CI estimation of 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) given x and n

Description

CI estimation of 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) given x and n

Usage

1
ciAAllx(x, n, alp, h)

Arguments

x

- Number of success

n

- Number of trials

alp

- Alpha value (significance level required)

h

- Adding factor

Details

The Confidence Interval of using 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) for n given alp, x and h

Value

A dataframe with

name

- Name of the method

x

- Number of successes (positive samples)

LLT

- Lower limit

ULT

- Upper Limit

LABB

- Lower Abberation

UABB

- 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, ciALRx, ciALTx, ciASCx, ciATWx, ciAWDx

Examples

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

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