Gene counting for haplotype analysis with missing data, adapted for hap.score
hap.em(id, data, locus.label=NA, converge.eps=1e-06, maxiter=500, miss.val=0)a vector of individual IDs
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.
Vector of labels for loci, of length K (see definition of data matrix).
Convergence criterion, based on absolute change in log likelihood (lnlike).
Maximum number of iterations of EM
missing value
List with components:
Indicator of convergence of the EM algorithm (1=converged, 0 = failed).
Number of iterations completed in the EM alogrithm.
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.
Vector of labels for loci, of length K (see definition of input values).
Matrix of unique haplotypes. Each row represents a unique haplotype, and the number of columns is the number of loci.
Vector of mle's of haplotype probabilities. The ith element of hap.prob corresponds to the ith row of haplotype.
Value of lnlike at last EM iteration (maximum lnlike if converged).
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.
Vector for the count of haplotype pairs that map to each subject's marker genotypes.
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.
Similar to hap1code, but for each subject's second haplotype.
Vector of posterior probabilities of pairs of haplotypes for a person, given thier marker phenotypes.
See hap
# NOT RUN {
data(hla)
hap.em(id=1:length(hla[,1]),data=hla[,3:8],locus.label=c("DQR","DQA","DQB"))
# }
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