pic | R Documentation |
Compute the phylogenetically independent contrasts using the method described by Felsenstein (1985).
pic(x, phy, scaled = TRUE, var.contrasts = FALSE,
rescaled.tree = FALSE)
x |
a numeric vector. |
phy |
an object of class |
scaled |
logical, indicates whether the contrasts should be
scaled with their expected variances (default to |
var.contrasts |
logical, indicates whether the expected
variances of the contrasts should be returned (default to
|
rescaled.tree |
logical, if |
If 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 phy
.
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 var.contrasts =
TRUE
). If the tree has node labels, these are used as labels of the
returned object.
If 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).
Emmanuel Paradis
Felsenstein, J. (1985) Phylogenies and the comparative method. American Naturalist, 125, 1–15.
read.tree
, compar.gee
,
compar.lynch
, pic.ortho
,
varCompPhylip
### The example in Phylip 3.5c (originally from Lynch 1991)
x <- "((((Homo:0.21,Pongo:0.21):0.28,Macaca:0.49):0.13,Ateles:0.62):0.38,Galago:1.00);"
tree.primates <- read.tree(text = x)
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
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