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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