View source: R/212.CoverageProb_ADJ_All.R
| covpAAll | R Documentation | 
Coverage Probability for 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)
covpAAll(n, alp, h, a, b, t1, t2)
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  | 
Calculates the Coverage Probability for 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine)
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  | 
[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.
Other Coverage probability of adjusted methods: 
PlotcovpAAS(),
PlotcovpAAll(),
PlotcovpALR(),
PlotcovpALT(),
PlotcovpASC(),
PlotcovpATW(),
PlotcovpAWD(),
covpAAS(),
covpALR(),
covpALT(),
covpASC(),
covpATW(),
covpAWD()
## 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)
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