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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