Usage
StudentSpikeSlabPrior(predictor.matrix,
response.vector = NULL,
expected.r2 = .5,
prior.df = .01,
expected.model.size = 1,
prior.information.weight = .01,
diagonal.shrinkage = .5,
optional.coefficient.estimate = NULL,
max.flips = -1,
mean.y = mean(response.vector, na.rm = TRUE),
sdy = sd(as.numeric(response.vector), na.rm = TRUE),
prior.inclusion.probabilities = NULL,
sigma.upper.limit = Inf,
degrees.of.freedom.prior = UniformPrior(.1, 100))Arguments
predictor.matrix
The design matrix for the regression problem. Missing data is not allowed.
response.vector
The vector of responses for the regression.
Missing data is not allowed. If response.vector is not
available, you can pass response.vector = NULL, and specify
mean.y and sdy instead.
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 variables. Used to obtain the
'spike' portion of the spike and slab prior.
prior.information.weight
A positive scalar. Number of observations worth of weight that
should be given to the prior estimate of beta.
diagonal.shrinkage
The conditionally Gaussian prior for beta (the "slab") starts with a
precision matrix equal to the information in a single observation.
However, this matrix might not be full rank. The matrix can be made
full rank by averaging with its diagon
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
max.flips
The maximum number of variable inclusion indicators
the sampler will attempt to sample each iteration. If
max.flips <= 0<="" code=""> then all indicators will be sampled.
mean.y
The mean of the response vector, for use in cases when
specifying the response vector is undesirable.
sdy
The standard deviation of the response vector, for use in
cases when specifying the response vector is undesirable.
prior.inclusion.probabilities
A vector giving the prior
probability of inclusion for each variable.
sigma.upper.limit
The largest acceptable value for the residual
standard deviation. A non-positive number is interpreted as
Inf.
degrees.of.freedom.prior
An object of class
DoubleModel representing the prior distribution for the
Student T tail thickness (or "degrees of freedom") parameter.