\name{haplo.em.w}
\alias{haplo.em.w}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{Wrapper for EM computation of haplotype probabilities, with Progressive Insertion}
\description{
Wrapper for EM computation of haplotype probabilities, with Progressive Insertion.
}
\usage{
haplo.em.w(geneSetObj, 
           locus.label = NA, 
           miss.val = c(0, NA), 
           weight = NULL, 
           control = haplo.em.control())
}
%- maybe also 'usage' for other objects documented here.
\arguments{
  \item{geneSetObj}{a geneSet object.}
  \item{locus.label}{ vector of labels for loci.}
  \item{miss.val}{ vector of values that represent missing alleles in geno.}
  \item{weight}{ weights for observations.}
  \item{control}{ list of control parameters. The default is constructed by the
   function \code{\link[haplo.stats]{haplo.em.control}}.}
}
\details{
Please refer to \code{\link[haplo.stats]{haplo.em}} for more details.
}
\value{
  list with components:

\item{converge}{
indicator of convergence of the EM algorithm
  (1 = converge, 0 = failed).
}
\item{lnlike}{
value of lnlike at last EM iteration (maximum lnlike if converged).
}
\item{lr}{
likelihood ratio statistic to test the final lnlike against the
  lnlike that assumes complete linkage equilibrium among all loci
  (i.e., haplotype frequencies are products of allele frequencies).
}
\item{df.lr}{
degrees of freedom for likelihood ratio statistic. The df for the
  unconstrained final model is the  number of non-zero haplotype frequencies
  minus 1, and the df for the null model of complete linkage 
  equilibrium is the sum, over all loci, of (number of alleles - 1). The
  df for the lr statistic is df[unconstrained] - df[null]. This can
  result in negative df, if many haplotypes are estimated to have zero 
  frequency, or if a large amount of trimming occurs, when using large
  values of min.posterior in the list of control parameters.
}
\item{hap.prob}{
vector of mle's of haplotype probabilities.  The ith
  element of hap.prob corresponds to the ith row of 
  haplotype.
}
\item{locus.label}{
vector of labels for loci, of length K 
(see definition of input values).
}
\item{subj.id}{
vector of id's for subjects used in the analysis, based on row number
  of input geno matrix. If subjects are removed, then their id will be
  missing from subj.id.
}
\item{rows.rem}{
now defunct, but set equal to a vector of length 0, to be compatible 
  with other functions that check for rows.rem.
}
\item{indx.subj}{
vector for row index of subjects after expanding to all 
 possible pairs of haplotypes for each person.  If indx.subj=i, 
 then i is the ith row of  geno.  If the ith 
 subject has n possible pairs of haplotypes that correspond 
 to their marker genotype, then i is repeated n times.
}
\item{nreps}{
vector for the count of haplotype pairs that map to
  each subject's marker genotypes.
}
\item{max.pairs}{
vector of maximum number of pairs of haplotypes per subject that are
  consistent with their marker data in the matrix geno. The
  length of max.pairs  = nrow(geno). This vector is computed by  
  geno.count.pairs.
}
\item{hap1code}{
vector of codes for each subject's first haplotype.
  The values in hap1code are the row numbers of the unique
  haplotypes in the returned matrix haplotype.
}
\item{hap2code}{
similar to hap1code, but for each subject's second haplotype.
}
\item{post}{
vector of posterior probabilities of pairs of haplotypes for 
  a person, given their marker phenotypes.
}
\item{haplotype}{
matrix of unique haplotypes. Each row represents a unique 
  haplotype, and the number of columns is the number of loci.
}
\item{control}{
list of control parameters for algorithm. See haplo.em.control
}
}
\references{ ~put references to the literature/web site here ~ }
\author{ Weiliang Qiu \email{stwxq@channing.harvard.edu}, Ross Lazarus \email{ross.lazarus@channing.harvard.edu}}
\note{ ~~further notes~~ 
}
\seealso{ \code{\link{haplo.scan.w}}, \code{\link{haplo.score.slide.w}}, \code{\link{haplo.score.w}}}
\examples{
##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
"haplo.em.w"
}
\keyword{ misc }% at least one, from doc/KEYWORDS