sim.geno, by the Viterbi
algorithm with argmax.geno, or simply filling in
genotypes between markers that have matching genotypes.
fill.geno(cross, method=c("imp","argmax", "no_dbl_XO", "maxmarginal"), error.prob=0.0001, map.function=c("haldane","kosambi","c-f","morgan"), min.prob=0.95)cross. See
read.cross for details.sim.geno, using the Viterbi
algorithm, as implemented in argmax.geno, by simply
filling in missing genotypes between markers with matching genotypes,
or by choosing (at each marker) the genotype with maximal marginal probability.method="maxmarginal", genotypes with
probability greater than this value will be imputed; those less than
this value will be made missing.cross object with the genotype data replaced by an
imputed version. Any intermediate calculations (such as is produced
by calc.genoprob, argmax.geno
and sim.geno) are removed.
With method="imp", a single random imputation is performed,
using sim.geno.
With method="argmax", for each individual the most probable
sequence of genotypes, given the observed data (via
argmax.geno), is used.
With method="no_dbl_XO", non-recombinant intervals are filled
in; recombinant intervals are left missing. For example, a sequence of
genotypes like A---A---H---H---A (with A and H
corresponding to genotypes AA and AB, respectively, and with -
being a missing value) will be filled in as
AAAAA---HHHHH---A.
With method="maxmarginal", the conditional genotype
probabilities are calculated with calc.genoprob, and then at
each marker, the most probable genotype is determined. This is taken
as the imputed genotype if it has probability greater than
min.prob; otherwise it is made missing.
With method="no_dbl_XO" and method="maxmarginal",
some missing genotypes likely remain. With
method="maxmarginal", some observed genotypes may be made
missing.
sim.geno,
argmax.geno data(hyper)
out.mr <- scantwo(fill.geno(hyper,method="argmax"), method="mr")
plot(out.mr)
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