covpAAll: Coverage Probability for 6 adjusted methods (Wald, Wald-T,...

In proportion: Inference on Single Binomial Proportion and Bayesian Computations

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

Details

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

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

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)

## End(Not run)

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