Description Usage Arguments Details Value See Also Examples
Uses a hidden Markov model to calculate arg max Pr(g  O) where g is the underlying sequence of true genotypes and O is the observed multipoint marker data, with possible allowance for genotyping errors.
1 2 3 4 5 6 7 8 9 
cross 
Object of class 
map 
Genetic map of markers. May include pseudomarker
locations (that is, locations that are not within the marker
genotype data). If NULL, the genetic map in 
error_prob 
Assumed genotyping error probability 
map_function 
Character string indicating the map function to use to convert genetic distances to recombination fractions. 
lowmem 
If 
quiet 
If 
cores 
Number of CPU cores to use, for parallel calculations.
(If 
We use a hidden Markov model to find, for each individual on each chromosome, the most probable sequence of underlying genotypes given the observed marker data.
Note that we break ties at random, and our method for doing this may introduce some bias.
Consider the results with caution; the most probable sequence can
have very low probability, and can have features that are quite
unusual (for example, the number of recombination events can be too
small). In most cases, the results of a single imputation with
sim_geno()
will be more realistic.
An object of class "viterbi"
: a list of twodimensional
arrays of imputed genotypes, individuals x positions.
Also contains three attributes:
crosstype
 The cross type of the input cross
.
is_x_chr
 Logical vector indicating whether chromosomes
are to be treated as the X chromosome or not, from input cross
.
alleles
 Vector of allele codes, from input
cross
.
sim_geno()
, maxmarg()
, cbind.viterbi()
, rbind.viterbi()
1 2 3  grav2 < read_cross2(system.file("extdata", "grav2.zip", package="qtl2"))
map_w_pmar < insert_pseudomarkers(grav2$gmap, step=1)
g < viterbi(grav2, map_w_pmar, error_prob=0.002)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.