binom.confint(x, n, conf.level = 0.95, methods = "all", ...)
c("exact", "ac", "asymptotic", "wilson",
"prop.test", "bayes", "logit", "cloglog", "probit")
is allowed. Default is
"all"
.binom.bayes
.data.frame
containing the observed proportions and
the lower and upper bounds of the confidence interval for all the
methods in "methods"
.(1-alpha/2)^n
exact
asymptotic
agresti-coull
wilson
prop.test
prop.test(x = x, n = n, conf.level = conf.level)$conf.int
.}
bayes
binom.bayes
.}
logit
binom.logit
.}
cloglog
binom.cloglog
.}
probit
binom.probit
.}
profile
binom.profile
.}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.confint(x = c(2, 4), n = 100, tol = 1e-8)
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