Apply the batch mapping algorithm using overlapping windows.
map_overlapping_batches(
input.seq,
size = 50,
overlap = 15,
phase_cores = 1,
verbose = FALSE,
seeds = NULL,
tol = 1e-04,
rm_unlinked = TRUE,
max.gap = FALSE,
parallelization.type = "PSOCK"
)An object of class sequence, which is a list containing the
following components:
a vector containing the
(ordered) indices of markers in the sequence, according to the input file.
a vector with the linkage phases between markers
in the sequence, in corresponding positions. -1 means that there are
no defined linkage phases.
a vector with the
recombination frequencies between markers in the sequence. -1 means
that there are no estimated recombination frequencies.
log-likelihood of the corresponding linkage map.
name of the object of class outcross with the raw
data.
name of the object of class rf.2pts with the
2-point analyses.
an object of class sequence.
The center size around which an optimum is to be searched
The desired overlap between batches
The number of parallel processes to use when estimating the phase of a marker. (Should be no more than 4)
A logical, if TRUE its output progress status information.
A vector of phase information used as seeds for the first batch
tolerance for the C routine, i.e., the value used to evaluate convergence.
When some pair of markers do not follow the linkage criteria,
if TRUE one of the markers is removed and map is performed again.
the marker will be removed if it have gaps higher than this defined threshold in both sides
one of the supported cluster types. This should be either PSOCK (default) or FORK.
This algorithm implements the overlapping batch maps for high density marker sets. The mapping problem is reduced to a number of subsets (batches) which carry information forward in order to more accurately estimate recombination fractions and phasing. It is a adapted version of map.overlapping.batches function of BatchMap package. The main differences are that this onemap version do not have the option to reorder the markers according to ripple algorithm and, if the it finds markers that do not reach the linkage criterias, the algorithm remove the problematic marker and repeat the analysis. Than, the output map can have few markers compared with the input.seq.
pick_batch_sizes, map