ciAWDx: Adjusted Wald method of CI estimation

Description Usage Arguments Details Value References See Also Examples

View source: R/113.ConfidenceIntervals_ADJ_n_x.R

Description

Adjusted Wald method of CI estimation

Usage

1
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

1
2
x= 5; n=5; alp=0.05; h=2
ciAWDx(x,n,alp,h)

Example output

  x     LAWDx UAWDx LABB UABB ZWI
1 5 0.5061662     1   NO  YES  NO

proportion documentation built on May 1, 2019, 7:54 p.m.