addz2ci: Agresti-Coull CI for a binomial proportion based on adding...
In PropCIs: Various Confidence Interval Methods for Proportions
Description
Usage
Arguments
Value
References
Examples
Description
Agresti-Coull CI for a binomial proportion based on adding z^2/2
successes and z^2/2 failures before computing the Wald CI. The CI is
truncated, when it overshoots the boundary.
Usage
1
addz2ci(x, n, conf.level)
Arguments
x
number of successes
n
number of trials
conf.level
confidence coefficient
Value
A list with class '"htest"' containing the following components:
conf.int
The confidence intervall for the proportion
estimate
The estimator for the proportion
References
Agresti, A., Coull, B. (1998): Approximate is better than exact for
interval estimation of binomial proportions. The American
Statistician 52, 119–126.
Examples
1
addz2ci(x = 15, n = 112, conf.level = 0.95)
PropCIs documentation built on May 2, 2019, 5:49 a.m.
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Description Usage Arguments Value References Examples
Description
Agresti-Coull CI for a binomial proportion based on adding z^2/2 successes and z^2/2 failures before computing the Wald CI. The CI is truncated, when it overshoots the boundary.
Usage
1 | addz2ci(x, n, conf.level)
|
Arguments
x |
number of successes |
n |
number of trials |
conf.level |
confidence coefficient |
Value
A list with class '"htest"' containing the following components:
conf.int |
The confidence intervall for the proportion |
estimate |
The estimator for the proportion |
References
Agresti, A., Coull, B. (1998): Approximate is better than exact for interval estimation of binomial proportions. The American Statistician 52, 119–126.
Examples
1 | addz2ci(x = 15, n = 112, conf.level = 0.95)
|
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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