proportion (version 2.0.0)

covpAWD: Coverage Probability of Adjusted Wald method for given n

Description

Coverage Probability of Adjusted Wald method for given n

Usage

covpAWD(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

Value

A dataframe with

mcpAW

Adjusted Wald Coverage Probability

micpAW

Adjusted Wald 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

Details

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

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, covpAAS, covpAAll, covpALR, covpALT, covpASC, covpATW

Examples

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