| Title: | Blyth-Still-Casella Exact Binomial Confidence Intervals |
|---|---|
| Description: | Computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986) <doi:10.2307/3314658>. |
| Authors: | Ron Yu [aut, cre], Peiwen Wu [aut] |
| Maintainer: | Ron Yu <[email protected]> |
| License: | GPL-3 |
| Version: | 1.1.0 |
| Built: | 2024-12-19 06:39:57 UTC |
| Source: | CRAN |
blyth.still.casella() computes Blyth-Still-Casella exact binomial confidence intervals based on a refining procedure proposed by George Casella (1986).
blyth.still.casella( n, X = NULL, alpha = 0.05, digits = 2, CIs.init = NULL, additional.info = FALSE )blyth.still.casella( n, X = NULL, alpha = 0.05, digits = 2, CIs.init = NULL, additional.info = FALSE )
n |
number of trials |
X |
number of successes (optional) |
alpha |
confidence level = 1 - alpha |
digits |
number of significant digits after the decimal point |
CIs.init |
initial confidence intervals from which the refinement procedure begins (default starts from Clopper-Pearson confidence intervals) |
additional.info |
additional information about the types of interval endpoints and their possible range is provided if TRUE (default = FALSE) |
If X is specified, the corresponding confidence interval will be returned, otherwise a list of n + 1 confidence intervals will be returned.
If additional.info = FALSE, only a list of confidence interval(s) will be returned. For any conincidental endpoint, midpoint of its range will be displayed.
If additional.info = TRUE, the following lists will be returned:
ConfidenceInterval |
a list of confidence intervals |
CoincidenceEndpoint |
indices of coincidental lower endpoints (L.Index) and their corresponding upper endpoints (U.index) |
Range |
range for each endpoint |
# to obtain 95% CIs for n = 30 and X = 0 to 30 blyth.still.casella(n = 30, alpha = 0.05, digits = 4) # to obtain 90% CIs, endpoint types, indices of coincidental endpoints (if any), # and range of each endpoint for n = 30 and X = 23 blyth.still.casella(n = 30, X = 23, alpha = 0.05, digits = 4, additional.info = TRUE) # use initial confidence intervals defined by the user instead of Clopper-Pearson CIs # CIs.input needs to be a (n + 1) x 2 matrix with sufficient coverage CIs.input <- matrix(c(0,1), nrow = 11, ncol = 2, byrow = TRUE) # start with [0,1] intervals blyth.still.casella(n = 10, alpha = 0.05, digits = 4, CIs.init = CIs.input, additional.info = TRUE)# to obtain 95% CIs for n = 30 and X = 0 to 30 blyth.still.casella(n = 30, alpha = 0.05, digits = 4) # to obtain 90% CIs, endpoint types, indices of coincidental endpoints (if any), # and range of each endpoint for n = 30 and X = 23 blyth.still.casella(n = 30, X = 23, alpha = 0.05, digits = 4, additional.info = TRUE) # use initial confidence intervals defined by the user instead of Clopper-Pearson CIs # CIs.input needs to be a (n + 1) x 2 matrix with sufficient coverage CIs.input <- matrix(c(0,1), nrow = 11, ncol = 2, byrow = TRUE) # start with [0,1] intervals blyth.still.casella(n = 10, alpha = 0.05, digits = 4, CIs.init = CIs.input, additional.info = TRUE)