Compute the phylogenetically independent contrasts using the method described by Felsenstein (1985).
a numeric vector.
an object of class
logical, indicates whether the contrasts should be
scaled with their expected variances (default to
logical, indicates whether the expected
variances of the contrasts should be returned (default to
x has names, its values are matched to the tip labels of
phy, otherwise its values are taken to be in the same order
than the tip labels of
The user must be careful here since the function requires that both
series of names perfectly match. If both series of names do not match,
the values in the
x are taken to be in the same order than the
tip labels of
phy, and a warning message is issued.
either a vector of phylogenetically independent contrasts (if
var.contrasts = FALSE), or a two-column matrix with the
phylogenetically independent contrasts in the first column and their
expected variance in the second column (if
TRUE). If the tree has node labels, these are used as labels of the
rescaled.tree = TRUE, a list is returned with two elements
named “contr” with the above results and “rescaled.tree” with the
tree and its rescaled branch lengths (see Felsenstein 1985).
Felsenstein, J. (1985) Phylogenies and the comparative method. American Naturalist, 125, 1–15.
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### The example in Phylip 3.5c (originally from Lynch 1991) 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) names(X) <- names(Y) <- c("Homo", "Pongo", "Macaca", "Ateles", "Galago") pic.X <- pic(X, tree.primates) pic.Y <- pic(Y, tree.primates) cor.test(pic.X, pic.Y) lm(pic.Y ~ pic.X - 1) # both regressions lm(pic.X ~ pic.Y - 1) # through the origin unlink("ex.tre") # delete the file "ex.tre"
Pearson's product-moment correlation data: pic.X and pic.Y t = -0.85623, df = 2, p-value = 0.4821 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9874751 0.8823934 sample estimates: cor -0.5179156 Call: lm(formula = pic.Y ~ pic.X - 1) Coefficients: pic.X 0.4319 Call: lm(formula = pic.X ~ pic.Y - 1) Coefficients: pic.Y 0.998
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