binom.optim {binom}  R Documentation 
Uses optimization to minimize the integrated mean squared error between the calculated coverage and the desired confidence level for a given binomial confidence interval.
binom.optim(n, conf.level = 0.95, method = binom.lrt, k = n%/%2 + 1, p0 = 0, transform = TRUE, plot = FALSE, tol = .Machine$double.eps^0.5, start = NULL, ...)
n 
The number of independent trials in the binomial experiment. 
conf.level 
The level of confidence to be used in the confidence interval. 
method 
The method used to estimate the confidence interval. 
k 
See Details. 
p0 
The minimum probability of success to allow in the optimization. See Details. 
transform 
logical; If 
plot 
logical; If 
tol 
The minimum significance level to allow in the optimization. See Details. 
start 
A starting value on the optimal confidence level. 
... 
Additional arguments to pass to 
This function minimizes the squared error between the expected coverage probability and the desired confidence level.
alpha[opt]=argmin[alpha] integral[C(p,n)(1alpha)]^2dp
The optimizer will adjust confidence intervals for all x
=
0
to n
depending on the value of k
provided. If
k
is one, only the confidence levels for x
= 0
and
n
are adjusted. If k
= [n/2]
then all confidence
intervals are adjusted. This assumes the confidence intervals are the
same length for x
= x[k]
and x[n  k + 1]
, which is
the case for all methods provided in this package except
binom.cloglog
.
A list
with the following elements:
par 
Final confidence levels. The length of this vector is

value 
The final minimized value from 
counts 
The number of function and gradient calls from

convergence 
Convergence code from 
message 
Any message returned by the LBFGSB or BFGS optimizer. 
confint 
A 
Sundar DoraiRaj (sdorairaj@gmail.com)
binom.confint
, binom.plot
,
binom.coverage
, optim
binom.optim(10, k = 1) ## determine optimal significance for x = 0, 10 only binom.optim(3, method = binom.wilson) ## determine optimal significance for all x