ciAWD: Adjusted Wald method of CI estimation

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

Adjusted Wald method of CI estimation

Description

Adjusted Wald method of CI estimation

Usage

ciAWD(n, alp, h)

Arguments

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

Details

Given data x and n are modified as x + h and n + (2*h) respectively, where h > 0 then Wald-type interval is applied for all x = 0, 1, 2 ..n.

Value

A dataframe with

x	Number of successes (positive samples)
LAWD	Wald Lower limit
UAWD	Wald Upper Limit
LABB	Wald Lower Abberation
UABB	Wald 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(), ciAAll(), ciALR(), ciALT(), ciASC(), ciATW()

Examples

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

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