spike.slab.prior.base {BoomSpikeSlab}  R Documentation 
Base class for spike and slab priors
Description
A base class for SpikeSlabPrior and SpikeSlabPriorBase to ensure that elements common to both classes are handled consistently. Users will not normally interact with this function.
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 
expected.r2 
The expected Rsquare 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 freedom" representing the
number of observations that the guess should weigh. The guess at
sigma^2 is set to 
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.model.size 
A positive number less than

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 be set to 
mean.y 
The mean of the response vector. Used to create a
default value of 
sdy 
The standard deviation of the response vector. Used along
with 
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 nonpositive number is interpreted as

Value
Returns an object of class SpikeSlabPriorBase
, which is a
list with the following elements.
prior.inclusion.probabilities: A vector giving the prior probability of inclusion for each coefficient.
mu: A vector giving the prior mean of each coefficient conditional on inclusion.
sigma.guess: A prior estimate of the residual standard deviation.
prior.df: The number of observations worth of weight to be given to
sigma.guess
.
Author(s)
Steven L. Scott
References
George and McCulloch (1997), "Approaches to Bayesian Variable Selection", Statistica Sinica, 7, 339 – 373.
https://www3.stat.sinica.edu.tw/statistica/oldpdf/A7n26.pdf