\name{Sequential}
\alias{Sequential}
\alias{RPsequential}
\title{Methods relying on square roots of the covariance matrix}

\description{
 Sequential method relying on square roots of the covariance matrix
}

\usage{
RPsequential(phi, boxcox, back_steps, initial)
}

\arguments{
  \item{phi}{object of class \code{\link[=RMmodel-class]{RMmodel}};
    specifies the covariance model to be simulated.}
  \item{boxcox}{the one or two parameters of the box cox transformation.
    If not given, the globally defined parameters are used.
    See \command{\link{RFboxcox}} for details.
  }
  \item{back_steps}{
    Number of previous instances on which
    the algorithm should condition.
    If less than one then the number of previous instances
    equals \code{max} / (number of spatial points).
    
    Default: \code{10}.    
  }
 \item{initial}{
   First, N=(number of spatial points) * \code{back_steps}
   number of points are simulated. Then, sequentially,
   all spatial points for the next time instance
   are simulated at once, based on the previous \code{back_steps}
   instances. The distribution of the first N points
   is the correct distribution, but
   differs, in general, from the distribution of the sequentially
   simulated variables. We prefer here to have the same distribution
   all over (although only approximatively the correct one),
   hence do some initial sequential steps first.
   If \code{initial} is non-negative, then \code{initial}
   first steps are performed.
   If \code{initial} is negative, then
   \code{back_steps} - \code{initial}
 initial steps are performed. The latter ensures that
 none of the very first N variables are returned.
 
 Default: \code{-10}.
 }
}


\details{
\command{RPsequential}
 is programmed for spatio-temporal models
 where the field is modelled sequentially in the time direction
 conditioned on the previous \eqn{k} instances.
 For \eqn{k=5} the method has its limits for about 1000 spatial
 points. It is an approximative method. The larger \eqn{k} the
 better.
 It also works for certain grids where the last dimension should
 contain the highest number of grid points. 
}

\value{
 \command{\link{RPsequential}} returns an object of class \code{\link[=RMmodel-class]{RMmodel}}.

}

\references{
 \itemize{
 \item
 Schlather, M. (1999) \emph{An introduction to positive definite
 functions and to unconditional simulation of random fields.}
 Technical report ST 99-10, Dept. of Maths and Statistics,
 Lancaster University. 

 }}
\me

\seealso{ \link{Gaussian},
  \link{RP}, \link{RPdirect}.
 }

\keyword{methods}

\examples{\dontshow{StartExample(reduced=FALSE)}
RFoptions(seed=0) ## *ANY* simulation will have the random seed 0; set
##                   RFoptions(seed=NA) to make them all random again
model <- RMgauss(var=10, s=10) + RMnugget(var=0.01)
plot(model, xlim=c(-25, 25))

z <- RFsimulate(model=RPsequential(model), 0:10, 0:10, n=4)
plot(z)

\dontshow{FinalizeExample()}}