man/evalPars.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/evalPars.R
\name{evalPars}
\alias{evalPars}
\alias{getFreeParsRanges}
\title{Evaluate a model for a matrix of parameters}
\usage{
evalPars(
  par.matrix,
  object,
  objective = hydromad.getOption("objective"),
  parallel = hydromad.getOption("parallel")[["evalPars"]]
)
}
\arguments{
\item{par.matrix}{Named matrix or data.frame of parameter values, with each
row corresponding to a model realisation to evaluate}

\item{object}{an object of class \code{hydromad}.}

\item{objective}{the objective function or expression, which can refer to Q
and X.  See \code{\link{objFunVal.hydromad}}}

\item{parallel}{Which parallelisation method to use. Options are
\code{"clusterApply"} and \code{"foreach"}. For any other value, parameters
will be evaluated sequentially. Default is \code{"none"}. For more
information see \code{\link{hydromad_parallelisation}}.}
}
\value{
evalPars returns a vector of objective function values,
corresponding to the evaluation of each row of \code{par.matrix}.
}
\description{
\code{evalPars} evaluates a model for a named matrix of parameters.
}
\details{
\code{evalPars} is used in conjunction with \code{getFreeParsRanges}
and the \code{sensitivity} package to perform sensitivity analysis. See
\code{demo(sensitivity)}. Note that the objective function may more
generally return any scalar result, e.g. a scalar prediction calculated only
using X.

Individual model evaluations are generally very fast, so parallelisation is
only really worthwhile when large numbers of evaluations are needed.
\code{"clusterApply"} has a slightly lower overhead. \code{"foreach"} allows
a broader range of options.
}
\examples{

data(Cotter)
obs <- Cotter[1:1000]
modx <- hydromad(obs,
  sma = "cmd", routing = "expuh",
  tau_s = c(2, 100), v_s = c(0, 1)
)

## Sample 10 random parameter sets for parameters with defined ranges
pars <- parameterSets(getFreeParsRanges(modx), 10, method = "random")

## Return the default objective function value for each model realisation
evalPars(pars, object = modx)

## Calculate the 20\%ile flow for each model realisation
evalPars(pars, object = modx, objective = ~ quantile(X, 0.2))

## Alternatively, sample 10 random parameter sets from all parameters
##  This allows specifying discrete values of parameters
pars <- parameterSets(coef(modx, warn = FALSE), 10, method = "random")
}
\seealso{
\code{\link{objFunVal.hydromad}}, \code{\link{parameterSets}},
\code{getFreeParsRanges}
}
\author{
Joseph Guillaume
}
\keyword{models}