prior_PP {BayesTools}  R Documentation 
Creates a prior distribution for PET or PEESE models
Description
prior
creates a prior distribution for fitting a PET or
PEESE style models in RoBMA. The prior distribution can be visualized
by the plot
function.
Usage
prior_PET(
distribution,
parameters,
truncation = list(lower = 0, upper = Inf),
prior_weights = 1
)
prior_PEESE(
distribution,
parameters,
truncation = list(lower = 0, upper = Inf),
prior_weights = 1
)
Arguments
distribution 
name of the prior distribution. The
possible options are
"point" for a point density characterized by a
location parameter.
"normal" for a normal distribution characterized
by a mean and sd parameters.
"lognormal" for a lognormal distribution characterized
by a meanlog and sdlog parameters.
"cauchy" for a Cauchy distribution characterized
by a location and scale parameters. Internally
converted into a generalized tdistribution with df = 1 .
"t" for a generalized tdistribution characterized
by a location , scale , and df parameters.
"gamma" for a gamma distribution characterized
by either shape and rate , or shape and
scale parameters. The later is internally converted to
the shape and rate parametrization
"invgamma" for an inversegamma distribution
characterized by a shape and scale parameters. The
JAGS part uses a 1/gamma distribution with a shape and rate
parameter.
"beta" for a beta distribution
characterized by an alpha and beta parameters.
"exp" for an exponential distribution
characterized by either rate or scale
parameter. The later is internally converted to
rate .
"uniform" for a uniform distribution defined on a
range from a to b

parameters 
list of appropriate parameters for a given
distribution .

truncation 
list with two elements, lower and
upper , that define the lower and upper truncation of the
distribution. Defaults to list(lower = Inf, upper = Inf) .
The truncation is automatically set to the bounds of the support.

prior_weights 
prior odds associated with a given distribution.
The value is passed into the model fitting function, which creates models
corresponding to all combinations of prior distributions for each of
the model parameters and sets the model priors odds to the product
of its prior distributions.

Value
prior_PET
and prior_PEESE
return an object of class 'prior'.
See Also
plot.prior()
, prior()
Examples
# create a halfCauchy prior distribution
# (PET and PEESE specific functions automatically set lower truncation at 0)
p1 < prior_PET(distribution = "Cauchy", parameters = list(location = 0, scale = 1))
plot(p1)
[Package
BayesTools version 0.1.3
Index]