\name{initialize-methods}
\docType{methods}
\alias{initialize-methods}
%\alias{initialize}
\alias{initialize,simObj-method}

\title{Methods for Function `initialize' in Package `simecol'}

\description{
  This function is used to initialize objects derived from the
  \code{simObj} superclass, it is by default automatically called during
  object creation and by \code{sim}.
}
\usage{
\S4method{initialize}{simObj}(.Object, \dots)
}

\arguments{
  \item{.Object}{\code{simObj} instance which is to be
    re-initialized.
  }
  \item{\dots}{provided for compatibility with the default method of
    \code{initialize}, or slots of the object which is to be created (in
    case of \code{\link[methods]{new}}).
  }
}

\section{Methods}{
  \describe{
    \item{.Object = "ANY"}{Generic function: see \code{\link[methods]{new}}.}
    \item{.Object = "simObj"}{
      The \code{initialize} function is normally called implicitly by
      \code{new} to create new objects. It may also be called explicitly
      to return a cloned and re-initialized object.

      The \pkg{simecol} version of \code{initialize} provides an
      additonal mechanism to call a user specified function provided in
      the \code{initfun} slot of a \code{simObj} instance that can
      perform computations during the object creation process. The
      \code{initfunc} must have \code{obj} as its only argument and must
      return the modified version of this \code{obj}, see examples
      below. As a side effect end to ensure consistency,
      \code{initialize} clears outputs stored in slot \code{out} from
      former simulations.
    }
  }
}

\seealso{
\code{\link{simObj}}, \code{\link[methods]{new}}

}
\examples{
## Note: new calls initialize and initialize calls initfunc(obj)
lv_efr <- new("odeModel",
  main = function (time, init, parms, ...) {
    x <- init
    p <- parms
    S <- approxTime1(inputs, time, rule=2)["s.in"]
    dx1 <-   S * p["k1"] * x[1] - p["k2"] * x[1] * x[2]
    dx2 <-     - p["k3"] * x[2] + p["k2"] * x[1] * x[2]
    list(c(dx1, dx2))
  },
  parms  = c(k1=0.2, k2=0.2, k3=0.2),
  times  = c(from=0, to=100, by=0.5),
  init   = c(prey=0.5, predator=1),
  solver = "lsoda",
  initfunc = function(obj) {
    tt <- fromtoby(times(obj))
    inputs(obj) <- as.matrix(data.frame(
            time = tt,
            s.in = pmax(rnorm(tt, mean=1, sd=0.5), 0)
          ))
    obj
  }
)
plot(sim(lv_efr))                     # initialize called automatically
plot(sim(lv_efr))                     # automatic initialization, different figure

lv_efr<- initialize(lv_efr)           # re-initialize manually
plot(sim(lv_efr, initialize = FALSE)) # simulation with that configuration
    

}
\keyword{methods}