View source: R/221.CoverageProb_CC_All.R
covpCAll | R Documentation |
Coverage Probability for 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine)
covpCAll(n, alp, c, a, b, t1, t2)
n |
- Number of trials |
alp |
- Alpha value (significance level required) |
c |
- Continiuty correction |
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 |
The Coverage Probability of 5 continuity corrected methods (Wald, Wald-T, Score, Logit-Wald, ArcSine) for n
given alp
, h
, a
, b
, t1
and t2
using all the methods
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 for continuity corrected methods:
PlotcovpCAS()
,
PlotcovpCAll()
,
PlotcovpCLT()
,
PlotcovpCSC()
,
PlotcovpCTW()
,
PlotcovpCWD()
,
covpCAS()
,
covpCLT()
,
covpCSC()
,
covpCTW()
,
covpCWD()
## Not run: n= 10; alp=0.05; c=1/(2*n);a=1;b=1; t1=0.93;t2=0.97 covpCAll(n,alp,c,a,b,t1,t2) ## End(Not run)
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