pegas (version 0.10)

amova: Analysis of Molecular Variance

Description

This function performs a hierarchical analysis of molecular variance as described in Excoffier et al. (1992). This implementation accepts any number of hierarchical levels.

Usage

amova(formula, data = NULL, nperm = 1000, is.squared = FALSE)
# S3 method for amova
print(x, ...)

Arguments

formula

a formula giving the AMOVA model to be fitted with the distance matrix on the left-hand side of the ~, and the population, region, etc, levels on its right-hand side (see details).

data

an optional data frame where to find the hierarchical levels; by default they are searched for in the user's workspace.

nperm

the number of permutations for the tests of hypotheses (1000 by default). Set this argument to 0 to skip the tests and simply estimate the variance components.

is.squared

a logical specifying whether the distance matrix has already been squared.

x

an object of class "amova".

…

unused (here for compatibility.

Value

An object of class "amova" which is a list with a table of sums of square deviations (SSD), mean square deviations (MSD), and the number of degrees of freedom, and a vector of variance components.

Details

The formula must be of the form d ~ A/B/... where d is a distance object, and A, B, etc, are the hierarchical levels from the highest to the lowest one. Any number of levels is accepted, so specifying d ~ A will simply test for population differentiation.

It is assumed that the rows of the distance matrix are in the same order than the hierarchical levels (which may be checked by the user).

References

Excoffier, L., Smouse, P. E. and Quattro, J. M. (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics, 131, 479--491.

See Also

amova in ade4 for an implementation of the original Excoffier et al.'s model; adonis in vegan for a general (multivariate) implementation of an ANOVA framework with distances.

Examples

# NOT RUN {
### All examples below have 'nperm = 100' for faster execution times.
### The default 'nperm = 1000' is recommended.
require(ape)
data(woodmouse)
d <- dist.dna(woodmouse)
g <- factor(c(rep("A", 7), rep("B", 8)))
p <- factor(c(rep(1, 3), rep(2, 4), rep(3, 4), rep(4, 4)))
amova(d ~ g/p, nperm = 100) # 2 levels
amova(d ~ p, nperm = 100) # 1 level
amova(d ~ g, nperm = 100)

## 3 levels (quite slow):
# }
# NOT RUN {
pop <- gl(64, 5, labels = paste0("pop", 1:64))
region <- gl(16, 20, labels = paste0("region", 1:16))
conti <- gl(4, 80, labels = paste0("conti", 1:4))
dd <- as.dist(matrix(runif(320^2), 320))
amova(dd ~ conti/region/pop, nperm = 100)
# }