In guenardg/MPSEM: Modelling Phylogenetic Signals using Eigenvector Maps

knitr::opts_chunk$set(echo = TRUE)
hook_output <- knitr::knit_hooks$get("output")
knitr::knit_hooks$set(output = function(x, options) {
  if (!is.null(n <- options$out.lines)) {
    x <- xfun::split_lines(x)
    if (length(x) > n) {
      # truncate the output
      x <- c(head(x, n), "....\n")
    }
    x <- paste(x, collapse = "\n")
  }
  hook_output(x, options)
})
##
### Load packages here:
##
### Figure counter:
(
  function() {
    log <- list(
      labels = character(),
      captions = character()
    )
    list(
      register = function(label, caption) {
        log$labels <<- c(log$labels, label)
        log$captions <<- c(log$captions, caption)
        invisible(NULL)
      },
      getNumber = function(label) {
        which(log$labels == label)
      },
      getCaption = function(label) {
        a <- which(log$labels == label)
        cap <- log$captions[a]
        cat(sprintf("Fig. %d. %s\n\n---\n",a,cap))
        invisible(NULL)
      }
    )
  }
)() -> figCounter

Package version: r packageVersion("MPSEM")

Introduction

Phylogenetic Eigenvector Maps (PEM) is a method to perform phylogenetic modelling. Phylogenetic modelling consists in modelling trait evolution and predicting trait values using phylogeny as an explanatory factor [@Guenard2013]. Phylogenetic modelling allows one to predict trait values when it is difficult or impractical to obtain them, for instance when species are rare, extinct, or when information is needed for several species and trait values are only available for a relatively small number of them [@Guenard2011;@Guenard2014].

To apply phylogenetic modelling, one needs to have a set of species with known phylogeny and trait values (hereafter referred to as the model species) as well as to know the locations, with respect to the phylogeny of the models species, of the species for which trait values are being predicted (hereafter referred to as the target species). Phylogenetic modelling can be performed jointly with trait correlation modelling: it is possible to use other traits with known (or estimable) values for the target species to help predict a trait of interest. Phylogenetic trees being acyclic graphs, I will hereby prefer terms belonging to the graph theory over terms phylogeneticists may be more familiar with. Therefore I will use edge over branches and vertex over root, node or tip; safe in cases where I want to be specific about what a vertex represents.

The Phylogenetic eigenvector maps (PEM) expression

Statistical maps are a type of geographic map representing the values or states of a variable across space <https://encyclopedia2.thefreedictionary.com/statistical+map).

In a paper entitled "The interpretation of statistical maps", [@Moran1948] described tests of significance of the spatial relationships among values of qualitative variables on statistical maps.

"Moran's eigenvector maps" (MEM), an expression coined by @Dray2006, describes the variation of spatial eigenvectors whose eigenvalues are proportional to Moran's I spatial autocorrelation statistics [@Moran1950] of the corresponding eigenvectors. Spatial eigenvectors are mathematical constructs that describe the variation of quantities across space (or time) at different spatial scales. They were originally called Principal Coordinates of Neighbour Matrices by @Borcard2002.

Phylogenetic eigenvector maps (PEM) [@Diniz2012; @Guenard2013] are sets of eigenfunctions describing the structure of a phylogenetic graph, which represents either a Darwinian phylogenetic tree or a reticulated tree, i.e., a phylogenetic tree with reticulations. The various eigenvectors describe the variation across a phylogeny at different phylogenetic scales.

Contrary to MEM, the eigenvalues in PEM are not proportional to Moran's I autocorrelation coefficients of the corresponding eigenvectors.

The PEM work flow consists in

1) calculating the influence matrix of the graph,

2) specifying a model of trait evolution along the edges of the phylogenetic tree,

3) calculating the left eigenvectors of the weighted and centred influence matrix and

4) use these eigenvectors as descriptors [@Guenard2013].

An R language implementation of that approach is found in package MPSEM. MPSEM was developed to make the aforementioned process as seamless as possible. It is a work in progress; I welcome anyone to provide relevant suggestions and constructive remarks aimed at making MPSEM a better, more efficient and user-friendly, interface to phylogenetic modelling.

Assuming package MPSEM is installed, the first step to calculate a PEM is to load package MPSEM, which depends on packages ape and MASS:

library(MPSEM)

Preparing the data

For the present tutorial, we will use the data set perissodactyla from R package caper. These data from @Purvis1995 are loaded into your R workspace as follows:

data(perissodactyla,package="caper")

par(mar=c(2,2,2,2))
plot(perissodactyla.tree)
par(mar=c(5,4,4,2))
figCounter$register(
  "theTree",
  "The phylogenetic tree used for this example."
)

figCounter$getCaption("theTree")

The perissodactyla data set contains perissodactyla.tree, a phylogenetic tree encompassing r length(perissodactyla.tree$tip.label) odd-toed ungulate species (Fig. r figCounter$getNumber("theTree")) and perissodactyla.data, a data frame containing information four trait for these species, namely, the ($\log_{10}$) of the adult female weight, gestation weight, and neonatal weight, as well a fourth categorical trait telling whether of not the species has territorial behaviour.

knitr::kable(perissodactyla.data)

There are four species for which territoriality is missing: the Sumatran rhinoceros (Dicerorhinus sumatrensis), the Javan rhinoceros (Rhinoceros sondaicus), the mountain tapir (Tapirus pinchaque), and the South American tapir (Tapirus terrestris). For the benefit of this example, we performed a quick search over the Internet to attempt at completing this data. We came up with the following completion data:

perissodactyla.data[c(2,9,12,13),"Territoriality"] <- c("No","No","Yes","Yes")

These data are not the result of a professional enquiery and thus must not be used outside of the present example. Poeple interested in the behaviour of these species should seek advice from a competent scientist.

Before going any further, it is important to make sure that the species in the tree object are the same and presented in the same order as the ones in the data table. Glancing at the data table, species clearly cannot match since the latter feature information for only r nrow(perissodactyla.data) of the r length(perissodactyla.tree$tip.label) species in the tree. We will therefore match the tip labels of the original tree in the data table using the binary (Latin) species names in a character vector spmatch. When no matching element from the data table is found, the value NA appears at the corresponding position in spmatch. We can therefore use these missing values to reference the species that can be dropped from the tree using function drop.tip() from package ape as follows:

match(
  perissodactyla.tree$tip.label,
  perissodactyla.data[,1L]
) -> spmatch

drop.tip(
  perissodactyla.tree,
  perissodactyla.tree$tip.label[is.na(spmatch)]
) -> perissodactyla.tree

Now that the data match the tree in terms of species content, we then need to make sure that species ordering also matches as follows:

cbind(perissodactyla.tree$tip.label, perissodactyla.data[,1L])

Since they do not, we need to recalculate spmatch with the new, reduced, tree and re-order the data accordingly:

match(
  perissodactyla.tree$tip.label,
  perissodactyla.data[,1L]
) -> spmatch

perissodactyla.data[spmatch,] -> perissodactyla.data

all(perissodactyla.tree$tip.label == perissodactyla.data[,1L])

The last code line is just a last check to guarantee that all species names are matching. As a last step before we are done with data manipulation, I will put the binary names in place of the row names and delete the table's first row:

perissodactyla.data[,1L] -> rownames(perissodactyla.data)
perissodactyla.data[,-1L] -> perissodactyla.data

The data now appear as follows:

knitr::kable(perissodactyla.data)

Calculating PEM

Edge weighting function

As previously announced, I use the vocabulary of the graph theory when describing PEM: a tree is a (directed) graph, a branch is an edge, and the root, nodes, and tips are vertices. PEM allows one to specify a model of trait evolution along the edges of the tree. The trait evolution model is a power function of the edge lengths, with parameters $a$ and $\psi$ describing the shape of the relationship between the edge lengths and trait evolution rate:

$$ w_{a,\psi}(\phi_{j})= \begin{cases} \psi\phi^{\frac{1-a}{2}} & \phi_{j}>0\ 0 & \phi_{j}=0, \end{cases} $$ where $a$ is the steepness parameter describing how abrupt the changes in trait values occur with time following branching, $\psi$ is the evolution rate of the trait, and $\phi_{j}$ is the length of edge $j$ [@Guenard2013].

par(mar=c(4.5,4.5,1,7) + 0.1)
d <- seq(0, 2, length.out=1000)
a <- c(0,0.33,0.67,1,0.25,0.75,0)
psi <- c(1,1,1,1,0.65,0.65,0.4)
cc <- c(1,1,1,1,1,1,1)
ll <- c(1,2,2,2,3,3,3)
trial <- cbind(a, psi)
colnames(trial) <- c("a","psi")
ntrials <- nrow(trial)
nd <- length(d)
matrix(
  NA,
  ntrials,
  nd,
  dimnames=list(paste("a=", trial[,"a"], ", psi=", trial[,"psi"], sep=""),
                paste("d=", round(d,4), sep=""))
) -> w
for(i in 1:ntrials)
  w[i,] <- MPSEM::PEMweights(d, trial[i,"a"], trial[i,"psi"])
plot(NA, xlim=c(0,2), ylim=c(0,1.6), ylab="Weight", xlab="Distance", axes=FALSE)
axis(1, at=seq(0,2,0.5), label=seq(0,2,0.5))
axis(2, las=1)
text(expression(paste(~~~a~~~~~~~psi)),x=2.2,y=1.57,xpd=TRUE,adj=0)
for(i in 1:ntrials) {
  lines(x=d, y=w[i,], col=cc[i], lty=ll[i])
  text(paste(sprintf("%.2f", trial[i,1]), sprintf("%.2f",trial[i,2]), sep="  "),
       x=rep(2.2,1), y=w[i,1000], xpd=TRUE, adj=0)
}
rm(d,a,psi,cc,ll,trial,ntrials,nd,w,i)
figCounter$register(
  "edgeWeighting",
  paste(
    "Output of the edge weighting function for different sets of parameters",
    "$a$ and $\\psi$."
  )
)

figCounter$getCaption("edgeWeighting")

As the steepness parameter increases, the weight assigned to a given edge increases more sharply with respect to the phylogenetic distance (or evolutionary time; Fig. r figCounter$getNumber("edgeWeighting")). In the context of PEM, the edge weight represent the relative rate of evolution of the trait; the greater the edge weight, the greater the trait change along that edge. When $a=0$, trait evolution is neutral and therefore proceeds by random walk along edges. When $a=1$, edge weights no longer increase as a function of edge lengths. That situation corresponds to the scenario in which trait evolution is driven by the strongest possible natural selection: following a speciation event, trait either change abruptly (directional selection) at the vertex or do not change at all (stabilizing selection).

Also, the shape parameters may differ for different parts of the phylogenetic tree or network. For instance, the trait may have evolved neutrally at a steady rate from the base of a tree up to a certain node, and then, may have been subjected to different levels of selection pressure and various evolution rate from some of the nodes towards their tips.

Phylogenetic graph

The first step to build a PEM is to convert the phylogenetic tree. The is done by giving the tree to function as.graph() as follows:

gr1 <- as.graph(perissodactyla.tree)

Here's a snipet showing how the graph container used by MPSEM stores graph information:

str(gr1)

The object consists in a data frame containing the vertex information, together with another data frame containing the edge information and stored as an attribute called edge. The vertex data frame may have $0$ or more columns containing vertex information and its rows reference the vertices (minimally, their name labels, which are stored as the row names of the data frame). In the present case, a logical vector created by as.graph() and called $species stores whether a given vertex represents a species (i.e., it is a tip) or not (i.e., it is a node). The edge attribute is a data frame containing information about the edges of the graph, namely the indices of their origin and destination vertices (the first two columns), and an arbitrary number of supplementary columns storing any other edge properties. In the present case, a numeric vector created by as.graph() and called $distance stores the phylogenetic distances ($\phi_{j}$), which, in this example, correspond to the branch lengths of perissodactyla.tree.

Building the eigenvector map

Beginning on MPSEM version 0.6, PEM are generated using function PEM (before version 0.6, a defunct function called PEM.build() was used for that purpose). That function minimally takes a graph-class object and outputs a PEM-class object. By default, the whole graph is assumed to have a single value of the steepness parameter (i.e., assuming neutral evolution; $a=0$ for all the graph's edges) and homogeneous evolution rate (i.e., $\psi = 1$ for all the graph's edges). Function PEM() is called as follows:

## Calculate the PEM object using the default:
perissodactyla.PEM <- PEM(gr1)

## Show the PEM object:
perissodactyla.PEM

Heterogeneous evolutionary process

Heterogeneous evolutionary processes are defined by two linear sub models on the graph's edges; one for parameter $a$ and one for parameter $\psi$. Each of these models is itself defined using a model matrix and a parameter vector. These model matrices have as many rows as the number of edges, and as many columns as the numbers of parameters. The model matrix for $a$ ($\mathbf{M}_a$, argument mm_a) needs at least a single all-ones column working together with a length one parameter vector ($\mathbf{b}_a$) to represent a constant value to apply to all edges (this is the default for mm_a). The value of $a$ for any edge $i$ is obtained from the linear combination $\eta_i = \mathbf{M}_a \mathbf{b}_a$ as follows:

$$ a_i = \frac{1}{1 + e^{-\eta_i}}, $$

which constrains the value of $a_i$ between $0$ and $1$. The default value for mm_a is a single-column all-ones matrix, whereas the single value for $\mathbf{b}a$ is $-\infty$, which makes $a_i = 0$ for all edges. The linear sub model for $\psi$ is defined in a similar way, with the exception that it needs no all-ones column. The value of $\psi$ for any edge $i$ is obtained from the linear combination $\eta_i = \mathbf{M}\psi \mathbf{b}_\psi$ as follows:

$$ \psi_i = \frac{2}{1 + e^{-\eta_i}}, $$

which constrains the values of $\psi$ between $0$ and $2$. The model matrix for $\psi$ ($\mathbf{M}{\psi}$, argument mm_psi) may be a zero-column matrix working together with a length zero parameter vector ($\mathbf{b}{\psi}$) implying a constant value $\eta=0$ to apply to all edges.

The default behavior of function PEM() for mm_psi to be a zero-column matrix and b_psi to be a zero-length vector thereby applying a constant value of $\psi=1$ for all the edges of the graph.

The elements of the model matrices are binary values (i.e., taking either values $0$ or $1$) which can be defined manually or using functions isUnderVertex() or isUnderEdge(). Function isUnderVertex() generates sets of vectors with the value $1$ for all edges under a given vertex and $0$ elsewhere. Likewise function isUnderEdge() generates sets of vectors with the value $1$ for all edges under a given edge and $0$ elsewhere.

The following figure will help us figure out the indices of the edges involved:

perissodactyla.tree -> tree
sprintf("N%s",1L:tree$Nnode) -> tree$node.label

par(mar=c(2,2,2,2))
plot(tree, show.node.label=TRUE)

edgelabels(
  sprintf("E%d",1L:nrow(tree$edge)),
  edge=1L:nrow(tree$edge),
  bg="white",
  cex=0.75
)

figCounter$register(
  "trainingTree",
  "The labelled training species tree for this example."
)
figCounter$getCaption("trainingTree")

For instance, let us define a model matrix with a contrast for all species of genus Equus, which are located under vertex N8, and a second contrast vector for the complete lineage leading to species of genus Tapirus from the rest of the tree, which are all found under edge E9 as follows:

cbind(
  isUnderVertex(gr1, "N9"),
  isUnderEdge(gr1,"E11")
) -> mm
mm

A PEM is generated on the basis of this model matrix using function PEM() as follows:

## Calculate the PEM:
PEM(
  x = gr1,
  a = c(-6,5,8),            ## steepness sub model parameters
  psi = c(-1,1),            ## evolution rate sub model parameters
  mm_a = cbind(1, mm),      ## model matrix: steepness sub model
  mm_psi = mm               ## model matrix: evolution rate sub model
) -> perissodactyla.PEM

## Show the PEM object:
perissodactyla.PEM

## Extract the graph from within the PEM-class object:
gr <- perissodactyla.PEM$graph()

## Show the steepness and evolution rate at the edges:
round(edge(gr)[,c("a","psi")],3)

In this example, it is assumed that the steepness and evolution rate are $a \approx 0.269$ and $\psi \approx 0.538$ among genus Equus (E15--E21), $a \approx 0.881$ and $\psi \approx 1.462$ among genus Tapirus (E9--E13), and $a \approx 0.002$ and $\psi = 1$ from the root of the tree up to the vertex where the two latter genera begin as well as among the other genera (E1--E8 and E14). The models may involve more complex designs these two additive terms (eg., nested factors, interaction terms) provided that the parameter values are known or that sufficient data is available to estimate the parameters.

In addition to the phylogenetic graph, to evolution model matrices, and two parameter vectors, function PEM() has an argument d, the name of the edge property where the phylogenetic distances are stored and sp, the name of the vertex property specifying what vertex is a species. The default values for d and sp are those produced by as.graph(), and can therefore be omitted in most cases. The pem-class object that is used to store PEM information is rather complex and we will hereby refrain from browsing through it. Methods as.matrix and as.data.frame enables one to extract the eigenvectors from the PEM-class object. For a set of $n$ species, these methods return a matrix encompassing at most $n - 1$ column vectors that can be used in models to represent phylogenetic structure in traits. Here the phylogenetic patterns of variation described by two eigenvectors of the PEM we calculated above:

tmp <- par(no.readonly = TRUE)
par(mfrow=c(1,2), mar=c(1.1,1.1,2.6,0.1))

## Singular vectors are extracted using the as.matrix method:
perissodactyla.U <- as.matrix(perissodactyla.PEM)

plot(perissodactyla.tree, x.lim=60, cex=1.0)

par(mar=c(1.1,0.1,2.6,1.1))
plot(NA, xlim=c(1,ncol(perissodactyla.U)), ylim=c(1,nrow(perissodactyla.U)),
     ylab="", xlab="", axes=FALSE, cex=1.5)

axis(3, at=1:ncol(perissodactyla.U), tick=FALSE, cex.axis=1.1,
     label=parse(text=sprintf("bold(u)[%d]",1:ncol(perissodactyla.U))))

absrng <- max(abs(perissodactyla.U))

for(i in 1:ncol(perissodactyla.U))
  points(
    x = rep(i,nrow(perissodactyla.U)),
    y = 1:nrow(perissodactyla.U),
    cex = 4*abs(perissodactyla.U[,i])/absrng,
    bg = grey(c(0,1))[1.5 + 0.5*sign(perissodactyla.U[,i])],
    pch=22
  )

par(tmp)

figCounter$register(
  "eigenvectorExample",
  paste(
    "Example of a set of 12 eigenvectors obtained from the exemplary data set.",
    "The size of the markers scales linearly with the absolute value of the",
    "loadings, whereas the background colors are showing their signs (white:",
    "negative loadings, black: positive loadings)."
  )
)
figCounter$getCaption("eigenvectorExample")

For instance, the pattern shown by the first eigenvector essentially contrasts Equids and the other odd-toed ungulate species, whereas the pattern shown by the second eigenvector essentially contrasts tapirs and Rhinocerotids. The other $10$ eigenvectors show various other smaller-scale phylogenetic patterns.

Estimate weighting parameters empirically

Class PEM has a method called evolution.model to estimate the best set of weighting function parameters to use for modelling, which are often unknown to the user. From the initial values provided when creating the PEM-class object, method evolution.model estimates the parameter empirically using restricted maximum likelihood from one or more response variables provided to it through its argument y and, optionally, from a set of auxiliary traits (argument x).

Method evolution.model replaces the defunct function PEM.fitSimple(), enabled one to estimate the value of a single $a$ value, with $\psi=1$ for a tree. The framework developed since MPSEM version 0.6 enables more flexibility in specifying models with heterogeneous $a$ and $\psi$ for various parts of the phylogenetic network.

Method evolution.model requires a response variable (here the neonate weight) that will be used to optimize the parameter vectors $\mathbf{b}a$ and (as required) $\mathbf{b}\psi$ and is called as follows:

perissodactyla.PEM <- PEM(gr1, a = 0)   ## A simpler, single-steepness, model

evolution.model(
  object = perissodactyla.PEM,
  y = perissodactyla.data[,"log.neonatal.wt"]
) -> opt

opt

Method evolution.model returns a list with information about the optimization process. Alternatively, the response trait (argument y) and any other auxiliary trait (argument x; here the female weight and territoriality) may be prepared using function model.data and then used as follows:

perissodactyla.PEM_aux <- PEM(gr1, a = 0)

## Data preparation:
model.data(
  formula = log.neonatal.wt~log.female.wt+Territoriality,
  data = perissodactyla.data
) -> mdat

evolution.model(
  object = perissodactyla.PEM_aux,
  y = mdat$y,
  x = mdat$x
) -> opt_aux

opt_aux

Function model.data produces a list with the response traits (element $y) and auxiliary traits (element $x) specified by a formula, with the data provided using data frame, not unlike what functions like lm or glm perform internally prior to model estimation. It is noteworthy that any call to evolution.model automatically updates the PEM contained in the PEM object provided to the method and makes the generalized least squares estimate of the response trait variance (or residual variance with respect to the auxiliary trait) available from within the PEM object. Response trait variance estimates can later be accessed as follows:

perissodactyla.PEM_aux$S2()

It is noteworthy that estimates of the steepness parameter (stored as element $optim\$par of the PEM objects) and, consequently, the resulting phylogenetic eigenvectors, will be different depending on the use of auxiliary traits. In the example above, for instance, $\eta_a$ was estimated to r round(opt$par$a, 2) in the first call, when no auxiliary trait is involved and to r round(opt_aux$par$a, 2) in the second call, when the female weight is used as an auxiliary trait.

Prediction scores

The location of any target species is some distance from its latest common ancestor (LCA) with respect to the species of the graph, which itself is found either at a vertex, or on an edge at a specific distance from its origin. Class PEM has a predict method that returns prediction scores for any graph location, be it at a vertex or at any point along an edge. This method internally calls one of two embedded functions for any target. These embedded functions are called $predictVertex() and $predictEdge() and return the PEM prediction scores for any vertex or edge, respectively. PEM method predict is called as follows:

## Model without an auxiliary trait.
predict(
  object = perissodactyla.PEM,
  newdata = data.frame(
    row.names = sprintf("target_%d",1:6),
    ref = c(1,3,5,2,3,4),
    dist = c(NA,0.1,NA,NA,0.4,1.1),
    lca = c(0,2,2.3,0,2,1.1)
  )
)

## Model with two auxiliary traits.
predict(
  object = perissodactyla.PEM_aux,
  newdata = data.frame(
    row.names = sprintf("target_%d",1:6),
    ref = c(1,3,5,2,3,4),
    dist = c(NA,0.1,NA,NA,0.4,1.1),
    lca = c(0,2,2.3,0,2,1.1)
  )
)

Argument newdata is a three-column data frame with the following contents:

ref : an integer giving the index of the vertex or edge where the LCA is found,

dist : a numeric vector; for indices in ref that are edges, the distance from the edge origin where the LCA is found, for indices that are vertices, a missing values,

lca : a numeric vector giving the distance of the target from the LCA.

The method returns a matrix of PEM scores to be used as predictors with an attribute called "vf" giving the prediction variance increment associated with the distance between the LCA and the target.

Phylogenetic modelling

To model trait values, PEM are used as descriptors in other modelling method. Any suitable method can be used. The eigenvectors from the PEM are obtained from method as.matrix or as.data.frame as follows:

## Extract the eigenvector as a matrix:
as.matrix(perissodactyla.PEM)

## Extract the eigenvector as a data frame:
as.data.frame(perissodactyla.PEM)

Package MPSEM includes a framework for ordinary least squares (OLS) regression based on R's lm function and methods. This framwork is implemented by function pemlm, which called as follows:

## A first pemlm model without an auxiliary trait:
pemlm(
  log.neonatal.wt~1,
  perissodactyla.data,
  perissodactyla.PEM
) -> lm1

## Summary of the first model:
summary(lm1)

## Anova of the first model:
anova(lm1)

## A second pemlm model with two auxiliary traits:
pemlm(
  log.neonatal.wt~log.female.wt+Territoriality,
  perissodactyla.data,
  perissodactyla.PEM_aux
) -> lm2

## Summary of the second model:
summary(lm2)

## Anova of the second model:
anova(lm2)

Models produced by pemlm do not immediately include phylogenetic eigenvectors. The process for adding phylogenetic eigenvectors to the model is mediated using a set of embedded functions, a complete description of which would be beyond the scope of the present document. Embedded function $forward performs stepwise variable addition of the PEM eigenfunctions on the basis of the corrected Akaike Information Criterion (AICc) [@Hurvich1993] as follows:

## Adding eigenfunctions to the first model until the smallest AICc is reached
lm1$forward()
lm1

## Calculating the summary and anova 
summary(lm1)
anova(lm1)

## Adding eigenfunctions to the second model until the smallest AICc is reached
lm2$forward()
lm2

summary(lm2)
anova(lm2)

Making predictions

The perisoadctyla data set has one missing gestation weight value for the mountain tapir (Tapirus pinchaque). Here, we will exemplify how to make predictions by estimating its most likely value. First, we need a graph (here a tree) for all the species with the exception of Tapirus pinchaque, and the coordinates of that species in that graph. This is accomplished by method locate as follows:

sp <- "Tapirus pinchaque"     ## A selected species.
train <- locate(gr1, sp)      ## The locate method.
train

Here, we can se that Tapirus pinchaque is located at distance r train$location$ladist from and LCA located at distance r train$location$dist along an edge with index r train$location$ref. The locate method returns a two-element list whose first member ($x) is the graph where the target species have been removed (ie. the residual graph) and whose second member ($location) a three-column date frame of the graph location (columns ref, dist, and lca described previously) for each of the targets. We then need to prepare the model data for all the traits available as follows:

## Prepare the model data:
model.data(
  formula = log.gestation.length~log.female.wt+log.neonatal.wt+Territoriality,
  data = perissodactyla.data
) -> mdat

For making predictions, we need to build a PEM using residual graph obtain from method locate as follows:

## Initial PEM (single a = 0.5, single psi = 1) built from the residual graph:
pem.train <- PEM(train$x, a = 0)

## Estimate the evolution model:
evolution.model(
  object = pem.train,
  y = mdat$y[names(mdat$y) != sp],
  x = mdat$x[rownames(mdat$x) != sp,]
) -> opt

## Build a linear model:
pemlm(
  formula = log.gestation.length~log.female.wt+log.neonatal.wt+Territoriality,
  data = perissodactyla.data %>% .[rownames(.) != sp,],
  pem = pem.train
) -> lm3

## Summary and anova for the auxiliary trait model:
summary(lm3)
anova(lm3)

## Add the phylogenetic eigenfunctions:
lm3$forward()

## Summary and anova for the final model:
summary(lm3)
anova(lm3)

Finally, we use the predict() method (for pemlm-class objects) to obtain the predicted value as follows:

## Make the prediction:
predict(
  object = lm3,
  newdata = perissodactyla.data[sp,],
  newloc = train$location
) -> prd
prd

## Substitute the missing value for the estimated gestation time:
perissodactyla.data[sp,"log.gestation.length"] <- prd

Cross-validation of PEM predictions

Here is and example of how to perform a leave-one-out cross-validation of a data set using the R code featured in the previous sections. Predictions will be added to table to a table called perissodactyla.pred. The cross-validation is carried out as follows:

## Table storing the results:
data.frame(
  observed = perissodactyla.data$log.neonatal.wt,
  auxiliary = NA,
  predictions = NA,
  lower = NA,
  upper = NA,
  row.names = rownames(perissodactyla.data)
) -> perissodactyla.pred

## Obtaining the updated model data:
model.data(
  formula = log.neonatal.wt~log.female.wt+log.gestation.length+Territoriality,
  data = perissodactyla.data
) -> mdat

## For each species i:
for(i in 1L:nrow(perissodactyla.pred)) {

  ## Calculate the residual graph and location:
  train <- locate(gr1, rownames(perissodactyla.pred)[i])

  ## Calculate a PEM:
  pem.train <- PEM(train$x, a = 0)

  ## Estimate the evolution model:
  evolution.model(
    object = pem.train,
    y = mdat$y[-i],
    x = mdat$x[-i,]
  ) -> opt

  ## Build an empty (auxiliary trait only) pemlm model:
  pemlm(
    formula = log.neonatal.wt~log.female.wt+log.gestation.length+Territoriality,
    data = perissodactyla.data[-i,],
    pem = pem.train
  ) -> lm_cv

  ## Make prediction using the empty model:
  predict(
    lm_cv$auxModel,
    perissodactyla.data[i,]
  ) -> perissodactyla.pred[i,2L]

  ## Add the PEM eigenfunction(s) on the basis of the AICc:
  lm_cv$forward()

  ## Make the prediction using the PEM-based model, including the limits of the
  ## 95% prediction interval:
  predict(
    object = lm_cv,
    newdata = perissodactyla.data[i,],
    newloc = train$location,
    interval = "prediction"
  ) -> perissodactyla.pred[i,3L:5L]
}

## Prediction coefficient using the auxiliary traits alone:
Psquare(perissodactyla.pred$observed, perissodactyla.pred$auxiliary)

## Prediction coefficient using the PEM eigenfunctions:
Psquare(perissodactyla.pred$observed, perissodactyla.pred$predictions)

## Calculate the range of the whole data (predictions and interval limits):
rng <- range(perissodactyla.pred)

## Save the graphical parameters:
p <- par(no.readonly = TRUE)

## Generates an empty plot:
par(mar=c(5,5,2,2))
plot(NA, xlim=rng, ylim=rng, xlab="Observed (log) neonatal",
     ylab="predicted (log) neonatal", las=1, asp=1)

## Show the predictions without the PEM:
points(x=perissodactyla.pred$observed, y=perissodactyla.pred$auxiliary, pch=21,
       bg="blue")

## Show the predictions and their prediction intervals with the PEM:
arrows(x0=perissodactyla.pred$observed, x1=perissodactyla.pred$observed,
       y0=perissodactyla.pred$lower, y1=perissodactyla.pred$upper, code=3,
       angle=90, length=0.05)
points(x=perissodactyla.pred$observed, y=perissodactyla.pred$predictions,
       pch=21, bg="black")

## 1:1 line:
abline(0,1)

## Restore the graphical parameters:
par(p)

figCounter$register(
  "crossPreds",
  paste(
    "Leave-one-out crossvalidated prediction of the neonatal weight for",
    nrow((perissodactyla.data)),
    "odd-toed ungulate species."
  )
)

figCounter$getCaption("crossPreds")

From the present cross-validation, we found that the ($\log_{10}$) neonatal body mass can be predicted with a cross-validated $R^{2}$ of r round(Psquare(perissodactyla.pred$observed, perissodactyla.pred$predictions),2) (Fig. r figCounter$getNumber("crossPreds")).

Other utility functions

Influence matrix

The influence matrix is used internally to calculate PEM. It is a matrix having as many rows as the number of vertices (species + nodes) and as many columns as the number of edges. Any given element of the influence matrix is coding whether a vertex, which is represented a row of the matrix is influenced an edge, which is represented by a column of the matrix. In the context of PEM, a vertex is influenced by an edge when the former has ancestors on the latter or, in other words, when an edge is on the path leading from a tip to the root of the tree. The influence matrix is obtained as follows:

InflMat(gr1) -> res
res

Update and a PEM with new parameters

The calculation of the influence matrix performed by PEM() for a given phylogenetic graph need not be done every time new weighting function parameters are to be tried. For that reason, MPSEM provides an update method for the PEM class that takes a previously calculated PEM object, applies new edge weighting, and recalculates the phylogenetic eigenvectors:

## Update the PEM object, setting a to 0.9:
update(perissodactyla.PEM, a = log(0.9/(1 - 0.9)), psi=NULL)

## Access the parameter values
perissodactyla.PEM$par()

## The parameter values are also available from the edge properties:
edge(perissodactyla.PEM$graph())

The response trait variance estimates will not readily be updated by the update method

## Old trait variance value:
perissodactyla.PEM$S2()

## Recalculating trait variance:
perissodactyla.PEM$S2(y = perissodactyla.data[,"log.neonatal.wt"])

## The new trait variance value is available thereafter:
perissodactyla.PEM$S2()

References

guenardg/MPSEM documentation built on April 14, 2025, 3:53 p.m.