Description Usage Arguments Details Value See Also Examples
Summarises and plots the prior distribution for each parameter in the FCS2 model. This can be used to aid the selection of parameters for each prior.
1 |
parameters |
a named list of named vectors, each specifying the
parameter values for a single prior distribution. See ‘Details’ for
how these are specified. Alternatively, an |
print |
whether to print a summary of each prior distribution. |
plot |
whether to plot each prior distribution. |
prob |
a decimal between 0 and 1 giving the size of the confidence
interval displayed for each prior (if |
... |
further arguments passed to |
This function can also be used to translate a user-friendly specification of each prior distribution to the form required by INLA and BUGS to fit the model.
Each prior distribution can be specified either by their mean
and
standard deviation sd
, mean
and variance var
or by
providing values for their parameters.
Invisibly returns a modified list of prior parameters with each prior specified using standardised parameters, as required for INLA and BUGS:
The log-Normal prior for the shape parameter r is given by the mean
mu
and precision tau
of the equivalent Normal prior for
log(r).
The Beta prior for the catch probability q is given
by two shape parameters a
and b
.
The Normal prior for a
linear variable beta.
* or gamma.
* is given by the mean
and precision
.
The Gamma prior for a precision hyperparameter
tau.
* or phi.
* is given by the shape a
and rate
b
.
Prior parameters specified for a scale hyperparameter sigma.
* or
nu.
* (following the hyperprior
distribution) are
transformed to the corresponding precision parameter tau.
* or
phi.
* respectively.
1 2 3 4 5 6 7 8 9 10 11 12 | # Specify a range of priors by their parameters:
# each prior is plotted and summarised by default
fcs2Priors(list(r=c(mu=0, sigma=3),
q=c(a=0.5, b=0.5),
beta=c(mean=0, precision=0.5),
phi=c(a=1, b=0.3)))
# Priors can also be specified by their mean and sd or var:
fcs2Priors(list(r=c(mean=3, sd=10),
q=c(mean=0.6, sd=0.2),
beta=c(mean=0, var=2),
sigma=c(mean=1.5, sd=3)))
|
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