gc.em.Rd
% 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}