ciAAll: CI estimation of 6 adjusted methods (Wald, Wald-T,...
In proportion: Inference on Single Binomial Proportion and Bayesian Computations

Description Usage Arguments Details Value References See Also Examples

View source: R/111.ConfidenceIntervals_ADJ_n.R

CI estimation of 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) given adding factor

1	ciAAll(n, alp, h)

`n`	- Number of trials
`alp`	- Alpha value (significance level required)
`h`	- adding factor

The Confidence Interval using 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) for n given alp and h

A dataframe with

`name`	- Name of the method
`x`	- Number of successes (positive samples)
`LLT`	- Lower limit
`ULT`	- Upper Limit
`LABB`	- Lower Abberation
`UABB`	- Upper Abberation
`ZWI`	- Zero Width Interval

[1] 1998 Agresti A and Coull BA. Approximate is better than "Exact" for interval estimation of binomial proportions. The American Statistician: 52; 119 - 126.

[2] 1998 Newcombe RG. Two-sided confidence intervals for the single proportion: Comparison of seven methods. Statistics in Medicine: 17; 857 - 872.

[3] 2008 Pires, A.M., Amado, C. Interval Estimators for a Binomial Proportion: Comparison of Twenty Methods. REVSTAT - Statistical Journal, 6, 165-197.

prop.test and binom.test for equivalent base Stats R functionality, binom.confint provides similar functionality for 11 methods, wald2ci which provides multiple functions for CI calculation , binom.blaker.limits which calculates Blaker CI which is not covered here and propCI which provides similar functionality.

Other Adjusted methods of CI estimation: PlotciAAS, PlotciAAllg, PlotciAAll, PlotciALR, PlotciALT, PlotciASC, PlotciATW, PlotciAWD, ciAAS, ciALR, ciALT, ciASC, ciATW, ciAWD