\name{predict.sns}
\alias{predict.sns}
\alias{summary.predict.sns}
\alias{print.summary.predict.sns}
\title{
Sample-based prediction using "sns" Objects
}
\description{
Method for sample-based prediction using the output of \code{\link{sns.run}}.
}
\usage{
\method{predict}{sns}(object, fpred
, nburnin = max(nrow(object)/2, attr(object, "nnr"))
, end = nrow(object), thin = 1, ...)
\method{summary}{predict.sns}(object
, quantiles = c(0.025, 0.5, 0.975)
, ess.method = c("coda", "ise"), ...)
\method{print}{summary.predict.sns}(x, ...)
}
%- maybe also 'usage' for other objects documented here.
\arguments{
\item{object}{Object of class "sns" (output of \code{\link{sns.run}}) or "predict.sns" (output of \code{predict.sns}).}
\item{fpred}{Prediction function, accepting a single value for the state vector and producing a vector of outputs.}
\item{nburnin}{Number of burn-in iterations discarded for sample-based prediction.}
\item{end}{Last iteration used in sample-based prediction.}
\item{thin}{One out of \code{thin} iterations within the specified range are used for sample-based prediction.}
\item{quantiles}{Values for which sample-based quantiles are calculated.}
\item{ess.method}{Method used for calculating effective sample size. Default is to call \code{effectiveSize} from package \code{coda}.}
\item{x}{An object of class "summary.predict.sns".}
\item{...}{Arguments passed to/from other functions.}
}
\value{
\code{predict.sns} produces a matrix with number of rows equal to the length of prediction vector produces by \code{fpred}. Its numnber of columns is equal to the number of samples used within the user-specified range, and after thinning (if any). \code{summary.predict.sns} produces sample-based prediction mean, standard deviation, quantiles, and effective sample size.
}
\author{
Alireza S. Mahani, Asad Hasan, Marshall Jiang, Mansour T.A. Sharabiani
}
\note{
See package vignette for more details on SNS theory, software, examples, and performance.
}
\seealso{
\code{\link{sns.run}}
}
\examples{
\dontrun{
# using RegressionFactory for generating log-likelihood and derivatives
library("RegressionFactory")
loglike.poisson <- function(beta, X, y) {
regfac.expand.1par(beta, X = X, y = y,
fbase1 = fbase1.poisson.log)
}
# simulating data
K <- 5
N <- 1000
X <- matrix(runif(N * K, -0.5, +0.5), ncol = K)
beta <- runif(K, -0.5, +0.5)
y <- rpois(N, exp(X \%*\% beta))
beta.init <- rep(0.0, K)
beta.smp <- sns.run(beta.init, loglike.poisson,
niter = 1000, nnr = 20, mh.diag = TRUE, X = X, y = y)
# prediction function for mean response
predmean.poisson <- function(beta, Xnew) exp(Xnew \%*\% beta)
ymean.new <- predict(beta.smp, predmean.poisson,
nburnin = 100, Xnew = X)
summary(ymean.new)
# (stochastic) prediction function for response
predsmp.poisson <- function(beta, Xnew)
rpois(nrow(Xnew), exp(Xnew \%*\% beta))
ysmp.new <- predict(beta.smp, predsmp.poisson
, nburnin = 100, Xnew = X)
summary(ysmp.new)
}
}