View source: R/binom.confint.R
binom.confint | R Documentation |
Uses eight different methods to obtain a confidence interval on the binomial probability.
binom.confint(x, n, conf.level = 0.95, methods = "all", ...)
x |
Vector of number of successes in the 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. |
methods |
Which method to use to construct the interval. Any
combination of |
... |
Additional arguments to be passed to |
Nine methods are allowed for constructing the confidence interval(s):
exact
- Pearson-Klopper method. See also
binom.test
.
asymptotic
- the text-book definition for confidence
limits on a single proportion using the Central Limit Theorem.
agresti-coull
- Agresti-Coull method. For a 95% confidence
interval, this method does not use the concept of "adding 2
successes and 2 failures," but rather uses the formulas explicitly
described in the following link:
http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Agresti-Coull_Interval.
wilson
- Wilson method.
prop.test
- equivalent to prop.test(x = x, n = n,
conf.level = conf.level)$conf.int
.
bayes
- see binom.bayes
.
logit
- see binom.logit
.
cloglog
- see binom.cloglog
.
probit
- see binom.probit
.
profile
- see binom.profile
.
By default all eight are estimated for each value of x
and/or
n
. For the "logit", "cloglog", "probit", and "profile"
methods, the cases where x == 0
or x == n
are treated
separately. Specifically, the lower bound is replaced by
(alpha/2)^n
and the upper bound is replaced by (1-alpha/2)^n
.
A data.frame
containing the observed proportions and
the lower and upper bounds of the confidence interval for all the
methods in "methods"
.
Sundar Dorai-Raj (sdorairaj@gmail.com)
A. Agresti and B.A. Coull (1998), Approximate is better than "exact" for interval estimation of binomial proportions, American Statistician, 52:119-126.
R.G. Newcombe, Logit confidence intervals and the inverse sinh transformation (2001), American Statistician, 55:200-202.
L.D. Brown, T.T. Cai and A. DasGupta (2001), Interval estimation for a binomial proportion (with discussion), Statistical Science, 16:101-133.
Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (1997) Bayesian Data Analysis, London, U.K.: Chapman and Hall.
binom.bayes
, binom.logit
,
binom.probit
, binom.cloglog
,
binom.coverage
, prop.test
,
binom.test
for comparison to method
"exact"
binom.confint(x = c(2, 4), n = 100, tol = 1e-8)
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