binom.lrt {binom} | R Documentation |
Uses the lrt likelihood on the observed proportion to construct confidence intervals.
binom.lrt(x, n, conf.level = 0.95, bayes = FALSE, conf.adj = FALSE, plot = FALSE, ...)
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. |
bayes |
logical; if TRUE use a Bayesian correction at the
edges. Specfically, a beta prior with shape parameters 0.5 is used.
If bayes is numeric, it is assumed to be the parameters
to beta distribution. |
conf.adj |
logical; if TRUE 0 or 100% successes return a
one-sided confidence interval |
plot |
logical; if TRUE a plot showing the the square root
of the binomial deviance with reference lines for mean, lower, and
upper bounds. This argument can also be a list of plotting parameters
to be passed to xyplot . |
... |
ignored |
Confidence intervals are based on profiling the binomial deviance in the
neighbourhood of the MLE. If 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
. Furthermore, if
conf.adj
is TRUE
, then the upper (or lower) bound uses
a 1 - alpha
confidence level. 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.
A data.frame
containing the observed
proportions and the lower and upper bounds of the confidence
interval.
Sundar Dorai-Raj (sdorairaj@gmail.com)
binom.confint
, binom.bayes
, binom.cloglog
,
binom.logit
, binom.probit
, binom.coverage
,
confint
in package MASS,
family
, glm
binom.lrt(x = 0:10, n = 10)