If you are unfamiliar with phylogenetic comparative methods in general, I’d advise you learn more simple methods first. If you are comfortable with phylogenetic methods, but not with phylogenetic path analysis (PPA) in particular, you should have a look at the example below together with the wonderful book chapter by Gonzalez-Voyer and Von Hardenberg.

If you’re just looking for how to use the package, the PeerJ paper and these vignettes are what you are looking for.

This package follows the general approach to phylogenetic methods as
used by the package `phylolm`

(this package is used for model
fitting “under the hood”). This means in particular that you line up
your phylogenetic tree and data by assigning the tip labels as the row
names of your data.frame. (Please note that this is the same for
`ape`

, but different from the approach of
`caper::pgls`

where you create a comparative data
object.)

Your phylogenetic tree needs to be a `phylo`

object, and
many tree files can be read into R using the `ape`

package
(which is installed automatically with `phylopath`

). For
example:

```
library(ape)
<- read.tree('my_tree.tre') # For Newick format trees
my_tree <- read.nexus('my_tree.nex') # For NEXUS format trees my_tree
```

More info on reading trees can be found on the r-phylo wiki.

If you have a column in your data that contain the species names, you have to set those names are rownames, e.g. like this:

`rownames(my_data) <- my_data$species_name`

Often, tree tip labels don’t have spaces between the genus and
species names but underscores (`_`

). If this is the case for
you, but you have names with species in your data, you need to replace
those spaces. For example:

```
$tip.label # Check the tip labels of your tree
my_treerownames(my_data) <- gsub(' ', '_', my_data$species_name_with_spaces)
```

Please be aware that some `data.frame`

extensions like
`data.table`

s (used by the `data.table`

package)
and `tibble`

s (used by the tidyverse packages) do
*not* support rownames, and cannot be used. You can coerce those
back to normal `data.frames`

using
`my_data <- as.data.frame(my_data)`

.

`phylopath`

makes it’s best attempt at helping you out
with your data and tree. That means in particular that:

- an informative error is raised when you have species (rownames!) in your data that are not in the tree,
- that
`NA`

values are filtered out of the data as necessary, with an informative message, and - that the tree will be pruned for you if it has surplus species, with an informative message.

You can set different models of evolution just like in
`phylolm`

, using the `model`

parameter. By default
`phylopath`

uses Pagel’s lambda. This model of evolution only
applies when fitting regressions to continuous variables. The following
models of evolution are available (per
`?phylolm::phylolm`

):

`"BM"`

: Brownian motion model.`"OUfixedRoot"`

: the Ornstein-Uhlenbeck model with an ancestral state to be estimated at the root.`"OUrandomRoot"`

: the Ornstein-Uhlenbeck model with the ancestral state at the root having the stationary distribution.`"lambda"`

: Pagel’s lambda model (*default*).`"kappa"`

: Pagel’s kappa model.`"delta"`

: Pagel’s delta model.`"EB"`

: the early burst model.`"trend"`

: the Brownian motion model with a trend.

If you include binary data, these regressions do not use that model,
but there are two methods for estimating the logistic PGLS model, which
you can set using the `method`

parameter. You can either use
`"logistic_MPLE"`

(the default) or
`"logistic_IG10"`

(also see
`?phylolm::phyloglm`

).

Other settings of `phylolm`

and `phyloglm`

,
such as constraints on the phylogenetic parameter, can be set easily by
passing those to `phylo_path`

, and will be respected in
downstream functions.

Below I recreate the phylogenetic path analysis described in:

Gonzalez-Voyer A & von Hardenberg A. 2014. An Introduction to Phylogenetic Path Analysis. Chapter 8. In: Garamszegi LZ (ed.), Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. pp. 201-229. Springer-Verlag Berlin Heidelberg.

You can find this book chapter online. For an introduction to the methodology, as well as the data, see the wonderful book chapter.

Specifically, we recreate the Rhinogrades example here. The data used has been included in this package.

Following figure 8.7, we first create all 9 causal models using the
`define_model_set`

function. This function uses regression
equations (or `formula`

s) to express the hypothesized
relationships in the models. Formulas should be of the form
`parent ~ child`

and describe each path in your model.
Multiple children of a single parent can be combined into a single
formula: `parent ~ child1 + child2`

. Use `.common`

to include paths that should appear in all your models.

This is usually the hardest part of the analysis, both in thinking and in coding. Make sure you have a reasonable set of models that describe the different hypotheses that you want to compare. I’d advise against just trying to run all combinations you can think of. As for the coding, double-check the outcome is actually what you wanted (use the plots below), typos are easily made!

```
library(phylopath)
<- define_model_set(
models one = c(RS ~ DD),
two = c(DD ~ NL, RS ~ LS + DD),
three = c(RS ~ NL),
four = c(RS ~ BM + NL),
five = c(RS ~ BM + NL + DD),
six = c(NL ~ RS, RS ~ BM),
seven = c(NL ~ RS, RS ~ LS + BM),
eight = c(NL ~ RS),
nine = c(NL ~ RS, RS ~ LS),
.common = c(LS ~ BM, NL ~ BM, DD ~ NL)
)
```

The `define_model_set`

function simply produces a set of
matrices that summarize the connections between the variables. For
example:

`$one models`

```
## BM NL DD RS LS
## BM 0 1 0 0 1
## NL 0 0 1 0 0
## DD 0 0 0 1 0
## RS 0 0 0 0 0
## LS 0 0 0 0 0
## attr(,"class")
## [1] "matrix" "array" "DAG"
```

It is good to check if the DAG looks like you were expecting. Instead
of staring at a bunch of matrices, simply `plot`

one of the
models to inspect it visually.

`plot(models$one)`

Or better yet, plot all of the models at once:

`plot_model_set(models)`

Now that we have the model set, we can perform the path analysis
using the `phylo_path`

function. For this we will need a data
set, included in this package as `rhino`

, as well as a
phylogenetic tree, `rhino_tree`

. The package will take care
of finding the d-separation statements and fitting the necessary
models.

Importantly, when using PGLS, we need to be consistent in which
variables are used as independent and dependent variables in the
analysis. If one has a specific idea about which variables are to be
considered as up- and down-stream, then you can use the
`order`

argument to give the ordering (from up to down). In
this case, we supply the ordering to mimic the choices made by the
chapter authors. Alternatively, you can choose to not supply an order,
and the function will try to make a sensible order by itself. If the
combination of all causal models is itself a DAG, the ordering of that
model will be used, otherwise the ordering will be constructed by
consensus (i.e. the most common ordering is chosen).

**Generally, I advise to not supply the order argument, in
order to reduce “researcher degrees of freedom”.**

By default, `phylo_path`

uses Pagel’s “lambda” model of
evolution (specified here for clarity), but if you want, for example, to
use a simple Brownian motion model, you can supply
`model = 'BM'`

instead.

`<- phylo_path(models, data = rhino, tree = rhino_tree, model = 'lambda') result `

The result we end up with is a `phylo_path`

object. Simply
printing it gives us a quick summary of what is in the object. In this
case we end up with five continuous variables, nine causal models and 21
unique regressions.

` result`

```
## A phylogenetic path analysis, on the variables:
## Continuous: BM NL DD RS LS
## Binary:
##
## Evaluated for these models: one two three four five six seven eight nine
##
## Containing 46 phylogenetic regressions, of which 22 unique
```

To get an overview of the analysis, we can ask for its
`summary`

:

`<- summary(result)) (s `

```
## model k q C p CICc delta_CICc l w
## eight eight 6 9 6.11 9.10e-01 26.1 0.00 1.00e+00 3.44e-01
## four four 5 10 4.78 9.05e-01 27.3 1.14 5.66e-01 1.95e-01
## six six 5 10 4.97 8.93e-01 27.4 1.33 5.14e-01 1.77e-01
## nine nine 5 10 5.73 8.37e-01 28.2 2.09 3.52e-01 1.21e-01
## five five 4 11 3.46 9.03e-01 28.5 2.34 3.10e-01 1.07e-01
## seven seven 4 11 4.70 7.89e-01 29.7 3.59 1.66e-01 5.72e-02
## three three 6 9 27.17 7.30e-03 47.2 21.06 2.68e-05 9.20e-06
## one one 6 9 62.01 9.70e-09 82.0 55.89 7.29e-13 2.51e-13
## two two 5 10 60.97 2.38e-09 83.4 57.33 3.56e-13 1.23e-13
```

And plotting that summary gives us a quick overview of the support for each model:

`plot(s)`

To view the best ranked model, we can use `best`

. This
returns a DAG with standardized regression coefficients, as well as a
matrix of standard errors. These can be obtained for any particular
model we looked at by using the `choice`

function.

_Note: These functions can also obtain confidence intervals for the
estimates, which `phylolm`

achieves through bootstrapping.
This makes these very slow though, and by default this is turned off.
Pass the number of bootstrap replicates you want to any modelling
function in this package via the `boot`

parameter to estimate
confidence intervals, e.g. `best(result, boot = 500)`

. Either
standard errors or confidence intervals can be plotted with
`coef_plot`

(see below).

`<- best(result)) (best_model `

```
## $coef
## BM LS RS NL DD
## BM 0 0.4973937 0 0.4613623 0.0000000
## LS 0 0.0000000 0 0.0000000 0.0000000
## RS 0 0.0000000 0 0.5280685 0.0000000
## NL 0 0.0000000 0 0.0000000 0.6285344
## DD 0 0.0000000 0 0.0000000 0.0000000
##
## $se
## BM LS RS NL DD
## BM 0 0.08934185 0 0.06500775 0.00000000
## LS 0 0.00000000 0 0.00000000 0.00000000
## RS 0 0.00000000 0 0.05726520 0.00000000
## NL 0 0.00000000 0 0.00000000 0.08006703
## DD 0 0.00000000 0 0.00000000 0.00000000
##
## attr(,"class")
## [1] "fitted_DAG"
```

This object can also be plotted, now the numbers and width of the arrow represent path coefficients. In this case, all paths are green since all relationships are positive.

`plot(best_model)`

From the summary we could see that in reality, there are several
models that are quite good. Instead of using the best model, we can use
the average of the best models, weighted by their relative evidence. By
simply calling `average`

, we can obtain the coefficients and
standard errors of the averaged model where the CICc
`cut_off`

is 2 by default. If a model does not include a
path, we assume that coefficient to be 0.

`<- average(result) average_model `

```
## Registered S3 methods overwritten by 'MuMIn':
## method from
## nobs.phylolm phylolm
## logLik.phylolm phylolm
```

`plot(average_model, algorithm = 'mds', curvature = 0.1) # increase the curvature to avoid overlapping edges`

Note that, by default, the path averaging is only done for the models
that actually contain that path. This facilitates the detection of weak
effects, but also biases coefficients away from zero. Alternatively, we
can assume the coefficients (and their variance) for absent paths to be
zero by setting `avg_method = "full"`

.

```
<- average(result, avg_method = "full")
average_model_full plot(average_model_full, algorithm = 'mds', curvature = 0.1)
```