binom.profile(x, n, conf.level = 0.95, maxsteps = 50,
del = zmax/5, bayes = TRUE, plot = FALSE, ...)
TRUE
use a Bayesian correction at the
edges.TRUE
plot the profile with a
spline
fit.data.frame
containing the observed
proportions and the lower and upper bounds of the confidence
interval.x == 0
or x == n
and
bayes
is TRUE
, then a Bayesian adjustment is made to move
the log-likelihood function away from Inf
. Specifically, these
values are replaced by (x + 0.5)/(n + 1)
, which is the posterier
mode of f(p|x)
using Jeffrey's prior on p
. Typically, the
observed mean will not be inside the estimated confidence interval.
If bayes
is FALSE
, then the Clopper-Pearson exact method
is used on the endpoints. This tends to make confidence intervals at the
end too conservative, though the observed mean is guaranteed to be
within the estimated confidence limits.binom.confint
, binom.bayes
, binom.cloglog
,
binom.logit
, binom.probit
, binom.coverage
,
confint
in package MASS,
family
, glm
binom.profile(x = 0:10, n = 10)
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