ape (version 1.8-4)

Correlogram: Compute a correlogram

Description

Compute a correlogram from taxonomic variables or from a phylogenetic tree with branch lengths. The first method relies on the weight.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. For the correlogram.formula function, if several y are specified, an object of class correlogramList (a list of correlogram objects) is sent.

Usage

correlogram.formula(formula, data = NULL, use="all.obs")
  correlogram.phylo(x, phy, nclass = NULL, breaks = NULL)

Value

An object of class correlogram, containing:obsall measured Moran's Ip.valuesthe p-values of each Ilabelsthe names of each levelor an object of class correlogramList containing a list of correlogram objects.

Warning

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.

Details

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).

See Also

plot.correlogram, plot.correlogramList

Examples

library(ape)
  data(carnivora)
  ### Using the formula interface:
  co <- correlogram.formula(
			log10(SW) ~ Order/SuperFamily/Family/Genus,
			data=carnivora)
  co
  plot(co)
	### Several correlograms on the same plot:
  cos <- correlogram.formula(
			log10(SW) + log10(FW) ~ Order/SuperFamily/Family/Genus,
			data=carnivora)
  names(cos)
  plot(cos)

  ### Using the phylo interface:
  ### (the same analysis than in help(pic)...)

  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 = "")
  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% level
  coY2 <- correlogram.phylo(Y, tree.primates); coY2
  ### Not significant
  unlink("ex.tre") # delete the file "ex.tre"