View source: R/clopper.pearson.ci.R
clopper.pearson.ci | R Documentation |
Computing upper, lower or two-sided Clopper-Pearson confidence limits for a given confidence level.
clopper.pearson.ci(k, n, alpha = 0.1, CI = "upper")
k |
number of failures/successes. |
n |
number of trials. |
alpha |
significance level for the (1-alpha)* 100% confidence level (default alpha=0.1). |
CI |
indicates the kind of the confidence interval, options: "upper" (default), "lower", "two.sided". |
Computes the confidence limits for the p of a binomial distribution. Confidence intervals are obtained by the definition of Clopper and Pearson. The two-sided interval for k=0 is (0,1-(alpha/2)^(1/n)), for k=n it is ((alpha/2)^(1/n),1).
A data frame containing the kind of the confidence interval, upper and lower limits and the used significance level alpha
.
D.Kurz, H.Lewitschnig, J.Pilz, Decision-Theoretical Model for Failures which are Tackled by Countermeasures, IEEE Transactions on Reliability, Vol. 63, No. 2, June 2014.
Thulin, M., The cost of using exact confidence intervals for a binomial proportion, Electronic Journal of Statistics, vol. 8, pp. 817-840, 2014
C.J.Clopper and E.S. Pearson, The use of confidence or fiducial limits illustrated in the case of the binomial, Biometrika, vol. 26, pp. 404-413, 1934.
clopper.pearson.ci(5,100000,alpha=0.05) # Confidence.Interval = upper # Lower.limit = 0 # Upper.limit = 0.0001051275 # alpha = 0.05 clopper.pearson.ci(5,100000,CI="two.sided") # Confidence.Interval = two.sided # Lower.limit = 1.97017e-05 # Upper.limit = 0.0001051275 # alpha = 0.1
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