poisson.spike {BoomSpikeSlab}  R Documentation 
MCMC algorithm for Poisson regression models with a 'spikeandslab' prior that places some amount of posterior probability at zero for a subset of the coefficients.
poisson.spike(formula, exposure = 1, niter, data, subset, prior = NULL, na.action = options("na.action"), contrasts = NULL, drop.unused.levels = TRUE, initial.value = NULL, ping = niter / 10, nthreads = 4, seed = NULL, ...)
formula 
A model formula, as would be passed to 
exposure 
A vector of exposure durations matching the length of
the response vector. If 
niter 
The number of MCMC iterations to run. 
data 
An optional data frame, list or environment (or object
coercible by 
subset 
An optional vector specifying a subset of observations to be used in the fitting process. 
prior 
A list such as that returned by

na.action 
A function which indicates what should happen when
the data contain 
contrasts 
An optional list. See the 
drop.unused.levels 
A logical value indicating whether factor levels that are unobserved should be dropped from the model. 
initial.value 
Initial value for the MCMC algorithm. Can either
be a numeric vector, a 
ping 
If positive, then print a status update to the console
every 
nthreads 
The number of CPUthreads to use for data augmentation. 
seed 
Seed to use for the C++ random number generator. It
should be 
... 
Extra arguments to be passed to 
The MCMC algorithm used here is based on the auxiliary mixture sampling algorithm published by FruhwirthSchnatter, Fruhwirth, Held, and Rue (2009).
Returns an object of class poisson.spike
. The returned object
is a list with the following elements.
beta 
A 
prior 
The prior used to fit the model. If a 
Steven L. Scott
Sylvia FruhwirthSchnatter, Rudolf Fruhwirth, Leonhard Held, and Havard Rue. Statistics and Computing, Volume 19 Issue 4, Pages 479492. December 2009
lm.spike
SpikeSlabPrior
,
plot.lm.spike
,
summary.lm.spike
,
predict.lm.spike
.
simulate.poisson.spike < function(n = 100, p = 10, ngood = 3, niter=1000){ x < cbind(1, matrix(rnorm(n * (p1)), nrow=n)) beta < c(rnorm(ngood), rep(0, p  ngood)) lambda < exp(x %*% beta) y < rpois(n, lambda) x < x[,1] model < poisson.spike(y ~ x, niter=niter) return(invisible(model)) } model < simulate.poisson.spike() plot(model) summary(model)