Usage
SpikeSlabPriorBase(number.of.variables,
expected.r2 = .5,
prior.df = .01,
expected.model.size = 1,
optional.coefficient.estimate = NULL,
mean.y,
sdy,
prior.inclusion.probabilities = NULL,
sigma.upper.limit = Inf)Arguments
number.of.variables
The number of columns in x.
expected.r2
The expected R-square for the regression. The spike and slab prior
requires an inverse gamma prior on the residual variance of the
regression. The prior can be parameterized in terms of a guess at
the residual variance, and a "degrees of fre
prior.df
A positive scalar representing the prior 'degrees of freedom' for
estimating the residual variance. This can be thought of as the
amount of weight (expressed as an observation count) given to the
expected.r2 argument.
expected.model.size
A positive number less than
ncol(x), representing a guess at the number of significant
predictor p variables. Used to compute a default value of
prior.inclusion.probabilities if the latter is NULL.
optional.coefficient.estimate
If desired, an estimate of the
regression coefficients can be supplied. In most cases this will be
a difficult parameter to specify. If omitted then a prior mean of
zero will be used for all coordinates except the intercept, which
will b
mean.y
The mean of the response vector. Used to create a
default value of optional.coefficient.estimate when the latter
is NULL.
sdy
The standard deviation of the response vector. Used along
with expected.r2 to create a prior estimate of the residual
variance.
prior.inclusion.probabilities
A vector giving the prior
probability of inclusion for each coefficient.
sigma.upper.limit
The largest acceptable value for the residual
standard deviation. A non-positive number is interpreted as
Inf.