ratesci (version 0.2-0)

jeffreysci: Jeffreys and other approximate Bayesian confidence intervals for a single

Description

Generalised approximate Bayesian confidence intervals based on a Beta (for binomial rates) or Gamma (for Poisson rates) conjugate priors. Encompassing the Jeffreys method (with Beta(0.5, 0.5) or Gamma(0.5) respectively), as well as any user-specified prior distribution. Clopper-Pearson also included by way of a "continuity correction".

Usage

jeffreysci(x, n, ai = 0.5, bi = 0.5, cc = 0, level = 0.95,
  distrib = "bin", adj = TRUE, ...)

Arguments

x

Numeric vector of number of events.

n

Numeric vector of sample sizes (for binomial rates) or exposure times (for Poisson rates).

ai, bi

Numbers defining the Beta prior distribution (default ai = bi = 0.5 for Jeffreys interval). Gamma prior for Poisson rates requires only ai.

cc

Number or logical specifying (amount of) "continuity correction". cc = 0 (default) gives Jeffreys interval, cc = 0.5 gives the Clopper-Pearson interval. A value between 0 and 0.5 allows a compromise between proximate and conservative coverage.

level

Number specifying confidence level (between 0 and 1, default 0.95).

distrib

Character string indicating distribution assumed for the input data: "bin" = binomial (default), "poi" = Poisson.

adj

Logical (default TRUE) indicating whether to apply the boundary adjustment recommended on p108 of Brown et al. (set to FALSE if informative priors are used)

...

Other arguments.

Examples

  #Jeffreys method:
  jeffreysci(x = 5, n = 56)