ciAWDx: 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

ciAWDx(x, n, alp, h)

Arguments

x	- Number of successes
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 the given x and n.

Value

A dataframe with

x	Number of successes (positive samples)
LAWDx	Adjusted Wald Lower limit
UAWDx	Adjusted Wald Upper Limit
LABB	Adjusted Wald Lower Abberation
UABB	Adjusted 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 given x & n: PlotciAAllx(), ciAASx(), ciAAllx(), ciALRx(), ciALTx(), ciASCx(), ciATWx()

Examples

x= 5; n=5; alp=0.05; h=2
ciAWDx(x,n,alp,h)

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