ciEX(n, alp, e)p (for all x = 0, 1, 2 ..n),
based on inverting
equal-tailed binomial tests with null hypothesis $$H0: p = p0$$ and calculated from the
cumulative binomial distribution. Exact two sided P-value is usually calculated as
$$P= 2[e*Pr(X = x) + min{(Pr(X < x), Pr(X > x))}]$$ where
probabilities are found at null value of p and \(0 <= e <= 1\).
The Confidence Interval of n given alp along with lower and upper abberation.[2] 1998 Agresti A and Coull BA. Approximate is better than "Exact" for interval estimation of binomial proportions. The American Statistician: 52; 119 - 126.
[3] 1998 Newcombe RG. Two-sided confidence intervals for the single proportion: Comparison of seven methods. Statistics in Medicine: 17; 857 - 872.
[4] 2001 Brown LD, Cai TT and DasGupta A. Interval estimation for a binomial proportion. Statistical Science: 16; 101 - 133.
[5] 2002 Pan W. Approximate confidence intervals for one proportion and difference of two proportions Computational Statistics and Data Analysis 40, 128, 143-157.
[6] 2008 Pires, A.M., Amado, C. Interval Estimators for a Binomial Proportion: Comparison of Twenty Methods. REVSTAT - Statistical Journal, 6, 165-197.
[7] 2014 Martin Andres, A. and Alvarez Hernandez, M. Two-tailed asymptotic inferences for a proportion. Journal of Applied Statistics, 41, 7, 1516-1529
prop.test and binom.test for equivalent base Stats R functionality,
binom.confint provides similar functionality for 11 methods,
wald2ci which provides multiple functions for CI calculation ,
binom.blaker.limits which calculates Blaker CI which is not covered here and
propCI which provides similar functionality.Other Basic methods of CI estimation: PlotciAS,
PlotciAllg, PlotciAll,
PlotciBA, PlotciEX,
PlotciLR, PlotciLT,
PlotciSC, PlotciTW,
PlotciWD, ciAS,
ciAll, ciBA,
ciLR, ciLT,
ciSC, ciTW,
ciWD
n=5; alp=0.05;e=0.5
ciEX(n,alp,e) #Mid-p
n=5; alp=0.05;e=1 #Clopper-Pearson
ciEX(n,alp,e)
n=5; alp=0.05;e=c(0.1,0.5,0.95,1) #Range including Mid-p and Clopper-Pearson
ciEX(n,alp,e)
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