ssize.propCI {MKpower} | R Documentation |
Sample Size Calculation for Confidence Interval of a Proportion
Description
Compute the sample size for the two-sided confidence interval of a single proportion.
Usage
ssize.propCI(prop, width, conf.level = 0.95, method = "wald-cc")
Arguments
prop |
expected proportion |
width |
width of the confidence interval |
conf.level |
confidence level |
method |
method used to compute the confidence interval; see Details. |
Details
The computation is based on the asymptotic formulas provided in Section 2.5.2
of Fleiss et al. (2003). If method = "wald-cc"
a continuity correction
is applied.
There are also methods for Jeffreys, Clopper-Pearson, Wilson and the
Agresti-Coull interval; see also binomCI
.
Value
Object of class "power.htest"
, a list of the arguments
(including the computed one) augmented with method
and
note
elements.
Author(s)
Matthias Kohl Matthias.Kohl@stamats.de
References
J.L. Fleiss, B. Levin and M.C. Paik (2003). Statistical Methods for Rates and Proportions. Wiley Series in Probability and Statistics.
W.W. Piegorsch (2004). Sample sizes for improved binomial confidence intervals. Computational Statistics & Data Analysis, 46, 309-316.
M. Thulin (2014). The cost of using exact confidence intervals for a binomial proportion. Electronic Journal of Statistics, 8(1), 817-840.
See Also
Examples
ssize.propCI(prop = 0.1, width = 0.1)
ssize.propCI(prop = 0.3, width = 0.1)
ssize.propCI(prop = 0.3, width = 0.1, method = "wald")
ssize.propCI(prop = 0.3, width = 0.1, method = "jeffreys")
ssize.propCI(prop = 0.3, width = 0.1, method = "clopper-pearson")
ssize.propCI(prop = 0.3, width = 0.1, method = "wilson")
ssize.propCI(prop = 0.3, width = 0.1, method = "agresti-coull")