proportion (version 2.0.0)

covpAAll: Coverage Probability for 6 adjusted methods

Description

Coverage Probability for 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)

Usage

covpAAll(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

method

Method name

MeanCP

Coverage Probability

MinCP

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

Calculates the Coverage Probability for 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)

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, covpALR, covpALT, covpASC, covpATW, covpAWD

Examples

## Not run: ------------------------------------
# n= 10; alp=0.05; h=2;a=1;b=1; t1=0.93;t2=0.97
# covpAAll(n,alp,h,a,b,t1,t2)
## ---------------------------------------------