Description Usage Arguments Details Value Author(s) Examples
The first three functions extract information on loci,
expand.genotype
creates a table of all possible genotypes given
a set of alleles, proba.genotype
calculates expected
probabilities of genotypes under Hardy–Weinberg equilibrium, and the
last two functions test whether a locus is a SNP or whether a
gentotype is phased.
1 2 3 4 5 6 7  getPloidy(x)
getAlleles(x)
getGenotypes(x)
expand.genotype(n, alleles = NULL, ploidy = 2, matrix = FALSE)
proba.genotype(alleles = c("1", "2"), p, ploidy = 2)
is.snp(x)
is.phased(x)

x 
an object of class 
n 
an integer giving how many alleles to consider (ignored if

alleles 
the allele names as a vector of mode character. 
ploidy 
an integer giving the ploidy level (either 2 or 4 for the moment). 
matrix 
a logical specifying whether to return the genotypes in a matrix or as a character vector. 
p 
a vector of allele probabilities; if missing, equal probabilities are assumed. 
expand.genotype
and proba.genotype
accept any level of
ploidy and any number of alleles.
For is.snp
, a locus is defined as a SNP if it has two alleles
and their labels are made of a single character (e.g., A and T, or 1
and 2, but not A and AT).
getPloidy
returns the ploidy level of all loci in an object of
class "loci"
as a numeric vector.
getAlleles
and getGenotypes
return the alleles and
genotypes, respectively, observed in all loci in an object of class
"loci"
as a list.
expand.genotype
returns a character vector (the default) or a
matrix where the rows are the genotypes and the columns are the
alleles. The matrix is numeric by default, or character if the
argument alleles
is given.
proba.genotype
returns a numeric vector with names set as the
genotypes.
is.snp
returns a logical vector specifying whether each locus
is a SNP.
is.phased
returns a matrix of the same size than the original
data specifying whether each genotype is phased or not.
Emmanuel Paradis
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  data(jaguar)
X < jaguar[, 1:2]
getAlleles(X)
getGenotypes(X)
expand.genotype(2)
expand.genotype(2, LETTERS[1:3])
expand.genotype(3, ploidy = 4)
proba.genotype() # classical HWE with 2 alleles
## an octoploid with a sixallele locus (1287 possible genotypes):
length(p < proba.genotype(alleles = LETTERS[1:6], ploidy = 8))
max(p) # ~ 0.006
## back to the jaguar data:
s < summary(X)
## allele counts from the first locus:
p < s[[1]]$allele
## expected probabilities for the 136 possible genotypes...
proba.genotype(names(p), p/sum(p))
## ... to be compared with s[[1]]$genotype

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