\name{set.default.parameters}
\alias{set.default.parameters}

\title{Get/set hyperparameters}
\description{Allows to set and retrieve various hyperparameters for different inference methods.}

\usage{
set.default.parameters(Sgenes, ...)
}
\arguments{
 \item{Sgenes}{character vector of S-gene identifiers}
 \item{...}{parameters to set (see details)}
}
\details{
Since version 2.5.4 functions in the nem package do not have any more a large amount of individual parameters. Instead there is just one hyperparameter, which is passed to all functions. Parameter values with the hyperparameter can be set with this function.
\describe{
 \item{type}{\code{mLL} or \code{FULLmLL} or \code{CONTmLL} or \code{CONTmLLBayes} or \code{CONTmLLMAP} or \code{gnem}. \code{CONTmLLDens} and \code{CONTmLLRatio} are identical to \code{CONTmLLBayes} and \code{CONTmLLMAP} and are still supported for compatibility reasons. \code{mLL} and \code{FULLmLL} are used for binary data (see \code{BoutrosRNAiDiscrete}) and \code{CONTmLL} for a matrix of effect probabilities. \code{CONTmLLBayes} and \code{CONTmLLMAP} are used, if log-odds ratios, p-value densities or any other model specifies effect likelihoods. \code{CONTmLLBayes} refers to an inference scheme, were the linking positions of effect reporters to network nodes are integrated out, and \code{CONTmLLMAP} to an inference scheme, were a MAP estimate for the linking positions is calculated. \code{depn} indicates Deterministic Effects Propagation Networks (DEPNs).}
  \item{para}{vector of length two: false positive rate and false negative rate for binary data. Used by \code{mLL}} 
  \item{hyperpara}{vector of length four: used by \code{FULLmLL()} for binary data}
  \item{Pe}{prior of effect reporter positions in the phenotypic hierarchy (same dimension as D). Not used type \code{gnem}. Default: NULL}
  \item{Pm}{prior over models (n x n matrix). Default: NULL}
  \item{Pmlocal}{local model prior for pairwise and triple learning. For pairwise learning generated by \code{local.model.prior} according to arguments \code{local.prior.size} and \code{local.prior.bias}}
  \item{local.prior.size}{prior expected number of edges in the graph (for pairwise learning). Default: no. nodes}
  \item{local.prior.bias}{bias towards double-headed edges. Default: 1 (no bias; for pairwise learning)}
  \item{triples.thrsh}{threshold for model averaging to combine triple models for each edge. Default: 0.5}
  \item{lambda}{regularization parameter to incorporate prior assumptions. May also be a vector of possible values, if \code{nemModelSelection} is used, Default: 0 (no regularization)}
  \item{delta}{regularization parameter for automated  subset selection of effect reporters (CONTmLLMAP only). Default: 1/no. nodes}
  \item{selEGenes}{automated E-gene subset selection (includes tuning of delta for CONTmLLMAP). Default: FALSE}  
  \item{trans.close}{Should always transitive closed graphs be computed? Default: TRUE. NOTE: This has only an impact for type  \code{nem.greedyMAP} and \code{gnem}. Default: TRUE}
  \item{backward.elimination}{For module networks and greedy hillclimbing inference: Try to eliminate edges increasing the likelihood. Works only, if trans.close=FALSE. Default: FALSE }
  \item{mode}{For Bayesian network inference and GNEMs: \code{binary_ML}: effects come from a binomial distribution - ML learning of parameters (Bayesian networks only); \code{binary_Bayesian}: effects come from a binomial distribution - Bayesian learning of parameters (Bayesian networks only); \code{continous_ML}: effects come from a normal distribution - ML learning of parameters; \code{continous_Bayesian}: effects come from a normal distribution - Bayesian learning of parameters.}
  \item{nu.intervention, lambda.intervention}{For \code{gnem}: For any perturbed node we suppose the unknown mean mu given its unknown variance sigma2 to be drawn from N(nu.intervention, sigma2/lambda.intervention). Default: nu.intervention=0.6, lambda.intervention=4}
  \item{nu.no\_intervention, lambda.no\_intervention}{The same parameters for unperturbed nodes. Default: nu.no\_intervention=0.95, lambda.no\_intervention=4}
  \item{df.intervention, scale.intervention}{For \code{gnem}: The unknown variance sigma2 for perturbed nodes is supposed to be drawn from Inv-\eqn{\chi^2}(df.intervention, scale.intervention). Default: df.intervention=4.4, scale.intervention=4.4}
  \item{df.no\_intervention, scale.no\_intervention}{The same parameters for unperturbed nodes. Default: df.no\_intervention=4.4, scale.no\_intervention=0.023}
  \item{map}{For \code{gnem}: Mapping of interventions to network nodes. The format is a named list of strings with names being the interventions and entries being the network nodes. Default: Entries and names are the network nodes. }
  \item{outputdir}{Directory where to put diagnostic plots. Default: folder "QualityControl" in current working directory}
  \item{debug}{Print out or plot diagnostic information. Default: FALSE}

 }
}
\value{
A list containing all parameters described above.
}
\examples{
control = set.default.parameters(LETTERS[1:5], type="CONTmLLBayes", selEGenes=TRUE) # set inference type and whether to use automatic E-gene selection for a network with nodes "A"-"E".
}
\author{Holger Froehlich \url{http:/www.dkfz.de/mga2/people/froehlich}}
\keyword{graphs}
\keyword{models}