\name{bgModel}
\alias{bgModel}
\title{Estimating the background Markov model}
\description{
  bgModel() obtains an estimate of the Markov model used by cosmo() for
  modeling the distribution of nucleotides that are not part of the
  motif. bgModel() can select the order of this model data-adaptively by
  likelihood-based cross-validation (a k-th order Markov model allows
  the probability of encountering the four different nucleotides in a
  given position to depend on the k previous nucleotides). 
  
}
\usage{
  bgModel(seqs, order=NULL, fold = 5, maxOrder = 6) 
}
\arguments{
  \item{seqs}{ This argument specifies the sequences that are to be
    used to estimate the background Markov model. If seqs == "browse", a browser appears that
    allows the user to select a file that contains the sequences in
    FASTA format. If seqs is another character string, it is assumed
    to give the path to a FASTA file containing the sequences of interest.
    Lastly, seqs may be a list with each element representing a sequence
    in the form of a single string such as "ACGTAGCTAG" ("seq" entry)
    and a description ("desc" entry).}
  \item{order}{\code{numerical} The order of the Markov background model. If
    this argument is NULL, the order is selected data-adaptively by
    likelihood-based cross-validation. Otherwise, a Markov model for the
    specified order is estimated.} 
  \item{fold}{\code{numerical} cross-validation fold for selecting
    order of background Markov model}
  \item{maxOrder}{\code{numerical} Maximum order to consider for Markov background model.} 
}
\details{

}
\value{
  A list with the folowing elements:
  \item{transMat}{The estimated transition matrix for the 
    background Markov model. This is a list of matrices, with the first
    matrix given the transition probabilities for the 0th order Markov
    model, the second matrix giving the transition probabilities for a
    1st order Markov model, and so on.}
   \item{order}{The selected order of the background Markov model.}
   \item{klDivs}{The Kullback-Leibler divergences for the different
     candidate orders for the background Markov model. Likelihood-based
     cross-validation selects the order with the minimum
     Kullback-Leibler divergence.}
}
\references{}
\author{Oliver Bembom, \email{bembom@berkeley.edu}}
\note{}

\seealso{
  \code{\link{cosmo}}
}
\examples{
## path to example sequence file in FASTA format
seqFile <- system.file("Exfiles","seq.fasta",package="cosmo")

## estimate transition matrix for order 2
tmat1 <- bgModel(seqFile, order=2)

## select order data-adaptively
tmat2 <- bgModel(seqFile)

}
\keyword{}