amova | R Documentation |
This function performs a hierarchical analysis of molecular variance as described in Excoffier et al. (1992). This implementation accepts any number of hierarchical levels.
amova(formula, data = NULL, nperm = 1000, is.squared = FALSE)
## S3 method for class 'amova'
print(x, ...)
getPhi(sigma2)
write.pegas.amova(x, file = "")
formula |
a formula giving the AMOVA model to be fitted with the
distance matrix on the left-hand side of the |
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 |
sigma2 |
a named vector of variance components. |
file |
a file name. |
... |
unused (here for compatibility. |
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).
The function getPhi()
is a convenience function for extracting a
table of hierarchical Phi-statistics for reporting. This will be an N+1
by N matrix where N is the number of hierarchcial levels and GLOBAL is
always the first row of the matrix. The matrix can read as COLUMN in ROW.
If the variance components passed to getPhi() are not named, they will be reported as "level 1", "level 2", etc.
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.
If there are more than three levels, approximate formulae are used to estimate the variance components.
If there is an error message like this:
Error in FUN(X[[1L]], ...) : 'bin' must be numeric or a factor
it may be that the factors you use in the formula were not read
correctly. You may convert them with the function factor
, or,
before reading your data files, do this command (in case this option
was modified):
options(stringsAsFactors = TRUE)
Emmanuel Paradis, Zhian N. Kamvar, and Brian Knaus
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.
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.
### 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)))
(d_gp <- amova(d ~ g/p, nperm = 100)) # 2 levels
sig2 <- setNames(d_gp$varcomp$sigma2, rownames(d_gp$varcomp))
getPhi(sig2) # Phi table
amova(d ~ p, nperm = 100) # 1 level
amova(d ~ g, nperm = 100)
## 3 levels (quite slow):
## Not run:
pop <- gl(64, 5, labels = paste0("pop", 1:64))
region <- gl(16, 20, labels = paste0("region", 1:16))
conti <- gl(4, 80, labels = paste0("conti", 1:4))
dd <- as.dist(matrix(runif(320^2), 320))
(dd_crp <- amova(dd ~ conti/region/pop, nperm = 100))
sig2 <- setNames(dd_crp$varcomp$sigma2, rownames(dd_crp$varcomp))
getPhi(sig2)
## End(Not run)
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