genpop {adegenet} | R Documentation |
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.
is.genpop(x) as.genpop(tab = NULL, prevcall = NULL) ## S3 method for class 'genpop': print(x, ...) ## S3 method for class 'genpop': summary(object, ...)
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 . |
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. |
Thibaut Jombart jombart@biomserv.univ-lyon1.fr
makefreq
, genind
, import2genind
, genetix2genind
,
genepop2genind
, fstat2genind
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") }