makefreq: Compute allelic frequencies

In adegenet: Exploratory Analysis of Genetic and Genomic Data

Compute allelic frequencies

Description

The function makefreq is a generic to compute allele frequencies. These can be derived for genind or genpop objects. In the case of genind objects, data are kept at the individual level, but standardised so that allele frequencies sum up to 1.

Usage

makefreq(x, ...)

## S4 method for signature 'genind'
makefreq(x, quiet = FALSE, missing = NA, truenames = TRUE, ...)

## S4 method for signature 'genpop'
makefreq(x, quiet = FALSE, missing = NA, truenames = TRUE, ...)

Arguments

x	a genind or genpop object.
...	further arguments (curently unused)
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	deprecated; there for backward compatibility

Details

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.

Note that this function is now a simple wrapper for the accessor tab.

Value

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

Author(s)

Thibaut Jombart [e-mail]

See Also

genpop

Examples

## Not run: 
data(microbov)
obj1 <- microbov
obj2 <- genind2genpop(obj1)

# perform a correspondance analysis on counts data
Xcount <- tab(obj2, NA.method="zero")
ca1 <- dudi.coa(Xcount,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
Xfreq <- makefreq(obj2, missing="mean")
Xfreq <- tab(obj2, NA.method="mean") # equivalent to line above
pca1 <- dudi.pca(Xfreq,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")

## End(Not run)

adegenet documentation built on Feb. 16, 2023, 6 p.m.