\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
}

\seealso{
\code{\link{sns}}, \code{\link{sns.run}}
}