Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/binom.coverage.R
Determines the probability coverage for a binomial confidence interval.
1 | binom.coverage(p, n, conf.level = 0.95, method = "all", ...)
|
p |
The (true) probability of success in a binomial experiment. |
n |
Vector of number of independent trials in the binomial experiment. |
conf.level |
The level of confidence to be used in the confidence interval. |
method |
Either a character string to be passed to
|
... |
Additional parameters to be passed to
|
Derivations are based on the results given in the references. Methods
whose coverage probabilities are consistently closer to 0.95 are more
desireable. Thus, Wilson's, logit, and cloglog appear to be good for
this sample size, while Jeffreys, asymptotic, and prop.test are
poor. Jeffreys is a variation of Bayes using prior shape parameters of
0.5 and having equal probabilities in the tail. The Jeffreys'
equal-tailed interval was created using binom.bayes using (0.5,0.5) as
the prior shape parameters and type = "central"
.
A data.frame
containing the "method"
used, "n"
, "p"
,
and the coverage probability, C(p,n)
.
Sundar Dorai-Raj (sdorairaj@gmail.com)
L.D. Brown, T.T. Cai and A. DasGupta (2001), Interval estimation for a binomial proportion (with discussion), Statistical Science, 16:101-133.
L.D. Brown, T.T. Cai and A. DasGupta (2002), Confidence Intervals for a Binomial Proportion and Asymptotic Expansions, Annals of Statistics, 30:160-201.
1 | binom.coverage(p = 0.5, n = 50)
|
method p n coverage
1 agresti-coull 0.5 50 0.9350914
2 asymptotic 0.5 50 0.9350914
3 bayes 0.5 50 0.9350914
4 cloglog 0.5 50 0.9511261
5 exact 0.5 50 0.9671609
6 logit 0.5 50 0.9671609
7 probit 0.5 50 0.9350914
8 profile 0.5 50 0.9350914
9 lrt 0.5 50 0.9350914
10 prop.test 0.5 50 0.9671609
11 wilson 0.5 50 0.9350914
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