\name{drawPMParam}
\alias{drawPMParam}
\alias{lpriorPMParam}
\title{Draws a value of the parameter set for a ProficiencyModel
}
\description{

  Each \code{\link{ProficiencyModel}} object should contain a prior
  distribtion for its parameter object.  The \code{drawPMParam} function
  draws a random set of values for the parameter object from that
  distribution. The \code{lpriorPMParam} function calculates the log of
  the prior probability of a given parameter.
  
}
\usage{
drawPMParam(pm, background = NA)
lpriorPMParam(pm, param=parameters(pm), background = NA)
}
\arguments{
  \item{pm}{
    An object of class \code{\link{ProficiencyModel}}.
  }
  \item{param}{
    A set of parameters whose prior distribution is to be evaluated.
  }
  \item{background}{
    An optional set of background (demographic) variables.  Some
    proficiency model types ignore background variables.
  }
}
\value{

  The function \code{drawPMParam} returns parameter object of a class
  appropriate for use with the \code{pm} argument.

  The function \code{lpriorPMParam} returns a scalar giving the log of
  the prior probability of the argument.
}
\references{
%% ~put references to the literature/web site here ~
}
\author{Russell Almond}

\seealso{
  \code{\link{ProficiencyModel}}
}
\examples{
pm1 <-
  TimelessNormalPM(muMean=c(Mechanics=2,Fluency=2),  varWeight=3,
                   precScale=solve(matrix(c(.7,.3,.3,.7),2,2)), Sdf=3)

pm1.p1 <- drawPMParam(pm1)
lpriorPMParam(pm1,pm1.p1)

}
\keyword{ distribution }
\keyword{ random }