View source: R/123.ConfidenceIntervals_CC_n_x.R
| ciCTWx | R Documentation | 
Continuity corrected Wald-T method of CI estimation
ciCTWx(x, n, alp, c)
| x | - Number of successes | 
| n | - Number of trials | 
| alp | - Alpha value (significance level required) | 
| c | - Continuity correction | 
Approximate method based on a t_approximation of the standardized point estimator using the test statistic (abs(phat-p)-c)/SE where c > 0 is a constant for continuity correction for all x = 0, 1, 2 ..n. Boundary modifications when x = 0 or x = n using Wald adjustment method with h = 2.
A dataframe with
| x | Number of successes (positive samples) | 
| LCTWx  | T-Wald Lower limit | 
| UCTWx  | T-Wald Upper Limit | 
| LABB  | T-Wald Lower Abberation | 
| UABB  | T-Wald Upper Abberation | 
| ZWI  | Zero Width Interval | 
[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.
prop.test and binom.test for equivalent base Stats R functionality,
binom.confint  provides similar functionality for 11 methods,
wald2ci which provides multiple functions for CI calculation ,
binom.blaker.limits which calculates Blaker CI which is not covered here and
propCI which provides similar functionality.
Other Continuity correction methods of CI estimation given x and n: 
PlotciCAllxg(),
PlotciCAllx(),
ciCAllx(),
ciCLTx(),
ciCSCx(),
ciCWDx()
x=5; n=5; alp=0.05;c=1/2*n ciCTWx(x,n,alp,c)
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