proportion (version 2.0.0)

ciAWDx: 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

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

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.

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)