View source: R/121.ConfidenceIntervals_CC_n.R
| ciCLT | R Documentation | 
Continuity corrected Logit Wald method of CI estimation
ciCLT(n, alp, c)
| n | - Number of trials | 
| alp | - Alpha value (significance level required) | 
| c | - Continuity correction | 
Wald-type interval for the logit transformation of the parameter p
using the test statistic
(abs(L(phat)-L(p))-c)/SE
where c > 0 is a constant for continuity correction and L(y) = log(y/1-y)
for all x = 0, 1, 2 ..n. Boundary modifications when x = 0 or x = n
using Exact method values.
A dataframe with
| x | Number of successes (positive samples) | 
| LCLT  | Logit Wald Lower limit | 
| UCLT  | Logit Wald Upper Limit | 
| LABB  | Logit Wald Lower Abberation | 
| UABB  | Logit 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: 
PlotciCAS(),
PlotciCAllg(),
PlotciCAll(),
PlotciCLT(),
PlotciCSC(),
PlotciCTW(),
PlotciCWD(),
ciCAS(),
ciCAll(),
ciCSC(),
ciCTW(),
ciCWD()
n=5; alp=0.05;c=1/(2*n) ciCLT(n,alp,c)
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