adegenet (version 1.0-0)

genpop: adegenet class for allele counts in populations

Description

The objects of class genpop contain alleles counts for several loci. It consists in a list with several components (see value section). Such object is obtained using genind2genpop which converts individuals genotypes of known population into a genpop object. Note that the function summary of a genpop object returns a list of components.

Usage

is.genpop(x)
as.genpop(tab = NULL, prevcall = NULL)
print.genpop(x,...)
summary.genpop(object,...)

Arguments

x

an object of class genpop.

tab

a populations x alleles matrix of allele counts.

prevcall

call of an object, for internal use.

...

other -unused- arguments

object

an object of class genpop.

Value

• tab
  matrix of alleles counts for each combinaison of population -in rows- and alleles -in columns-. Rows and columns are given generic names.
• pop.names
  character vector containing the real names of the populations
• loc.names
  character vector containing the real names of the loci
• loc.nall
  integer vector giving the number of alleles per locus
• loc.fac
  locus factor for the columns of tab
• all.names
  list having one component per locus, each containing a character vector of alleles names
• call
  the matched call
• npop
  (summary) number of populations.
• loc.nall
  (summary) number of alleles per locus.
• pop.nall
  (summary) number of alleles per population.
• NA.perc
  (summary) percentage of - appearing - missing data.

See Also

makefreq, genind, import2genind, genetix2genind, genepop2genind, fstat2genind

Examples

obj1 <- import2genind(system.file("files/nancycats.gen",
package="adegenet"))
is.genpop(obj1)
summary(obj1)
obj1


obj2 <- genind2genpop(obj1)
is.genpop(obj2)
obj2

if(require(ade4)){
data(microsatt)
# use as.genpop to convert convenient count tab to genpop
obj3 <- as.genpop(microsatt$tab)
obj3

all(obj3$tab==microsatt$tab)
all(obj3$pop.names==rownames(microsatt$tab))
# it worked

# perform a correspondance analysis
obj4 <- genind2genpop(obj1,missing="replace")
ca1 <- dudi.coa(as.data.frame(obj4$tab),scannf=FALSE)
s.label(ca1$li,sub="Correspondance Analysis",csub=2)
add.scatter.eig(ca1$eig,2,xax=1,yax=2,posi="top")
}