% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gc.em.R
\name{gc.em}
\alias{gc.em}
\title{Gene counting for haplotype analysis}
\usage{
gc.em(
  data,
  locus.label = NA,
  converge.eps = 1e-06,
  maxiter = 500,
  handle.miss = 0,
  miss.val = 0,
  control = gc.control()
)
}
\arguments{
\item{data}{Matrix of alleles, such that each locus has a pair of
adjacent columns of alleles, and the order of columns
corresponds to the order of loci on a chromosome. If
there are K loci, then ncol(data) = 2*K. Rows represent
alleles for each subject.}

\item{locus.label}{Vector of  labels  for  loci,  of  length  K  (see definition of data matrix).}

\item{converge.eps}{Convergence criterion, based on absolute  change in log likelihood (lnlike).}

\item{maxiter}{Maximum number of iterations of EM.}

\item{handle.miss}{a flag for handling missing genotype data, 0=no, 1=yes.}

\item{miss.val}{missing value.}

\item{control}{a function, see \code{\link[gap]{genecounting}}.}
}
\value{
List with components:
\describe{
 \item{converge}{Indicator of convergence of the EM algorithm
 (1=converged, 0 = failed).}
 \item{niter}{Number of iterations completed in the EM alogrithm.}
 \item{locus.info}{A list with  a  component for each locus.  Each
  component is also a list, and  the  items of a locus-
  specific list are the locus name and a vector for the
  unique alleles for the locus.}
 \item{locus.label}{Vector of  labels  for  loci,  of  length  K  (see
   definition of input values).}
 \item{haplotype}{Matrix of unique haplotypes. Each row represents a
  unique  haplotype, and the number of columns is the number of loci.}
 \item{hap.prob}{Vector of mle's of haplotype probabilities.  The ith
  element of hap.prob corresponds to the ith row of haplotype.}
 \item{hap.prob.noLD}{Similar to hap.prob, but assuming no linkage
  disequilibrium.}
 \item{lnlike}{Value of lnlike at last EM iteration (maximum lnlike if converged).}
 \item{lr}{Likelihood ratio statistic to test no linkage disequilibrium among all loci.}
 \item{indx.subj}{Vector for index of subjects, after  expanding  to
  all possible  pairs  of  haplotypes  for  each person. If
  indx=i, then i is the ith row of input matrix data. If the
  ith subject has  n possible  pairs  of haplotypes that
  correspond to their marker phenotype, then i is repeated n times.}
 \item{nreps}{Vector for the count of haplotype pairs that map to
  each subject's marker genotypes.}
 \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 thier marker phenotypes.}
 \item{htrtable}{A table which can be used in haplotype trend regression}
}
}
\description{
Gene counting for haplotype analysis with missing data, adapted for hap.score
}
\note{
Adapted from GENECOUNTING.
}
\examples{
\dontrun{
data(hla)
gc.em(hla[,3:8],locus.label=c("DQR","DQA","DQB"),control=gc.control(assignment="t"))
}

}
\references{
Zhao, J. H., Lissarrague, S., Essioux, L. and P. C. Sham (2002).
GENECOUNTING: haplotype analysis with missing genotypes.
Bioinformatics 18(12):1694-1695

Zhao, J. H. and P. C. Sham (2003). Generic number systems and haplotype
analysis. Comp Meth Prog Biomed 70: 1-9
}
\seealso{
\code{\link[gap]{genecounting}}, \code{\link[gap]{LDkl}}
}
\author{
Jing Hua Zhao
}
\keyword{models}