ciAAll: CI estimation of 6 adjusted methods (Wald, Wald-T,...

In RajeswaranV/proportion: Inference on Single Binomial Proportion and Bayesian Computations

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

Description

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

Usage

ciAAll(n, alp, h)

Arguments

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

Details

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

Value

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

References

[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.

See Also

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()

Examples

n=5; alp=0.05;h=2
ciAAll(n,alp,h)

RajeswaranV/proportion documentation built on June 17, 2022, 9:11 a.m.