predict.lm.spike {BoomSpikeSlab}  R Documentation 
Generate draws from the posterior predictive distribution of a spike and slab regression.
## S3 method for class 'lm.spike' predict(object, newdata = NULL, burn = 0, na.action = na.pass, mean.only = FALSE, ...) ## S3 method for class 'logit.spike' predict(object, newdata, burn = 0, type = c("prob", "logit", "link", "response"), na.action = na.pass, ...) ## S3 method for class 'poisson.spike' predict(object, newdata = NULL, exposure = NULL, burn = 0, type = c("mean", "log", "link", "response"), na.action = na.pass, ...) ## S3 method for class 'probit.spike' predict(object, newdata, burn = 0, type = c("prob", "probit", "link", "response"), na.action = na.pass, ...) ## S3 method for class 'qreg.spike' predict(object, newdata, burn = 0, na.action = na.pass, ...) ## S3 method for class 'BayesNnet' predict(object, newdata = NULL, burn = 0, na.action = na.pass, mean.only = FALSE, seed = NULL, ...)
object 
A model object of class 
newdata 
Either If If 
exposure 
A vector of positive real numbers the same size as
newdata, or 
burn 
The number of MCMC iterations in the object to be discarded as burnin. 
na.action 
a function which indicates what should happen when
the data contain 
type 
The type of prediction desired. For For Both cases also accept 
mean.only 
Logical. If 
seed 
Random seed for the C++ random number generator. This is only needed for models that require C++ to implement their predict method. 
... 
Unused, but present for compatibility with generic

Returns a matrix of predictions, with each row corresponding to a row in newdata, and each column to an MCMC iteration.
Steven L. Scott
lm.spike
SpikeSlabPrior
summary.lm.spike
plot.lm.spike
niter < 1000 n < 100 p < 10 ngood < 3 x < cbind(1, matrix(rnorm(n * (p1)), nrow=n)) beta < rep(0, p) good < sample(1:p, ngood) beta[good] < rnorm(ngood) sigma < 1 y < rnorm(n, x %*% beta, sigma) model < lm.spike(y ~ x  1, niter=niter) plot(model) plot.ts(model$beta) hist(model$sigma) ## should be near true value new.x < cbind(1, matrix(rnorm(100 * (p1)), ncol = (p1))) pred < predict(model, newdata = new.x, burn = 100)