proportion (version 2.0.0)

ciATWx: AdjustedWALD-T method of CI estimation

Description

AdjustedWALD-T method of CI estimation

Usage

ciATWx(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)

LATWx

T-Wald Lower limit

UATWx

T-Wald Upper Limit

LABB

T-Wald Lower Abberation

UABB

T-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 approximate method based on a t_approximation of the standardized point estimator 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, ciAWDx

Examples

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