Description Usage Arguments Details Value
This function takes user input to create the list of prior paremters necessary for
Bayesian inference of aggregated trends. The prior parameter list is a necessary argument for
the mcmc.aggregate
function.
1 | defaultPriorList(trend.limit = NULL, model.data, gamma.mean, gamma.prec)
|
trend.limit |
Bounds on the individual site trends. |
model.data |
A data set which provides the individual site models used in |
gamma.mean |
Prior mean of the (multivariate) normal prior distribution for gamma |
gamma.prec |
Precision (inverse of variance) of the gamma prior distribution |
Using the site model data set model.data
and a soft bound on the individual site
trends (trend.limit
) this function creates a sensible default for the list of prior
parameters necessary for Bayesian inference of aggregated trends. The value of
trend.limit
should be in terms of
of 20 percent growth at each individual site). The lower bound is calculated as
1/(1+trend.limit). This is the ower bound that is symmetric about zero on the log-scale.
For trend.limit=0.2
, the lower bound is approximately -0.17.
Specifically, the prior distribution for the linear trend parameters (beta)
in log abundance at each site is set to a bivariate normal with mean m.i=c(0,0)
and precision matrix Q.i=diag(c(0,q))
, where q
is chosen such that
Pr[1/(1+trend.limit) < exp(beta[2])-1 < trend.limit] = 0.95.
A named list with entries for each of the parameters for which prior specification is necessary.
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