pegas (version 0.12)

heterozygosity: Heterozygosity at a Locus Using Gene Frequencies

Description

Thes functions compute the mean heterozygosity(ies) from gene frequencies, and return optionally the associated variance(s).

Usage

H(x, ...)
# S3 method for loci
H(x, variance = FALSE, observed = FALSE, ...)
# S3 method for default
H(x, variance = FALSE, ...)
heterozygosity(x, variance = FALSE)

Arguments

x

an object of class "loci", or vector or a factor.

variance

a logical indicating whether the variance of the estimated heterozygosity should be returned (TRUE), the default being FALSE.

observed

a logical specifying whether to calculate the observed heterozygosity.

...

currently unused

Value

For the default method: a numeric vector of length one with the estimated mean heterozygosity (the default), or of length two if the variance is returned.

For the "loci" method: a numeric matrix with one, two, or three columns with a row for each locus and the values of heterozygosity as columns.

Details

The argument x can be either a factor or a vector. If it is a factor, then it is taken to give the individual alleles in the population. If it is a numeric vector, then its values are taken to be the numbers of each allele in the population. If it is a non-numeric vector, it is a coerced as a factor.

The mean heterozygosity is estimated with:

$$\hat{H} = \frac{n}{n-1} \left(1 - \sum_{i=1}^k p_i^2 \right)$$

where \(n\) is the number of genes in the sample, \(k\) is the number of alleles, and \(p_i\) is the observed (relative) frequency of the \(i\)th allele.

References

Nei, M. (1987) Molecular evolutionary genetics. New York: Columbia University Press.

See Also

theta.s

Examples

# NOT RUN {
data(jaguar)
H(jaguar, TRUE, TRUE)
## use the (old) default method:
## convert the data and compute frequencies:
S <- summary(jaguar)
## compute H for all loci:
sapply(S, function(x) H(x$allele))
## ... and its variance
sapply(S, function(x) H(x$allele, variance = TRUE))
# }