prior_factor {BayesTools}  R Documentation 
Creates a prior distribution for factors
Description
prior_factor
creates a prior distribution for fitting
models with factor predictors. (Note that results across different operating
systems might vary due to differences in JAGS numerical precision.)
Usage
prior_factor(
distribution,
parameters,
truncation = list(lower = Inf, upper = Inf),
prior_weights = 1,
contrast = "meandif"
)
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.

contrast 
type of contrast for the prior distribution. The possible options are
"meandif" for contrast centered around the grand mean
with equal marginal distributions, making the prior distribution exchangeable
across factor levels. In contrast to "orthonormal" , the marginal distributions
are identical regardless of the number of factor levels and the specified prior
distribution corresponds to the difference from grand mean for each factor level.
Only supports distribution = "mnormal" and distribution = "mt"
which generates the corresponding multivariate normal/t distributions.
"orthonormal" for contrast centered around the grand mean
with equal marginal distributions, making the prior distribution exchangeable
across factor levels. Only supports distribution = "mnormal" and
distribution = "mt" which generates the corresponding multivariate normal/t
distributions.
"treatment" for contrasts using the first level as a comparison
group and setting equal prior distribution on differences between the individual
factor levels and the comparison level.
"independent" for contrasts specifying dependent prior distribution
for each factor level (note that this leads to an overparameterized model if the
intercept is included).

Value
return an object of class 'prior'.
See Also
prior()
Examples
# create an orthonormal prior distribution
p1 < prior_factor(distribution = "mnormal", contrast = "orthonormal",
parameters = list(mean = 0, sd = 1))
[Package
BayesTools version 0.2.17
Index]