Usage
SpikeSlabArPrior(
lags,
prior.inclusion.probabilities =
GeometricSequence( lags, initial.value = .8, discount.factor = .8),
prior.mean = rep(0, lags),
prior.sd =
GeometricSequence(lags, initial.value = .5, discount.factor = .8),
sdy,
prior.df = 1,
expected.r2 = .5,
sigma.upper.limit = Inf,
truncate = TRUE)Arguments
lags
A positive integer giving the maximum number of lags to
consider.
prior.inclusion.probabilities
A vector of length lags
giving the prior probability that the corresponding AR coefficient
is nonzero.
prior.mean
A vector of length lags giving the prior mean
of the AR coefficients. This should almost surely stay set at zero.
prior.sd
A vector of length lags giving the prior
standard deviations of the AR coefficients, which are modeled as
a-priori independent of one another.
sdy
The sample standard deviation of the series being modeled.
expected.r2
The expected fraction of variation in the response
explained by this AR proces.
prior.df
A positive number indicating the number of
observations (time points) worth of weight to assign to the guess at
expected.r2.
sigma.upper.limit
A positive number less than infinity
truncates the support of the prior distribution to regions where the
residual standard deviation is less than the specified limit. Any
other value indicates support over the entire positive real line.
truncate
If TRUE then the support of the distribution is
truncated to the region where the AR coefficients imply a stationary
process. If FALSE the coefficients are unconstrained.