makefreq {adegenet} | R Documentation |
The function makefreq
generates a table of allelic
frequencies from an object of class genpop
.
makefreq(x,quiet=FALSE,missing=NA,truenames=TRUE)
x |
an object of class genpop . |
quiet |
logical stating whether a conversion message must be printed (TRUE,default) or not (FALSE). |
missing |
treatment for missing values. Can be NA, 0 or "mean" (see details) |
truenames |
a logical indicating whether true labels (as opposed to generic labels) should be used to name the output. |
There are 3 treatments for missing values:
- NA: kept as NA.
- 0: missing values are considered as zero. Recommended for a PCA on
compositionnal data.
- "mean": missing values are given the mean frequency of the
corresponding allele. Recommended for a centred PCA.
Returns a list with the following components:
tab |
matrix of allelic frequencies (rows: populations; columns: alleles). |
nobs |
number of observations (i.e. alleles) for each population x locus combinaison. |
call |
the matched call |
Thibaut Jombart jombart@biomserv.univ-lyon1.fr
data(microbov) obj1 <- microbov obj2 <- genind2genpop(obj1) Xfreq <- makefreq(obj2,missing="mean") if(require(ade4)){ # perform a correspondance analysis on counts data Xcount <- genind2genpop(obj1,missing="chi2") ca1 <- dudi.coa(as.data.frame(Xcount@tab),scannf=FALSE) s.label(ca1$li,sub="Correspondance Analysis",csub=1.2) add.scatter.eig(ca1$eig,nf=2,xax=1,yax=2,posi="topleft") # perform a principal component analysis on frequency data pca1 <- dudi.pca(Xfreq$tab,scale=FALSE,scannf=FALSE) s.label(pca1$li,sub="Principal Component Analysis",csub=1.2) add.scatter.eig(pca1$eig,nf=2,xax=1,yax=2,posi="top") }