View source: R/221.CoverageProb_CC_All.R
covpCWD | R Documentation |
Coverage Probability of Continuity corrected Wald method
covpCWD(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 |
Evaluation of Wald-type interval with continuity correction using coverage probability, root mean square statistic, and the proportion of proportion lies within the desired level of coverage
A dataframe with
mcpCW |
Continuity corrected Wald Coverage Probability |
micpCW |
Continuity corrected Wald 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()
,
covpCAll()
,
covpCLT()
,
covpCSC()
,
covpCTW()
n= 10; alp=0.05; c=1/(2*n);a=1;b=1; t1=0.93;t2=0.97 covpCWD(n,alp,c,a,b,t1,t2)
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