

setStot

setDtot

getStatistic

setTSB

setTSS

getExpectedStatistic_b

getExpectedCLb_sb

getExpectedCLs_b

getExpectedCLs_b

getExpectedCLb_b

getExpectedCLsb_b

getLNQb

getLNQsb

doc