Uses the hidden Markov model technology to simulate from the joint distribution Pr(g | O) where g is the underlying genotype vector and O is the observed multipoint marker data, with possible allowance for genotyping errors.
sim.geno(cross, n.draws=16, step=0, off.end=0, error.prob=0.0001,
map.function=c("haldane","kosambi","c-f","morgan"),
stepwidth=c("fixed", "variable", "max"))
An object of class cross
. See
read.cross
for details.
Number of simulation replicates to perform.
Maximum distance (in cM) between positions at which the
simulated genotypes will be drawn, though for step=0
,
genotypes are drawn only at the marker locations.
Distance (in cM) past the terminal markers on each chromosome to which the genotype simulations will be carried.
Assumed genotyping error rate used in the calculation of the penetrance Pr(observed genotype | true genotype).
Indicates whether to use the Haldane, Kosambi, Carter-Falconer, or Morgan map function when converting genetic distances into recombination fractions.
Indicates whether the intermediate points should with
fixed or variable step sizes. We recommend using
"fixed"
; "variable"
is included for the qtlbim
package (http://www.ssg.uab.edu/qtlbim). The "max"
option inserts the minimal number of intermediate points so that the
maximum distance between points is step
.
The input cross
object is returned with a component,
draws
, added to each component of cross$geno
.
This is an array of size [n.ind x n.pos x n.draws] where n.pos is
the number of positions at which the simulations were performed and
n.draws is the number of replicates. Attributes "error.prob"
,
"step"
, and "off.end"
are set to the values of the
corresponding arguments, for later reference.
After performing the forward-backward equations, we draw from \(Pr(g_1 = v | O)\) and then \(Pr(g_{k+1} = v | O, g_k = u)\).
In the case of the 4-way cross, with a sex-specific map, we assume a constant ratio of female:male recombination rates within the inter-marker intervals.
# NOT RUN {
data(fake.f2)
# }
# NOT RUN {
fake.f2 <- sim.geno(fake.f2, step=2, n.draws=8)
# }
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