Version:	1.1-17
Title:	Exploratory Analyses for the Phylogenetic Comparative Method
Depends:	methods, ade4 (≥ 1.7-10)
Imports:	phylobase, ape, adegenet
Description:	Multivariate tools to analyze comparative data, i.e. a phylogeny and some traits measured for each taxa. The package contains functions to represent comparative data, compute phylogenetic proximities, perform multivariate analysis with phylogenetic constraints and test for the presence of phylogenetic autocorrelation. The package is described in Jombart et al (2010) <doi:10.1093/bioinformatics/btq292>.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
LazyLoad:	yes
RoxygenNote:	7.3.2
Encoding:	UTF-8
URL:	https://github.com/adeverse/adephylo
BugReports:	https://github.com/adeverse/adephylo/issues
NeedsCompilation:	yes
Packaged:	2025-03-14 10:48:51 UTC; siberchicot
Author:	Stéphane Dray [aut], Thibaut Jombart [aut], Anders Ellern Bilgrau [ctb], Aurélie Siberchicot [ctb, cre]
Maintainer:	Aurélie Siberchicot <aurelie.siberchicot@univ-lyon1.fr>
Repository:	CRAN
Date/Publication:	2025-03-14 17:10:10 UTC

The adephylo package

Description

This package is devoted to exploratory analysis of phylogenetic comparative data. It re-implements and extends phylogenetic procedures from the ade4 package (which are now deprecated).

Details

Comparative data (phylogeny+traits) are handled as phylo4d-class objects, a canonical class implemented by the phylobase package. Trees are handled as phylo objects (from the ape package) or as phylo4-class objects (phylobase's extension of phylo objects).

Main functionalities of adephylo are summarized below.

=== TOPOLOGICAL INFORMATION ===
Several functions allow one to retrieve topological information from a tree; such information can be used, for instance, as a basis to compute distances or proximities between tips.

- listDD: lists the direct descendants from each node of a tree.

- listTips: lists the tips descending from each node of a tree.

- .tipToRoot: finds the set of nodes between a tip and the root of a tree.

- sp.tips: finds the shortest path between tips of a tree.

- treePart: defines partitions of tips reflecting the topology of a tree. This function can output non-independent dummy vectors, or alternatively an orthonormal basis used by the orthogram procedure.

=== PHYLOGENETIC PROXIMITIES/DISTANCES ===
Several phylogenetic proximities and distances are implemented. Auxiliary function easing the computation of other distances/proximities are also provided:

- distRoot: computes different distances of a set of tips to the root.

- distTips: computes different pairwise distances in a set of tips.

- proxTips: computes different proximities between a set of tips.

=== MEASURES/TESTS OF PHYLOGENETIC AUTOCORRELATION ===
Several procedures allow one to measure, and/or test phylogenetic signal in biological traits:

- abouheif.moran: performs Abouheif's test, designed to detect phylogenetic autocorrelation in a quantitative trait. This implementation is not based on original heuristic procedure, but on the exact formulation proposed by Pavoine et al. (2008), showing that the test is in fact a Moran's index test. This implementation further extends the procedure by allowing any measure of phylogenetic proximity (5 are proposed).

- orthogram: performs the orthonormal decomposition of variance of a quantitative variable on an orthonormal basis as in Ollier et al. (2005). It also returns the results of five non parametric tests associated to the variance decomposition.

- moran.idx: computes Moran's index of autocorrelation given a variable and a matrix of proximities among observations (no test).

=== MODELLING/INVESTIGATION OF PHYLOGENETIC SIGNAL ===
Rather than testing or measuring phylogenetic autocorrelation, these procedures can be used for further investigation of phylogenetic signal. Some, like me.phylo, can be used to remove phylogenetic autocorrelation. Others can be used to understand the nature of this autocorrelation (i.e., to ascertain which traits and tips are concerned by phylogenetic non-independence).

- me.phylo/orthobasis.phylo: these synonymous functions compute Moran's eigenvectors (ME) associated to a tree. These vectors model different observable phylogenetic signals. They can be used as covariables to remove phylogenetic autocorrelation from data.

- orthogram: the orthogram mentioned above also provides a description of how biological variability is structured on a phylogeny.

- ppca: performs a phylogenetic Principal Component Analysis (pPCA, Jombart et al. 2010). This multivariate method investigates phylogenetic patterns in a set of quantitative traits.

=== GRAPHICS ===
Some plotting functions are proposed, most of them being devoted to representing phylogeny and a quantitative information at the same time.

- table.phylo4d: fairly customisable way of representing traits onto the tips of a phylogeny. Several traits can be plotted in a single graphic.

- bullseye: an alternative to table.phylo4d based on fan-like representation, better for large trees.

- scatter.ppca, screeplot.ppca, plot.ppca: several plots associated to a phylogenetic principal component analysis (see ppca).

=== DATASETS ===
Several datasets are also proposed. Some of these datasets replace former version from ade4, which are now deprecated. Here is a list of available datasets: carni19, carni70, lizards, maples, mjrochet, palm, procella, tithonia, and ungulates.

To cite adephylo, please use the reference given by citation("adephylo").

Author(s)

Thibaut Jombart <tjombart@imperial.ac.uk>
with contributions Stephane Dray <stephane.dray@univ-lyon1.fr> and Anders Ellern Bilgrau <abilgrau@math.aau.dk>.
Parts of former code from ade4 by Daniel Chessel and Sebastien Ollier.

See Also

The ade4 package for multivariate analysis.

Low-level auxiliary functions for adephylo

Description

These hidden functions are utils for adephylo, used by other functions. Regular users can use them as well, but no validity checks are performed for the arguments: speed is here favored over safety. Most of these functions handle trees inheriting phylo4-class class.

Usage

.tipToRoot(x, tip, root, include.root = FALSE)

Arguments

Details

.tipToRoot finds the set of nodes between a tip and the root of a tree.

Value

.tipToRoot: a vector of named integers identifying nodes.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

Examples

if(require(ape) & require(phylobase)){
## make a tree
x <- as(rtree(20),"phylo4")
plot(x,show.node=TRUE)

## .tipToRoot
root <- rootNode(x)
.tipToRoot(x, 1, root)
lapply(1:nTips(x), function(i) .tipToRoot(x, i, root))
}

Abouheif's test based on Moran's I

Description

The test of Abouheif (1999) is designed to detect phylogenetic autocorrelation in a quantitative trait. Pavoine et al. (2008) have shown that this tests is in fact a Moran's I test using a particular phylogenetic proximity between tips (see details). The function abouheif.moran performs basically Abouheif's test for several traits at a time, but it can incorporate other phylogenetic proximities as well.

Usage

abouheif.moran(
  x,
  W = NULL,
  method = c("oriAbouheif", "patristic", "nNodes", "Abouheif", "sumDD"),
  f = function(x) {
     1/x
 },
  nrepet = 999,
  alter = c("greater", "less", "two-sided")
)

Arguments

Details

Note that the original Abouheif's proximity (Abouheif, 1999; Pavoine et al. 2008) unifies Moran's I and Geary'c tests (Thioulouse et al. 1995).

abouheif.moran can be used in two ways:
- providing a data.frame of traits (x) and a matrix of phylogenetic proximities (W)
- providing a phylo4d-class object (x) and specifying the type of proximity to be used (method).

W is a squared symmetric matrix whose terms are all positive or null.

W is firstly transformed in frequency matrix A by dividing it by the total sum of data matrix :

a_{ij} = \frac{W_{ij}}{\sum_{i=1}^{n}\sum_{j=1}^{n}W_{ij}}

The neighbouring weights is defined by the matrix D = diag(d_1,d_2, \ldots) where d_i = \sum_{j=1}^{n}W_{ij}. For each vector x of the data frame x, the test is based on the Moran statistic x^{t}Ax where x is D-centred.

Value

Returns an object of class krandtest (randomization tests from ade4), containing one Monte Carlo test for each trait.

Author(s)

Original code from ade4 (gearymoran function) by Sebastien Ollier
Adapted and maintained by Thibaut Jombart <tjombart@imperial.ac.uk>.

References

Thioulouse, J., Chessel, D. and Champely, S. (1995) Multivariate analysis of spatial patterns: a unified approach to local and global structures. Environmental and Ecological Statistics, 2, 1–14.

See Also

- gearymoran from the ade4 package
- Moran.I from the ape package for the classical Moran's I test.

Examples

if(require(ade4)&& require(ape) && require(phylobase)){
## load data
data(ungulates)
tre <- read.tree(text=ungulates$tre)
x <- phylo4d(tre, ungulates$tab)

## Abouheif's tests for each trait
myTests <- abouheif.moran(x)
myTests
plot(myTests)

## a variant using another proximity
plot(abouheif.moran(x, method="nNodes") )

## Another example

data(maples)
tre <- read.tree(text=maples$tre)
dom <- maples$tab$Dom

## Abouheif's tests for each trait (equivalent to Cmean)
W1 <- proxTips(tre,method="oriAbouheif")
abouheif.moran(dom,W1)

## Equivalence with moran.idx

W2 <- proxTips(tre,method="Abouheif")
abouheif.moran(dom,W2)
moran.idx(dom,W2) 
}

Fan-like phylogeny with possible representation of traits on tips

Description

This function represents a phylogeny as a fan, using circles to provide a legend for distances and optionally colored symbols to represent traits associated to the tips of the tree. This function uses and is compatible with ape's plot.phylo.

Usage

bullseye(
  phy,
  traits = NULL,
  col.tips.by = NULL,
  col.pal = spectral,
  circ.n = 6,
  circ.bg = transp("royalblue", 0.1),
  circ.unit = NULL,
  legend = TRUE,
  leg.posi = "bottomleft",
  leg.title = "",
  leg.bg = "white",
  traits.inset = 1.1,
  traits.space = 0.05,
  traits.pch = 19,
  traits.cex = 1,
  alpha = 1,
  axis = TRUE,
  ...
)

Arguments

Value

No return value, function produces only a plot.

The phylo4d-class class for storing phylogeny+data.

plot.phylo from the ape package.

dotchart.phylog.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

See Also

table.phylo4d for non-radial plots.

Examples

if(require(ape) && require(phylobase) && require(adegenet)){

data(lizards)
tre <- read.tree(text=lizards$hprA) # make a tree

## basic plots
bullseye(tre)
bullseye(tre, lizards$traits)

## customized
oldpar <- par(mar=c(6,6,6,6))
bullseye(tre, lizards$traits, traits.cex=sqrt(1:7), alpha=.7,
         legend=FALSE, circ.unit=10, circ.bg=transp("black",.1),
         edge.width=2)
par(oldpar)
}

Phylogeny and quantative trait of carnivora

Description

This data set describes the phylogeny of carnivora as reported by Diniz-Filho et al. (1998). It also gives the body mass of these 19 species.

Format

carni19 is a list containing the 2 following objects :

tre

is a character string giving the phylogenetic tree in Newick format.

bm

is a numeric vector which values correspond to the body mass of the 19 species (log scale).

Note

This dataset replaces the former version in ade4.

Source

Diniz-Filho, J. A. F., de Sant'Ana, C.E.R. and Bini, L.M. (1998) An eigenvector method for estimating phylogenetic inertia. Evolution, 52, 1247–1262.

Examples

if(require(ape) && require(phylobase)){

data(carni19)
tre <- read.tree(text=carni19$tre)
x <- phylo4d(tre, data.frame(carni19$bm))
table.phylo4d(x, ratio=.5, center=FALSE)
}

Phylogeny and quantitative traits of carnivora

Description

This data set describes the phylogeny of 70 carnivora as reported by Diniz-Filho and Torres (2002). It also gives the geographic range size and body size corresponding to these 70 species.

Format

carni70 is a list containing the 2 following objects:

tre

is a character string giving the phylogenetic tree in Newick format. Branch lengths are expressed as divergence times (millions of years)

tab

is a data frame with 70 species and two traits: size (body size (kg)) ; range (geographic range size (km)).

Note

This dataset replaces the former version in ade4.

Source

Diniz-Filho, J. A. F., and N. M. Torres. (2002) Phylogenetic comparative methods and the geographic range size-body size relationship in new world terrestrial carnivora. Evolutionary Ecology, 16, 351–367.

Examples

if(require(ape) && require(phylobase)){

data(carni70)
rownames(carni70$tab) <- gsub("_", ".", rownames(carni70$tab))
tre <- read.tree(text=carni70$tre)
x <- phylo4d(tre, carni70$tab)
table.phylo4d(x)

oldpar <- par(mar=rep(.1,4))
table.phylo4d(x,cex.lab=.5, show.n=FALSE, ratio=.5)


## transform size in log and test for a phylogenetic signal
size <- log(carni70$tab)[,1]
names(size) <- row.names(carni70$tab)
orthogram(size, tre)

## transform range and test for a phylogenetic signal
yrange <- scale(carni70$tab)[,2]
names(yrange) <- row.names(carni70$tab)
orthogram(yrange, tre)

par(oldpar)
}

Compute the distance of tips to the root

Description

The function distRoot computes the distance of a set of tips to the root. Several distances can be used, defaulting to the sum of branch lengths.

Usage

distRoot(
  x,
  tips = "all",
  method = c("patristic", "nNodes", "Abouheif", "sumDD")
)

Arguments

Details

Abouheif distance refers to the phylogenetic distance underlying the test of Abouheif (see references). Let P be the set of all the nodes in the path going from node1 to node2. Let DDP be the number of direct descendants from each node in P. Then, the so-called 'Abouheif' distance is the product of all terms in DDP.

sumDD refers to a phylogenetic distance quite similar to that of Abouheif. We consider the same sets P and DDP. But instead of computing the product of all terms in DDP, this distance computes the sum of all terms in DDP.

Value

A numeric vector containing one distance value for each tip.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

References

Pavoine, S.; Ollier, S.; Pontier, D. & Chessel, D. (2008) Testing for phylogenetic signal in life history variable: Abouheif's test revisited. Theoretical Population Biology: 73, 79-91.

See Also

distTips which computes the same phylogenetic distances, but between tips.

Examples

if(require(ape) & require(phylobase)){
## make a tree
x <- as(rtree(50),"phylo4")
## compute 4 different distances
met <- c("patristic","nNodes","Abouheif","sumDD")
D <- lapply(met, function(e) distRoot(x, method=e) )
names(D) <- met
D <- as.data.frame(D)

## plot these distances along with the tree
temp <- phylo4d(x, D)
table.phylo4d(temp, show.node=FALSE, cex.lab=.6)
}

Compute some phylogenetic distance between tips

Description

The function distTips computes a given distance between a set of tips of a phylogeny. A vector of tips is supplied: distances between all possible pairs of these tips are computed. The distances are computed from the shortest path between the tips. Several distances can be used, defaulting to the sum of branch lengths (see argument method).

Usage

distTips(
  x,
  tips = "all",
  method = c("patristic", "nNodes", "Abouheif", "sumDD"),
  useC = TRUE
)

Arguments

Details

An option (enabled by default) allows computations to be run using compiled C code, which is much faster than pure R code. In this case, a matrix of all pairwise distances is returned (i.e., tips argument is ignored).

Abouheif distance refers to the phylogenetic distance underlying the test of Abouheif (see references). Let P be the set of all the nodes in the path going from node1 to node2. Let DDP be the number of direct descendants from each node in P. Then, the so-called 'Abouheif' distance is the product of all terms in DDP.

sumDD refers to a phylogenetic distance quite similar to that of Abouheif. We consider the same sets P and DDP. But instead of computing the product of all terms in DDP, this distance computes the sum of all terms in DDP.

Value

An object of class dist, containing phylogenetic distances.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

References

Pavoine, S.; Ollier, S.; Pontier, D. & Chessel, D. (2008) Testing for phylogenetic signal in life history variable: Abouheif's test revisited. Theoretical Population Biology: 73, 79-91.

See Also

distTips which computes several phylogenetic distances between tips.

Examples

if(require(ape) & require(phylobase)){
## make a tree
x <- as(rtree(10),"phylo4")
plot(x, show.node=TRUE)
axisPhylo()
## compute different distances
distTips(x, 1:3)
distTips(x, 1:3, "nNodes")
distTips(x, 1:3, "Abouheif")
distTips(x, 1:3, "sumDD")

## compare C and pure R code outputs
x <- rtree(10)
all.equal(as.matrix(distTips(x)), as.matrix(distTips(x, useC=FALSE)))
all.equal(as.matrix(distTips(x, meth="nNode")),
   as.matrix(distTips(x, meth="nNode", useC=FALSE)))
all.equal(as.matrix(distTips(x, meth="Abou")),
   as.matrix(distTips(x, meth="Abou", useC=FALSE)))
all.equal(as.matrix(distTips(x, meth="sumDD")),
   as.matrix(distTips(x, meth="sumDD", useC=FALSE)))

## compare speed
x <- rtree(50)
tim1 <- system.time(distTips(x, useC=FALSE)) # old pure R version
tim2 <- system.time(distTips(x)) # new version using C
tim1[c(1,3)]/tim2[c(1,3)] # C is about a thousand time faster in this case
}

List direct descendants for all nodes of a tree

Description

The function listDD lists the direct descendants from each node of a tree. The tree can be of class phylo, phylo4-class or phylo4d-class.

Usage

listDD(x, nameBy = c("label", "number"))

Arguments

Value

A list whose components are vectors of named nodes (or tips) for a given internal node.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

See Also

listTips which lists the tips descending from each node.

treePart which defines partitions of tips according to the tree topology.

Examples

if(require(ape) & require(phylobase)){
## make a tree
x <- as(rtree(20),"phylo4")
plot(x,show.node=TRUE)
listDD(x)
}

List tips descendings from all nodes of a tree

Description

The function listTips lists the tips descending from each node of a tree. The tree can be of class phylo, phylo4-class or phylo4d-class.

Usage

listTips(x)

Arguments

Value

A list whose components are vectors of named tips for a given node.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

See Also

listDD which lists the direct descendants for each node.

treePart which defines partitions of tips according to the tree topology.

Examples

if(require(ape) & require(phylobase)){
## make a tree
x <- as(rtree(20),"phylo4")
plot(x,show.node=TRUE)
listTips(x)
}

Phylogeny and quantitative traits of lizards

Description

This data set describes the phylogeny of 18 lizards as reported by Bauwens and D\'iaz-Uriarte (1997). It also gives life-history traits corresponding to these 18 species.

Format

lizards is a list containing the 3 following objects :

traits

is a data frame with 18 species and 8 traits.

hprA

is a character string giving the phylogenetic tree (hypothesized phylogenetic relationships based on immunological distances) in Newick format.

hprB

is a character string giving the phylogenetic tree (hypothesized phylogenetic relationships based on morphological characteristics) in Newick format.

Details

Variables of lizards$traits are the following ones : mean.L (mean length (mm)), matur.L (length at maturity (mm)), max.L (maximum length (mm)), hatch.L (hatchling length (mm)), hatch.m (hatchling mass (g)), clutch.S (Clutch size), age.mat (age at maturity (number of months of activity)), clutch.F (clutch frequency).

Note

This dataset replaces the former version in ade4.

References

Bauwens, D., and D\'iaz-Uriarte, R. (1997) Covariation of life-history traits in lacertid lizards: a comparative study. American Naturalist, 149, 91–111.

See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps063.pdf (in French).

Examples

if(require(ape) && require(phylobase)){

## see data
data(lizards)
liz.tr <- read.tree(tex=lizards$hprA) # make a tree
liz <- phylo4d(liz.tr, lizards$traits) # make a phylo4d object
table.phylo4d(liz)

## compute and plot principal components
if(require(ade4)){
liz.pca1 <- dudi.pca(lizards$traits, cent=TRUE,
   scale=TRUE, scannf=FALSE, nf=2) # PCA of traits
myPC <- phylo4d(liz.tr, liz.pca1$li) # store PC in a phylo4d object
varlab <- paste("Principal \ncomponent", 1:2) # make labels for PCs
table.phylo4d(myPC, ratio=.8, var.lab=varlab) # plot the PCs
add.scatter.eig(liz.pca1$eig,2,1,2,posi="topleft", inset=c(0,.15))
title("Phylogeny and the principal components")

## compute a pPCA ##
## remove size effect
temp <- lapply(liz.pca1$tab, function(e) residuals(lm(e~-1+liz.pca1$li[,1])) )
temp <- data.frame(temp)
row.names(temp) <- tipLabels(liz)

## build corresponding phylo4d object
liz.noSize <- phylo4d(liz.tr, temp)
ppca1 <- ppca(liz.noSize, method="Abouheif", scale=FALSE, scannf=FALSE)
plot(ppca1)

}
}

Phylogeny and quantitative traits of flowers

Description

This data set describes the phylogeny of 17 flowers as reported by Ackerly and Donoghue (1998). It also gives 31 traits corresponding to these 17 species.

Format

tithonia is a list containing the 2 following objects : - tre: a character string giving the phylogenetic tree in Newick format.
- tab: a data frame with 17 species and 31 traits.

Note

This dataset replaces the former version in ade4.

References

Ackerly, D. D. and Donoghue, M.J. (1998) Leaf size, sappling allometry, and Corner's rules: phylogeny and correlated evolution in Maples (Acer). American Naturalist, 152, 767–791.

Examples

if(require(ape) && require(phylobase)){

data(maples)

## see the tree
tre <- read.tree(text=maples$tre)
plot(tre)
axisPhylo()

## look at two variables
dom <- maples$tab$Dom
bif <- maples$tab$Bif
plot(bif,dom,pch = 20)
abline(lm(dom~bif)) # a strong negative correlation ?
summary(lm(dom~bif))
cor.test(bif,dom)

## look at the two variables onto the phylogeny
temp <- phylo4d(tre, data.frame(dom,bif, row.names=tre$tip.label))
table.phylo4d(temp) # correlation is strongly linked to phylogeny

## use ape's PIC (phylogenetic independent contrasts)
pic.bif <- pic(bif, tre)
pic.dom <- pic(dom, tre)
cor.test(pic.bif, pic.dom) # correlation is no longer significant
}

Phylogeny and quantitative traits of teleos fishes

Description

This data set describes the phylogeny of 49 teleos fishes as reported by Rochet et al. (2000). It also gives life-history traits corresponding to these 49 species.

Format

mjrochet is a list containing the 2 following objects :

tre

is a character string giving the phylogenetic tree in Newick format.

tab

is a data frame with 49 rows and 7 traits.

Details

Variables of mjrochet$tab are the following ones : tm (age at maturity (years)), lm (length at maturity (cm)), l05 (length at 5 per cent survival (cm)), t05 (time to 5 per cent survival (years)), fb (slope of the log-log fecundity-length relationship), fm (fecundity the year of maturity), egg (volume of eggs (mm^{3})).

Note

This dataset replaces the former version in ade4.

References

Rochet, M. J., Cornillon, P-A., Sabatier, R. and Pontier, D. (2000) Comparative analysis of phylogenic and fishing effects in life history patterns of teleos fishes. Oikos, 91, 255–270.

Examples

if(require(ape) && require(phylobase)){

data(mjrochet)
tre <- read.tree(text=mjrochet$tre) # make a tree
traits <- log((mjrochet$tab))

## build a phylo4d
mjr <- phylo4d(tre, traits)

## see data
table.phylo4d(mjr,cex.lab=.5,show.node=FALSE,symb="square")

## perform Abouheif's test for each trait
mjr.tests <- abouheif.moran(mjr, nrep=499)
mjr.tests


}

Computes Moran's index for a variable

Description

This simple function computes Moran's index of autocorrelation given a variable and a matrix of proximities among observations.

Usage

moran.idx(x, prox, addInfo = FALSE)

Arguments

Value

The numeric value of Moran's index.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

References

Moran, P.A.P. (1948) The interpretation of statistical maps. Journal of the Royal Statistical Society, B 10, 243–251.

Moran, P.A.P. (1950) Notes on continuous stochastic phenomena. Biometrika, 37, 17–23.

de Jong, P. and Sprenger, C. and van Veen, F. (1984) On extreme values of Moran's I and Geary's c. Geographical Analysis, 16, 17–24.

See Also

proxTips which computes phylogenetic proximities between tips of a phylogeny.

Examples

## use maples dataset
if(require(ape) && require(phylobase)){
data(maples)
tre <- ape::read.tree(text=maples$tre)
dom <- maples$tab$Dom
bif <- maples$tab$Bif


## get a proximity matrix between tips 
W <- proxTips(tre, met="Abouheif")

## compute Moran's I for two traits (dom and bif)
moran.idx(dom, W)
moran.idx(bif, W)
moran.idx(rnorm(nTips(tre)), W)

## build a simple permutation test for 'bif'
sim <- replicate(499, moran.idx(sample(bif), W)) # permutations
sim <- c(moran.idx(bif, W), sim)

pval <- mean(sim>=sim[1]) # right-tail p-value
pval

hist(sim, col="grey", main="Moran's I Monte Carlo test for 'bif'") # plot
mtext("Histogram of permutations and observation (in red)")
abline(v=sim[1], col="red", lwd=3)
}

Computes Moran's eigenvectors from a tree or a phylogenetic proximity matrix

Description

The function orthobasis.phylo (also nicknamed me.phylo) computes Moran's eigenvectors (ME) associated to a tree. If the tree has 'n' tips, (n-1) vectors will be produced. These vectors form an orthonormal basis: they are centred to mean zero, have unit variance, and are uncorrelated. Each vector models a different pattern of phylogenetic autocorrelation. The first vectors are those with maximum positive autocorrelation, while the last vectors are those with maximum negative autocorrelation. ME can be used, for instance, as regressors to remove phylogenetic autocorrelation from data (see references).

Usage

orthobasis.phylo(
  x = NULL,
  prox = NULL,
  method = c("patristic", "nNodes", "oriAbouheif", "Abouheif", "sumDD"),
  f = function(x) {
     1/x
 }
)

Arguments

Details

ME can be obtained from a tree, specifying the phylogenetic proximity to be used. Alternatively, they can be obtained directly from a matrix of phylogenetic proximities as constructed by proxTips.

Value

An object of class orthobasis. This is a data.frame with Moran's eigenvectors in column, with special attributes:
- attr(...,"values"): Moran's index for each vector - attr(...,"weights"): weights of tips; current implementation uses only uniform weights

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

References

Peres-Neto, P. (2006) A unified strategy for estimating and controlling spatial, temporal and phylogenetic autocorrelation in ecological models Oecologica Brasiliensis 10: 105-119.

Dray, S.; Legendre, P. and Peres-Neto, P. (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbours matrices (PCNM) Ecological Modelling 196: 483-493.

Griffith, D. and Peres-Neto, P. (2006) Spatial modeling in ecology: the flexibility of eigenfunction spatial analyses Ecology 87: 2603-2613.

See Also

- proxTips which computes phylogenetic proximities between tips.

- treePart which can compute an orthobasis based on the topology of a phylogeny.

Examples

if(require(ape) && require(phylobase)){

## SIMPLE EXAMPLE ##
## make a tree
x <- rtree(50)

## compute Moran's eigenvectors
ME <- me.phylo(x, met="Abouheif")
ME

## plot the 10 first vectors
obj <- phylo4d(x, as.data.frame(ME[,1:10]))
table.phylo4d(obj, cex.sym=.7, cex.lab=.7)



## REMOVING PHYLOGENETIC AUTOCORRELATION IN A MODEL ##
## use example in ungulates dataset
data(ungulates)
tre <- read.tree(text=ungulates$tre)
plot(tre)

## look at two traits
afbw <- log(ungulates$tab[,1])
neonatw <- log((ungulates$tab[,2]+ungulates$tab[,3])/2)
names(afbw) <- tre$tip.label
names(neonatw) <- tre$tip.label
plot(afbw, neonatw) # relationship between traits
lm1 <- lm(neonatw~afbw)
abline(lm1)

lm1
resid1 <- residuals(lm1)
orthogram(resid1, tre) # residuals are autocorrelated

## compute Moran's eigenvectors (ME)
myME <- me.phylo(tre, method="Abou")
lm2 <- lm(neonatw ~ myME[,1] + afbw) # use for ME as covariable
resid2 <- residuals(lm2)
orthogram(resid2, tre) # there is no longer phylogenetic autocorrelation

## see the difference
table.phylo4d(phylo4d(tre, cbind.data.frame(resid1, resid2)))
}

Orthonormal decomposition of variance

Description

This function performs the orthonormal decomposition of variance of a quantitative variable on an orthonormal basis. It also returns the results of five non parametric tests associated to the variance decomposition. It thus provides tools (graphical displays and test) for analysing phylogenetic, pattern in one quantitative trait. This implementation replace the (deprecated) version from the ade4 package.

Usage

orthogram(
  x,
  tre = NULL,
  orthobas = NULL,
  prox = NULL,
  nrepet = 999,
  posinega = 0,
  tol = 1e-07,
  cdot = 1.5,
  cfont.main = 1.5,
  lwd = 2,
  nclass,
  high.scores = 0,
  alter = c("greater", "less", "two-sided")
)

Arguments

Details

Several orthonormal bases can be used. By default, basis is constructed from a partition of tips according to tree topology (as returned by treePart); for this, the argument tre must be provided. Alternatively, one can provide an orthonormal basis as returned by orthobasis.phylo/me.phylo (argument orthobas), or provide a proximity matrix from which an orthobasis based on Moran's eigenvectors will be constructed (argument prox).

The function computes the variance decomposition of a quantitative vector x on an orthonormal basis B. The variable is normalized given the uniform weight to eliminate problem of scales. It plots the squared correlations R^{2} between x and vectors of B (variance decomposition) and the cumulated squared correlations SR^{2} (cumulative decomposition). The function also provides five non parametric tests to test the existence of autocorrelation. The tests derive from the five following statistics :

- R2Max=\max(R^{2}). It takes high value when a high part of the variability is explained by one score.
- SkR2k=\sum_{i=1}^{n-1}(iR^{2}_i). It compares the part of variance explained by internal nodes to the one explained by end nodes.
- Dmax=\max_{m=1,...,n-1}(\sum_{j=1}^{m}R^{2}_j - \frac{m}{n-1}). It examines the accumulation of variance for a sequence of scores.
- SCE=\sum_{m=1}^{n-1} (\sum_{j=1}^{m}R^{2}_j - \frac{m}{n-1})^{2}. It examines also the accumulation of variance for a sequence of scores.
- ratio: depends of the parameter posinega. If posinega > 0, the statistic ratio exists and equals \sum_{i=1}^{posinega}R^{2}_i. It compares the part of variance explained by internal nodes to the one explained by end nodes when we can define how many vectors correspond to internal nodes.

Value

If (high.scores = 0), returns an object of class 'krandtest' (randomization tests) corresponding to the five non parametric tests.

If (high.scores > 0), returns a list containg :

Note

This function replaces the former version from the ade4 package, which is deprecated. Note that if ade4 is not loaded BEFORE adephylo, then the version from ade4 will erase that of adephylo, which will still be available from adephylo::orthogram. In practice, though, this should never happen, since ade4 is loaded as a dependence by adephylo.

Author(s)

Original code: Sebastien Ollier and Daniel Chessel.

Current maintainer: Stephane Dray <stephane.dray@univ-lyon1.fr>

References

Ollier, S., Chessel, D. and Couteron, P. (2005) Orthonormal Transform to Decompose the Variance of a Life-History Trait across a Phylogenetic Tree. Biometrics, 62, 471–477.

See Also

orthobasis.phylo

Examples

if(require(ape) && require(phylobase)){

## a phylogenetic example
data(ungulates)
tre <- read.tree(text=ungulates$tre)
plot(tre)

## look at two traits
afbw <- log(ungulates$tab[,1])
neonatw <- log((ungulates$tab[,2]+ungulates$tab[,3])/2)
names(afbw) <- tre$tip.label
names(neonatw) <- tre$tip.label
plot(afbw, neonatw) # relationship between traits
lm1 <- lm(neonatw~afbw)
resid <- residuals(lm1)
abline(lm1)

## plot the two traits and the residuals of lm1
x <- phylo4d(tre, cbind.data.frame(afbw, neonatw, residuals=resid))
table.phylo4d(x) # residuals are surely not independant

## default orthogram for residuals of lm1
orthogram(resid, tre)

## using another orthonormal basis (derived from Abouheif's proximity)
myOrthoBasis <- orthobasis.phylo(tre, method="oriAbouheif") # Abouheif's proximities
orthogram(resid, ortho=myOrthoBasis) # significant phylog. signal

## Abouheif's test
W <- proxTips(tre, method="oriAbouheif") # proximity matrix
abouheif.moran(resid, W)
}

Phylogenetic and quantitative traits of amazonian palm trees

Description

This data set describes the phylogeny of 66 amazonian palm trees. It also gives 7 traits corresponding to these 66 species.

Format

palm is a list containing the 2 following objects:

tre

is a character string giving the phylogenetic tree in Newick format.

traits

is a data frame with 66 species (rows) and 7 traits (columns).

Details

Variables of palm$traits are the following ones:
- rord: specific richness with five ordered levels
- h: height in meter (squared transform)
- dqual: diameter at breast height in centimeter with five levels sout : subterranean, d1(0, 5 cm), d2(5, 15 cm), d3(15, 30 cm) and d4(30, 100 cm)
- vfruit: fruit volume in mm^{3} (logged transform)
- vgrain: seed volume in mm^{3} (logged transform)
- aire: spatial distribution area (km^{2})
- alti: maximum altitude in meter (logged transform)

Note

This dataset replaces the former version in ade4.

Source

This data set was obtained by Clementine Gimaret-Carpentier.

Examples

if(require(ape) && require(phylobase)){

## load data, make a tree and a phylo4d object
data(palm)
tre <- read.tree(text=palm$tre)
rord <- as.integer(palm$traits$rord) # just use this for plotting purpose
traits <- data.frame(rord, palm$traits[,-1])
x <- phylo4d(tre, traits)

## plot data
oldpar <- par(mar=rep(.1,4))
table.phylo4d(x, cex.lab=.6)

## test phylogenetic autocorrelation
if(require(ade4)){
prox <- proxTips(x, method="sumDD")
phylAutoTests <- gearymoran(prox, traits[,-3], nrep=499)
plot(phylAutoTests)
}
par(oldpar)
}

Phylogenetic principal component analysis

Description

These functions are designed to perform a phylogenetic principal component analysis (pPCA, Jombart et al. 2010) and to display the results.

Usage

ppca(
  x,
  prox = NULL,
  method = c("patristic", "nNodes", "oriAbouheif", "Abouheif", "sumDD"),
  f = function(x) {
     1/x
 },
  center = TRUE,
  scale = TRUE,
  scannf = TRUE,
  nfposi = 1,
  nfnega = 0
)

## S3 method for class 'ppca'
scatter(x, axes = 1:ncol(x$li), useLag = FALSE, ...)

## S3 method for class 'ppca'
print(x, ...)

## S3 method for class 'ppca'
summary(object, ..., printres = TRUE)

## S3 method for class 'ppca'
screeplot(x, ..., main = NULL)

## S3 method for class 'ppca'
plot(x, axes = 1:ncol(x$li), useLag = FALSE, ...)

Arguments

Details

ppca performs the phylogenetic component analysis. Other functions are:

- print.ppca: prints the ppca content

- summary.ppca: provides useful information about a ppca object, including the decomposition of eigenvalues of all axes

- scatter.ppca: plot principal components using table.phylo4d

- screeplot.ppca: graphical display of the decomposition of pPCA eigenvalues

- plot.ppca: several graphics describing a ppca object

The phylogenetic Principal Component Analysis (pPCA, Jombart et al., 2010) is derived from the spatial Principal Component Analysis (spca, Jombart et al. 2008), implemented in the adegenet package (see spca).

pPCA is designed to investigate phylogenetic patterns a set of quantitative traits. The analysis returns principal components maximizing the product of variance of the scores and their phylogenetic autocorrelation (Moran's I), therefore reflecting life histories that are phylogenetically structured. Large positive and large negative eigenvalues correspond to global and local structures.

Value

The class ppca are given to lists with the following components:

Other functions have different outputs:

- scatter.ppca returns the matched call.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

References

Jombart, T.; Pavoine, S.; Dufour, A. & Pontier, D. (2010, in press) Exploring phylogeny as a source of ecological variation: a methodological approach. doi:10.1016/j.jtbi.2010.03.038

Jombart, T., Devillard, S., Dufour, A.-B. and Pontier, D. (2008) Revealing cryptic phylogenetic patterns in genetic variability by a new multivariate method. Heredity, 101, 92–103.

See Also

The implementation of spca in the adegenet package (adegenet)

Examples

data(lizards)

if(require(ape) && require(phylobase)){

#### ORIGINAL EXAMPLE FROM JOMBART ET AL 2010 ####


## BUILD A TREE AND A PHYLO4D OBJECT
liz.tre <- read.tree(tex=lizards$hprA)
liz.4d <- phylo4d(liz.tre, lizards$traits)
oldpar <- par(mar=rep(.1,4))
table.phylo4d(liz.4d,var.lab=c(names(lizards$traits),
   "ACP 1\n(\"size effect\")"),show.node=FALSE, cex.lab=1.2)


## REMOVE DUPLICATED POPULATIONS
liz.4d <- prune(liz.4d, c(7,14))
table.phylo4d(liz.4d)


## CORRECT LABELS
lab <- c("Pa", "Ph", "Ll", "Lmca", "Lmcy", "Phha", "Pha",
   "Pb", "Pm", "Ae", "Tt", "Ts", "Lviv", "La", "Ls", "Lvir")
tipLabels(liz.4d) <- lab


## REMOVE SIZE EFFECT
dat <- tdata(liz.4d, type="tip")
dat <- log(dat)
newdat <- data.frame(lapply(dat, function(v) residuals(lm(v~dat$mean.L))))
rownames(newdat) <- rownames(dat)
tdata(liz.4d, type="tip") <- newdat[,-1] # replace data in the phylo4d object


## pPCA
liz.ppca <- ppca(liz.4d,scale=FALSE,scannf=FALSE,nfposi=1,nfnega=1, method="Abouheif")
liz.ppca
tempcol <- rep("grey",7)
tempcol[c(1,7)] <- "black"
barplot(liz.ppca$eig,main='pPCA eigenvalues',cex.main=1.8,col=tempcol)

par(mar=rep(.1,4))
plot(liz.ppca,ratio.tree=.7)


## CONTRIBUTIONS TO PC (LOADINGS) (viewed as dotcharts)
dotchart(liz.ppca$c1[,1],lab=rownames(liz.ppca$c1),main="Global principal
component 1")
abline(v=0,lty=2)

dotchart(liz.ppca$c1[,2],lab=rownames(liz.ppca$c1),main="Local principal
component 1")
abline(v=0,lty=2)


## REPRODUCE FIGURES FROM THE PAPER
obj.ppca <- liz.4d
tdata(obj.ppca, type="tip") <- liz.ppca$li
myLab <- paste(" ",rownames(liz.ppca$li), sep="")

## FIGURE 1
par(mar=c(.1,2.4,2.1,1))
table.phylo4d(obj.ppca, ratio=.7, var.lab=c("1st global PC", "1st local
   PC"), tip.label=myLab,box=FALSE,cex.lab=1.4, cex.sym=1.2, show.node.label=TRUE)
add.scatter.eig(liz.ppca$eig,1,1,1,csub=1.2, posi="topleft", ratio=.23)


## FIGURE 2
s.arrow(liz.ppca$c1,xlim=c(-1,1),clab=1.3,cgrid=1.3)



#### ANOTHER EXAMPLE - INCLUDING NA REPLACEMENT ####
## LOAD THE DATA
data(maples)
tre <- read.tree(text=maples$tre)
x <- phylo4d(tre, maples$tab)
omar <- par("mar")
par(mar=rep(.1,4))
table.phylo4d(x, cex.lab=.5, cex.sym=.6, ratio=.1) # note NAs in last trait ('x')

## FUNCTION TO REPLACE NAS
f1 <- function(vec){
if(any(is.na(vec))){
m <- mean(vec, na.rm=TRUE)
vec[is.na(vec)] <- m
}
return(vec)
}


## PERFORM THE PPCA
dat <- apply(maples$tab,2,f1) # replace NAs
x.noNA <- phylo4d(tre, as.data.frame(dat))
map.ppca <- ppca(x.noNA, scannf=FALSE, method="Abouheif")
map.ppca


## SOME GRAPHICS
screeplot(map.ppca)
scatter(map.ppca, useLag=TRUE)
plot(map.ppca, useLag=TRUE)


## MOST STRUCTURED TRAITS
a <- map.ppca$c1[,1] # loadings on PC 1
names(a) <- row.names(map.ppca$c1)
highContrib <- a[a< quantile(a,0.1) | a>quantile(a,0.9)]
datSel <- cbind.data.frame(dat[, names(highContrib)], map.ppca$li)
temp <- phylo4d(tre, datSel)
table.phylo4d(temp) # plot of most structured traits


## PHYLOGENETIC AUTOCORRELATION TESTS FOR THESE TRAITS
prox <- proxTips(tre, method="Abouheif")
abouheif.moran(dat[, names(highContrib)], prox)
par(oldpar)

}

Phylogeny and quantitative traits of birds

Description

This data set describes the phylogeny of 19 birds as reported by Bried et al. (2002). It also gives 6 traits corresponding to these 19 species.

Format

procella is a list containing the 2 following objects:

tre

is a character string giving the phylogenetic tree in Newick format.

traits

is a data frame with 19 species and 6 traits

Details

Variables of procella$traits are the following ones:
- site.fid: a numeric vector that describes the percentage of site fidelity
- mate.fid: a numeric vector that describes the percentage of mate fidelity
- mass: an integer vector that describes the adult body weight (g)
- ALE: a numeric vector that describes the adult life expectancy (years)
- BF: a numeric vector that describes the breeding frequencies
- col.size: an integer vector that describes the colony size (no nests monitored)

Note

This dataset replaces the former version in ade4.

References

Bried, J., Pontier, D. and Jouventin, P. (2002) Mate fidelity in monogamus birds: a re-examination of the Procellariiformes. Animal Behaviour, 65, 235–246.

See a data description at http://pbil.univ-lyon1.fr/R/pdf/pps037.pdf (in French).

Examples

if(require(ape) && require(phylobase)){

## load data, make tree and phylo4d object
data(procella)
tre <- read.tree(text=procella$tre)
x <- phylo4d(tre, procella$traits)
oldpar <- par(mar=rep(.1,4))
table.phylo4d(x,cex.lab=.7)
par(oldpar)
}

Compute some phylogenetic proximities between tips

Description

The function proxTips computes a given proximity between a set of tips of a phylogeny. A vector of tips is supplied: proximities between all possible pairs of these tips are computed. The proximities are computed from the shortest path between the tips.

Usage

proxTips(
  x,
  tips = "all",
  method = c("patristic", "nNodes", "oriAbouheif", "Abouheif", "sumDD"),
  f = function(x) {
     1/x
 },
  normalize = c("row", "col", "none"),
  symmetric = TRUE,
  useC = TRUE
)

Arguments

Details

Proximities are computed as the inverse (to the power a) of a phylogenetic distance (computed by distTips. Denoting D=[d_{ij}] a matrix of phylogenetic distances, the proximity matrix M=[m_{ij}] is computed as:

m_{ij} = \frac{1}{d_{ij}^a} \forall i \neq j

and

m_{ii} = 0

Several distances can be used, defaulting to the sum of branch lengths (see argument method). Proximities are not true similarity measures, since the proximity of a tip with itself is always set to zero.

The obtained matrix of phylogenetic proximities (M) defines a bilinear symmetric form when M is symmetric (default):

f(x,y) = x^{T}My

In general, M is not a metric because it is not positive-definite. Such a matrice can be used to measure phylogenetic autocorrelation (using Moran's index):

I(x) = \frac{x^TMx}{var(x)}

or to compute lag vectors (Mx) used in autoregressive models, like:

x = Mx + ... + e

where '...' is the non-autoregressive part of the model, and 'e' are residuals.

Abouheif proximity refers to the phylogenetic proximity underlying the test of Abouheif (see references). Let P be the set of all the nodes in the path going from node1 to node2. Let DDP be the number of direct descendants from each node in P. Then, the so-called 'Abouheif' distance is the inverse of the product of all terms in DDP. oriAbouheif returns a matrix with non-null diagonal elements, as formulated in Pavoine et al. (2008). This matrix is bistochastic (all marginal sums equal 1), but this bilinear symmetric form does not give rise to a Moran's index, since it requires a null diagonal. Abouheif contains Abouheif's proximities but has a null diagonal, giving rise to a Moran's index.

sumDD refers to a phylogenetic proximity quite similar to that of Abouheif. We consider the same sets P and DDP. But instead of taking the inverse of the product of all terms in DDP, this proximity computes the inverse of the sum of all terms in DDP. This matrix was denoted 'M' in Pavoine et al. (2008), who reported that it is related to May's index (May, 1990).

Value

A matrix of phylogenetic proximities.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

References

== About Moran's index with various proximities ==
Pavoine, S.; Ollier, S.; Pontier, D.; Chessel, D. (2008) Testing for phylogenetic signal in life history variable: Abouheif's test revisited. Theoretical Population Biology: 73, 79-91.

== About regression on phylogenetic lag vector ==
Cheverud, J. M.; Dow, M. M.; Leutenegger, W. (1985) The quantitative assessment of phylogentic constaints in comparative analyses: sexual dimorphism in body weights among primates. Evolution 39, 1335-1351.

Cheverud, J. M.; Dow, M. M. (1985) An autocorrelation analysis of genetic variation due to lineal fission in social groups of Rhesus macaques. American Journal of Phyisical Anthropology 67, 113-121.

== Abouheif's original paper ==
Abouheif, E. (1999) A method for testing the assumption of phylogenetic independence in comparative data. Evolutionary Ecology Research, 1, 895-909.

== May's index ==
May, R.M. (1990) Taxonomy as destiny. Nature 347, 129-130.

See Also

distTips which computes several phylogenetic distances between tips.

Examples

if(require(ape) & require(phylobase)){
## make a tree
x <- as(rtree(10),"phylo4")
plot(x, show.node=TRUE)
axisPhylo()
## compute different distances
proxTips(x, 1:5)
proxTips(x, 1:5, "nNodes")
proxTips(x, 1:5, "Abouheif")
proxTips(x, , "sumDD")

## see what one proximity looks like
M <- proxTips(x)
obj <- phylo4d(x,as.data.frame(M))
table.phylo4d(obj,symbol="sq")
}

Find the shortest path between tips of a tree

Description

The function sp.tips finds the shortest path between tips of a tree, identified as tip1 and tip2. This function applies to trees with the class phylo, phylo4-class or phylo4d-class. Several tips can be provided at a time.

Usage

sp.tips(x, tip1, tip2, useTipNames = FALSE, quiet = FALSE, include.mrca = TRUE)

Arguments

Details

The function checks if there are cases where tip1 and tip2 are the same. These cases are deleted when detected, issuing a warning (unless quiet is set to TRUE).

Value

A list whose components are vectors of named nodes forming the shortest path between a couple of tips.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

See Also

shortestPath which does the same thing as sp.tips, for any node (internal or tip), but much more slowly.

Examples

if(require(ape) & require(phylobase)){
## make a tree
x <- as(rtree(20),"phylo4")
plot(x,show.node=TRUE)
## get shortest path between tip 1 and all other tips.
sp.tips(x, "t1", "t2")
sp.tips(x, 1, 2:20, TRUE)

}

Graphical display of phylogeny and traits

Description

This function represents traits onto the tips of a phylogeny. Plotted objects must be valid phylo4d-class objects (implemented by the phylobase package). Current version allows plotting of a tree and one or more quantitative traits (possibly containing missing data, represented by an 'x').

Usage

table.phylo4d(
  x,
  treetype = c("phylogram", "cladogram"),
  symbol = c("circles", "squares", "colors"),
  repVar = 1:ncol(tdata(x, type = "tip")),
  center = TRUE,
  scale = TRUE,
  legend = TRUE,
  grid = TRUE,
  box = TRUE,
  show.tip.label = TRUE,
  show.node.label = TRUE,
  show.var.label = TRUE,
  ratio.tree = 1/3,
  font = 3,
  tip.label = tipLabels(x),
  var.label = colnames(tdata(x, type = "tip")),
  cex.symbol = 1,
  cex.label = 1,
  cex.legend = 1,
  pch = 20,
  col = heat.colors(100),
  coord.legend = NULL,
  ...
)

Arguments

Details

The plot of phylogenies is performed by a call to plot.phylo from the ape package. Hence, many of the arguments of plot.phylo can be passed to table.phylo4d, through the ... argument, but their names must be complete.

For large trees, consider using bullseye.

The function table.phylo4d is based on former plot method for phylo4d-class objects from the phylobase package. It replaces the deprecated ade4 functions symbols.phylog and table.phylog.

Value

No return value, function produces only a plot.

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

See Also

The phylo4d-class class for storing phylogeny+data.

For large trees, consider using bullseye.

plot.phylo from the ape package.

An alternative (deprecated) representation is available from dotchart.phylog.

Examples

if(require(ape) & require(phylobase) & require(ade4)){

## simulated data
tr <- rtree(20)
dat <- data.frame(a = rnorm(20), b = scale(1:20), c=runif(20,-2,2) )
dat[3:6, 2] <- NA # introduce some NAs
obj <- phylo4d(tr, dat) # build a phylo4d object
table.phylo4d(obj) # default scatterplot
table.phylo4d(obj,cex.leg=.6, use.edge.length=FALSE) # customized
table.phylo4d(obj,treetype="clad", show.node=FALSE, cex.leg=.6,
use.edge.length=FALSE, edge.color="blue", edge.width=3) # more customized


## teleost fishes data
data(mjrochet)
temp <- read.tree(text=mjrochet$tre) # make a tree
mjr <- phylo4d(x=temp,tip.data=mjrochet$tab) # male a phylo4d object
table.phylo4d(mjr,cex.lab=.5,show.node=FALSE,symb="square")


## lizards data
data(lizards)
liz.tr <- read.tree(tex=lizards$hprA) # make a tree
liz <- phylo4d(liz.tr, lizards$traits) # make a phylo4d object
table.phylo4d(liz)


## plotting principal components
liz.pca1 <- dudi.pca(lizards$traits, scannf=FALSE, nf=2) # PCA of traits
myPC <- phylo4d(liz.tr, liz.pca1$li) # store PC in a phylo4d object
varlab <- paste("Principal \ncomponent", 1:2) # make labels for PCs
table.phylo4d(myPC, ratio=.8, var.lab=varlab) # plot the PCs
add.scatter.eig(liz.pca1$eig,2,1,2,posi="topleft", inset=c(0,.15))
title("Phylogeny and the principal components")

}

Phylogeny and quantitative traits of flowers

Description

This data set describes the phylogeny of 11 flowers as reported by Morales (2000). It also gives morphologic and demographic traits corresponding to these 11 species.

Format

tithonia is a list containing the 2 following objects :

tre

is a character string giving the phylogenetic tree in Newick format.

tab

is a data frame with 11 species and 14 traits (6 morphologic traits and 8 demographic).

Details

Variables of tithonia$tab are the following ones :
morho1: is a numeric vector that describes the seed size (mm)
morho2: is a numeric vector that describes the flower size (mm)
morho3: is a numeric vector that describes the female leaf size (cm)
morho4: is a numeric vector that describes the head size (mm)
morho5: is a integer vector that describes the number of flowers per head
morho6: is a integer vector that describes the number of seeds per head
demo7: is a numeric vector that describes the seedling height (cm)
demo8: is a numeric vector that describes the growth rate (cm/day)
demo9: is a numeric vector that describes the germination time
demo10: is a numeric vector that describes the establishment (per cent)
demo11: is a numeric vector that describes the viability (per cent)
demo12: is a numeric vector that describes the germination (per cent)
demo13: is a integer vector that describes the resource allocation
demo14: is a numeric vector that describes the adult height (m)

Note

This dataset replaces the former version in ade4.

Source

Data were obtained from Morales, E. (2000) Estimating phylogenetic inertia in Tithonia (Asteraceae) : a comparative approach. Evolution, 54, 2, 475–484.

Examples

if(require(ape) && require(phylobase)){

data(tithonia)
tre <- read.tree(text=tithonia$tre)
traits <- log(tithonia$tab + 1)
rownames(traits) <- gsub("_", ".", rownames(traits))

## build a phylo4d object
x <- phylo4d(tre, traits)
oldpar <- par(mar=rep(.1,4))
table.phylo4d(x)
par(oldpar)
}

Define partitions of tips according from a tree

Description

The function treePart defines partitions of tips reflecting the topology of a tree. There are two possible outputs (handled by the argument result):
- basis mode: each node but the root is translated into a dummy vector having one value for each tip: this value is '1' if the tip descends from this node, and '0' otherwise.
- orthobasis: in this mode, an orthonormal basis is derived from the basis previously mentionned. This orthobasis was proposed in the orthogram (Ollier et al. 2006).

Usage

treePart(x, result = c("dummy", "orthobasis"))

Arguments

Details

Orthobasis produced by this function are identical to those stored in the Bscores component of deprecated phylog objects, from the ade4 package.

Value

A matrix of numeric vectors (in columns) having one value for each tip (rows).

Author(s)

Thibaut Jombart tjombart@imperial.ac.uk

References

Ollier, S., Chessel, D. and Couteron, P. (2005) Orthonormal Transform to Decompose the Variance of a Life-History Trait across a Phylogenetic Tree. Biometrics, 62, 471–477.

See Also

- listDD which is called by treePart.
- orthogram, which uses by default the orthobasis produced by treePart.

Examples

if(require(ape) & require(phylobase)){
## make a tree
x <- as(rtree(10),"phylo4")
partition <- treePart(x)
partition

## plot the dummy vectors with the tree
temp <- phylo4d(x, partition)
table.phylo4d(temp, cent=FALSE, scale=FALSE)
}

Phylogeny and quantitative traits of ungulates.

Description

This data set describes the phylogeny of 18 ungulates as reported by Pelabon et al. (1995). It also gives 4 traits corresponding to these 18 species.

Format

fission is a list containing the 2 following objects :

tre

is a character string giving the phylogenetic tree in Newick format.

tab

is a data frame with 18 species and 4 traits

Details

Variables of ungulates$tab are the following ones :

- afbw: is a numeric vector that describes the adult female body weight (g)
- mnw: is a numeric vector that describes the male neonatal weight (g)
- fnw: is a numeric vector that describes the female neonatal weight (g)
- ls: is a numeric vector that describes the litter size

Note

This dataset replaces the former version in ade4.

Source

Data were obtained from Pelabon, C., Gaillard, J.M., Loison, A. and Portier, A. (1995) Is sex-biased maternal care limited by total maternal expenditure in polygynous ungulates? Behavioral Ecology and Sociobiology, 37, 311–319.

Examples

if(require(ape) && require(phylobase)){
## load data
data(ungulates)
tre <- read.tree(text=ungulates$tre)
plot(tre)

## look at two traits
afbw <- log(ungulates$tab[,1])
neonatw <- log((ungulates$tab[,2]+ungulates$tab[,3])/2)
names(afbw) <- tre$tip.label
names(neonatw) <- tre$tip.label
plot(afbw, neonatw) # relationship between traits
lm1 <- lm(neonatw~afbw)
abline(lm1)
x <- phylo4d(tre, cbind.data.frame(afbw, neonatw)) # traits on the phylogeny

## test phylogenetic inertia in residuals
orthogram(residuals(lm1), x) 
}