Correlogram {ape} | R Documentation |
Compute a correlogram from taxonomic variables or from a phylogenetic tree with branch lengths.
The first method relies on the dist.taxo
function, and the second relies on the discrete.dist
function.
Both methods send an object of class 'correlogram' to be plotted by
the plot.correlogram
method.
correlogram.formula(formula, data) correlogram.phylo(x, phy, nclass = NULL, breaks = NULL)
x |
a vector containing the data to plot |
formula |
a formula of the kind y ~ g1/../gn , where y is the data to plot and all g are the nested levels |
data |
a dataframe containing all variables in the formula |
phy |
an object of class "phylo" width edge lengths |
breaks,nclass |
The class bounds or the number of classes to use. If both are NULL, compute Moran's I on the whole distance matrix. |
See example of the Moran.I
function to see how the correlogram.formula
function works.
To deal with phylogenies, the correlogram.phylo
function creates classes according to distances intervals.
Such intervals may be specified using the breaks
argument or by giving a number of classes (nclass
argument).
An object of class 'correlogram', containing:
obs |
all measured Moran's I |
p.values |
the p-values of each I |
labels |
the names of each level |
correlogram.phylo
will return NAs if void classes are used.
This may happen if breaks
if not properly defined, or sometimes with the nclass= argument, depending on the tree used.
Usually, you'll have to pull classes.
Julien Dutheil julien.dutheil@univ-montp2.fr
library(ape)### (the same analysis than in help(pic)...) data(carnivora) # Using the formula interface: co <- correlogram.formula(log10(SW) ~ Order/SuperFamily/Family/Genus, data=carnivora) co plot(co) # Using the phylo interface: cat("((((Homo:0.21,Pongo:0.21):0.28,", "Macaca:0.49):0.13,Ateles:0.62):0.38,Galago:1.00);", file = "ex.tre", sep = "\n") tree.primates <- read.tree("ex.tre") X <- c(4.09434, 3.61092, 2.37024, 2.02815, -1.46968) Y <- c(4.74493, 3.33220, 3.36730, 2.89037, 2.30259) # Since this is a small tree, 2 classes is a reasonable number: coX <- correlogram.phylo(X, tree.primates, nclass=2) coY <- correlogram.phylo(Y, tree.primates, nclass=2) plot(coX) plot(coY) # Nothing significant... # Computing Moran's I on the whole matrix: coX2 <- correlogram.phylo(X, tree.primates); coX2 # Significant at the 5 coY2 <- correlogram.phylo(Y, tree.primates); coY2 # Not significant unlink("ex.tre") # delete the file "ex.tre"