Usage
fec_mcmc(fec, rawCounts = FALSE, f = 50, model = c("PoGa", "ZIPoGa")[1],
priors.mu = list(hyperpars = c(1, 0.001), proposalDist = "kl"),
priors.phi = list(hyperpars = c(1, 0.1), proposalDist = "unif", v = 0.5),
priors.psi = list(hyperpars = c(1, 1)), maxiter.pilot = 10,
nburnin = 1000, nsamples = 10000, thin = 1, initials = NULL,
verbose = TRUE, .verboselevel = 0, ...)Arguments
fec
vector with faecal egg counts
rawCounts
logical indicating whether fec corresponds to raw counts
(as counted on the McMaster slide), or to calculated EpGs (raw counts times correction factor).
Defaults to FALSE.
f
correction factor for the McMaster technique (e.g. 50). Either a number
or a vector with different correction factors for each FEC
model
either "PoGa" or "ZIPoGa"
priors.mu
list with hyper-prior/proposal information for $\mu$
priors.phi
list with hyper-prior/proposal information for $\phi$
priors.psi
list with hyper-prior information for $\psi$
maxiter.pilot
maximal number of tries to determine a good tuning value
for the proposal distribution for $\phi$
nburnin
number of burn-in iterations
nsamples
number of desired samples
initials
named list with starting values for the parameters
mu, phi, mui, y,psi
verbose
print progress information
.verboselevel
print additional information, mainly for
debugging information, larger values print more details
...
extra arguments (not used)