ciAAllx: CI estimation of 6 adjusted methods (Wald, Wald-T,...
In proportion: Inference on Single Binomial Proportion and Bayesian Computations

Description Usage Arguments Details Value References See Also Examples

View source: R/113.ConfidenceIntervals_ADJ_n_x.R

CI estimation of 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) given x and n

1	ciAAllx(x, n, alp, h)

`x`	- Number of success
`n`	- Number of trials
`alp`	- Alpha value (significance level required)
`h`	- Adding factor

The Confidence Interval of using 6 adjusted methods (Wald, Wald-T, Likelihood, Score, Logit-Wald, ArcSine) for n given alp, x and h

A dataframe with

`name`	- Name of the method
`x`	- Number of successes (positive samples)
`LLT`	- Lower limit
`ULT`	- Upper Limit
`LABB`	- Lower Abberation
`UABB`	- 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 Adjusted methods of CI estimation given x & n: PlotciAAllx, ciAASx, ciALRx, ciALTx, ciASCx, ciATWx, ciAWDx

1 2	x=5; n=5; alp=0.05;h=2 ciAAllx(x,n,alp,h)

          method x LowerLimit UpperLimit LowerAbb UpperAbb ZWI
1       Adj-Wald 5  0.5061662  1.0000000       NO      YES  NO
2    Adj-ArcSine 5  0.4678770  0.9732727       NO       NO  NO
3  Adj-Liklihood 5  0.4582767  0.9591679       NO       NO  NO
4      Adj-Score 5  0.4525890  0.9367749       NO       NO  NO
5 Adj-Logit Wald 5  0.4209916  0.9439712       NO       NO  NO
6     Adj-Wald-T 5  0.4733987  1.0000000       NO      YES  NO