Coverage Probability of Adjusted ArcSine method for given n

Description

Coverage Probability of Adjusted ArcSine method for given n

Usage

1
covpAAS(n, alp, h, a, b, t1, t2)

Arguments

n

- Number of trials

alp

- Alpha value (significance level required)

h

- Adding factor

a

- Beta parameters for hypo "p"

b

- Beta parameters for hypo "p"

t1

- Lower tolerance limit to check the spread of coverage Probability

t2

- Upper tolerance limit to check the spread of coverage Probability

Details

Evaluation of adjusted Wald-type interval for the arcsine transformation of the parameter p using coverage probability, root mean square statistic, and the proportion of proportion lies within the desired level of coverage

Value

A dataframe with

mcpAA

Adjusted ArcSine Coverage Probability

micpAA

Adjusted ArcSine minimum coverage probability

RMSE_N

Root Mean Square Error from nominal size

RMSE_M

Root Mean Square Error for Coverage Probability

RMSE_MI

Root Mean Square Error for minimum coverage probability

tol

Required tolerance for coverage probability

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

Other Coverage probability of adjusted methods: PlotcovpAAS, PlotcovpAAll, PlotcovpALR, PlotcovpALT, PlotcovpASC, PlotcovpATW, PlotcovpAWD, covpAAll, covpALR, covpALT, covpASC, covpATW, covpAWD

Examples

1
2
n= 10; alp=0.05; h=2; a=1;b=1; t1=0.93;t2=0.97
covpAAS(n,alp,h,a,b,t1,t2)

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