poisTest: Poisson Test

In abnormally-distributed/Bayezilla: Bayesian Models With JAGS and Several Utilities for Data Exploration and Model Evaluation.

Description

Poisson Test

Usage

poisTest(x, t = c(1, 1), r = 1, shra = c(1e-06, 1e-06),
  one.sample = TRUE, iter = 10000, warmup = 2500, adapt = 2500,
  chains = 4, thin = 3, method = "parallel", cl = makeCluster(2),
  summarise = FALSE, ...)

Arguments

x	number of events. A vector of length one or two.
t	time base for event count. A vector of length one or two.
r	comparison value (only applies to one sample) on scale of lambda. Ie, on the scale of lambda_raw/t. Defaults to 1 and should typically stay 1 unless you have a specific hypothesis.
shra	the shape and rate parameters of the gamma prior. Defaults to c(1e-6, 1e-6) which is non-informative and yields a posterior mean typically equal to the maximum likelihood estimate.
one.sample	Defaults to TRUE
iter	the number of iterations. defaults to 10000.
warmup	number of burnin samples. defaults to 2500.
adapt	number of adaptation steps. defaults to 2500.
chains	number of chains. defaults to 4.
thin	the thinning interval. defaults to 3.
method	Defaults to "parallel". For an alternative parallel option, choose "rjparallel" or. Otherwise, "rjags" (single core run).
cl	Use parallel::makeCluster(# clusters) to specify clusters for the parallel methods. Defaults to two cores.
...	other arguments to run.jags

Value

an rjags object

Examples

poisTest()

abnormally-distributed/Bayezilla documentation built on Oct. 31, 2019, 1:57 a.m.