# PlotpCOpBIEX: Plots of p-confidence and p-bias of Exact method given n and...

### Description

Plots of p-confidence and p-bias of Exact method given n and alpha level

### Usage

 `1` ```PlotpCOpBIEX(n, alp, e) ```

### Arguments

 `n` - Number of trials `alp` - Alpha value (significance level required) `e` - Exact method indicator in [0, 1] 1: Clopper Pearson, 0.5: Mid P The input can also be a range of values between 0 and 1.

### Details

Evaluation of Confidence interval for `p` based on inverting equal-tailed binomial tests with null hypothesis H0: p = p0 using p-confidence and p-bias for the n + 1 intervals

### Value

A dataframe with

x1

Number of successes (positive samples)

pconf

p-Confidence

pbias

p-Bias

### References

[1] 2005 Vos PW and Hudson S. Evaluation Criteria for Discrete Confidence Intervals: Beyond Coverage and Length. The American Statistician: 59; 137 - 142.

Other p-confidence and p-bias of base methods: `PlotpCOpBIAS`, `PlotpCOpBIAll`, `PlotpCOpBIBA`, `PlotpCOpBILR`, `PlotpCOpBILT`, `PlotpCOpBISC`, `PlotpCOpBITW`, `PlotpCOpBIWD`, `pCOpBIAS`, `pCOpBIAll`, `pCOpBIBA`, `pCOpBIEX`, `pCOpBILR`, `pCOpBILT`, `pCOpBISC`, `pCOpBITW`, `pCOpBIWD`

### Examples

 ```1 2 3 4 5 6``` ```n=5; alp=0.05;e=0.5; # Mid-p PlotpCOpBIEX(n,alp,e) n=5; alp=0.05;e=1; #Clopper-Pearson PlotpCOpBIEX(n,alp,e) n=5; alp=0.05;e=c(0.1,0.5,0.95,1); #Range including Mid-p and Clopper-Pearson PlotpCOpBIEX(n,alp,e) ```

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