Description Usage Arguments Details Value Author(s) References See Also Examples
Calculate a fixed width confidence interval for the the binomial proportion based on one observation from the binomial distribution
1 | binom.liubailey(x, n, d, lambda=0, conf.level=0.95)
|
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 |
d |
half width of the confidence interval |
lambda |
Shrinkage factor. |
The confidence interval is as suggested in equation (3.1) of Liu & Bailey (2002).
(C_n(p.hat)-d,C_n(p.hat)+d).
The exact form is as follows: Let z be the appropriate (1-\code{conf.level})/2 quantile of the standard normal distribution the interval with shrinkage towards 0.5 is given by:
(\hat{p}_l,\hat{p}_u) = \hat{p}_n + \frac{λ z^2 (0.5-\hat{p}_n)}{n+z^2} \pm d
The interval is then expanded to a full length of 2d using the following transformation:
\hat{p}_l^* = \max(0,\min( 1-2d, \hat{p}_l))
\hat{p}_u^* = \min(1,\max( 2d, \hat{p}_u))
As a consequence, the computed interval will always have length 2d.
If fixed length is a desired property of your CI then this is a way to
go. However, the Liu and Bailey (2002) confidence intervals can have a low
coverage coefficients when n is very small compared to
d. When using the sample size computation procedure in
ciss.liubailey
one however ensures that n is large enough
for the selected d to guarantee the required coverage
coefficient. Thus, one should use binom.liubailey
in connection
with ciss.liubailey
.
A data.frame containing the observed proportions and the lower and
upper bounds of the confidence interval. The style is similar
to the binom.confint
function of the binom
package
M. H<f6>hle
Liu, W. and Bailey, B.J.R. (2002), Sample size determination for constructing a constant width confidence interval for a binomial success probability. Statistics and Probability Letters, 56(1):1-5.
1 2 3 4 5 6 7 | binom.liubailey(x=0:20,n=20, d=0.1, lambda=0)
#Compute coverage of this interval
cov <- coverage( binom.liubailey, n=20, alpha=0.05, d=0.1, lambda=0,
p.grid=seq(0,1,length=1000))
plot(cov,type="l")
|
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