Searching for evolutionary trends in phenotypes and rates
This function searches for evolutionary trends in the phenotypic mean and the evolutionary rates for the entire tree and individual clades.
search.trend(RR,y,x1=NULL,x1.residuals = FALSE,nsim=100,clus=0.5,node=NULL,cov=NULL, foldername,ConfInt=FALSE)
RR |
an object produced by |
y |
the named vector (or matrix if multivariate) of phenotypes. |
x1 |
the additional predictor to be specified if the RR object has been
created using an additional predictor (i.e. multiple version of
|
x1.residuals |
logical specifying whether the residuals of regression
between |
nsim |
number of simulations to be performed. It is set at 100 by default. |
clus |
the proportion of clusters to be used in parallel computing. To
run the single-threaded version of |
node |
the node number of individual clades to be specifically tested
and contrasted to each other. It is |
cov |
the covariate values to be specified if the RR object has been
created using a covariate for rates calculation. As for |
foldername |
the path of the folder where plots are to be found. |
ConfInt |
if |
The function simultaneously returns the regression of phenotypes and
phenotypic evolutionary rates against age tested against Brownian motion
simulations to assess significance. It stores the rates (absolute values)
versus age regression and the phenotype versus age regression plots as .pdf
files. In the plots, the 95% confidence intervals of phenotypes and rates
simulated under the Brownian motion for each node are plotted as shaded
areas. Regression lines are printed for all regressions. To assess
significance, slopes are compared to a family of simulated slopes
(BMslopes, where the number of simulations is equal to nsim),
generated under the Brownian motion, using the fastBM function in
the package phytools. Individual nodes are compared to the rest of
the tree in different ways depending on whether phenotypes or rates versus
age regressions are tested. With the former, the regression slopes for
individual clades and the slope difference between clades is contrasted to
slopes obtained through Brownian motion simulations. For the latter,
regression models are tested and contrasted to each other referring to
estimated marginal means, by using the emmeans function in the
package emmeans.
The multiple regression version of RRphylo allows
to incorporate the effect of an additional predictor in the computation of
evolutionary rates without altering the ancestral character estimation.
Thus, when a multiple RRphylo output is fed to search.trend, the
predictor effect is accounted for on the absolute evolutionary rates, but
not on the phenotype. However, in some situations the user might want to
‘factor out’ the predictor effect on phenotypes as well. Under the latter
circumstance, by setting the argument x1.residuals = TRUE, the residuals
of the response to predictor regression are used as to represent the
phenotype.
The function returns a list object including:
$rbt for each branch of the tree, there are the
RRphylo rates and the distance from the tree root (age). If y is
multivariate, it also includes the multiple rates for each y vector. If
node is specified, each branch is classified as belonging or not to
the indicated clades.
$pbt a data frame of phenotypic values (or y versus
x1 regression residuals if x1.residuals=TRUE) and their
distance from the tree root for each node (i.e. ancestral states) and tip
of the tree.
$phenotypic.regression results of phenotype (y versus
x1 regression residuals) versus age regression. It reports a p-value
for the regression slope between the variables (p.real), a p-value computed
contrasting the real slope to Brownian motion simulations (p.random), and a
parameter indicating the deviation of the phenotypic mean from the root
value in terms of the number of standard deviations of the trait
distribution (dev). dev is 0 under Brownian Motion. Only p.random should be
inspected to assess significance.
$rate.regression results of the rates (absolute values) versus age regression. It reports a p-value for the regression between the variables (p.real), a p-value computed contrasting the real slope to Brownian motion simulations (p.random), and a parameter indicating the ratio between the range of phenotypic values and the range of such values halfway along the tree height, divided to the same figure under Brownian motion (spread). spread is 1 under Brownian Motion. Only p.random should be inspected to assess significance.
$ConfInts the 95% confidence intervals around phenotypes and rates produced according to the Brownian motion model of evolution.
If specified, individual nodes are tested as the whole tree, the results are summarized in the objects:
$node.phenotypic.regression results of phenotype (or
y versus x1 regression residuals) versus age regression
through node. It reports the slope for the regression between the variables
at node (slope), a p-value computed contrasting the real slope to Brownian
motion simulations (p.random), the difference between estimated marginal
means predictions for the group and for the rest of the tree
(emm.difference), and a p-value for the emm.difference (p.emm).
$node.rate.regression results of the rates (absolute values) versus age regression through node. It reports the difference between estimated marginal means predictions for the group and for the rest of the tree (emm.difference), a p-value for the emm.difference (p.emm), the difference between regression slopes for the group and for the rest of the tree (slope.difference), and a p-value for the slope.difference (p.slope).
If more than one node is specified, the object $group.comparison reports the same results as $node.phenotypic.regression and $node.rate.regression obtained by comparing individual clades to each other.
Silvia Castiglione, Carmela Serio, Pasquale Raia, Alessandro Mondanaro, Marina Melchionna, Mirko Di Febbraro, Antonio Profico, Francesco Carotenuto
Castiglione, S., Serio, C., Mondanaro, A., Di Febbraro, M., Profico, A., Girardi, G., & Raia, P. (2019) Simultaneous detection of macroevolutionary patterns in phenotypic means and rate of change with and within phylogenetic trees including extinct species. PLoS ONE, 14: e0210101. https://doi.org/10.1371/journal.pone.0210101
## Not run:
data("DataOrnithodirans")
DataOrnithodirans$treedino->treedino
DataOrnithodirans$massdino->massdino
cc<- 2/parallel::detectCores()
# Extract Pterosaurs tree and data
library(ape)
extract.clade(treedino,746)->treeptero
massdino[match(treeptero$tip.label,names(massdino))]->massptero
massptero[match(treeptero$tip.label,names(massptero))]->massptero
# Case 1. "RRphylo" whitout accounting for the effect of a covariate
RRphylo(tree=treeptero,y=log(massptero))->RRptero
# Case 1.1. "search.trend" whitout indicating nodes to be tested for trends
search.trend(RR=RRptero, y=log(massptero), nsim=100, clus=cc,
foldername=tempdir(),cov=NULL,ConfInt=FALSE,node=NULL)
# Case 1.2. "search.trend" indicating nodes to be specifically tested for trends
search.trend(RR=RRptero, y=log(massptero), nsim=100, node=143, clus=cc,
foldername=tempdir(),cov=NULL,ConfInt=FALSE)
# Case 2. "RRphylo" accounting for the effect of a covariate
# "RRphylo" on the covariate in order to retrieve ancestral state values
RRphylo(tree=treeptero,y=log(massptero))->RRptero
c(RRptero$aces,log(massptero))->cov.values
names(cov.values)<-c(rownames(RRptero$aces),names(massptero))
RRphylo(tree=treeptero,y=log(massptero),cov=cov.values)->RRpteroCov
# Case 2.1. "search.trend" whitout indicating nodes to be tested for trends
search.trend(RR=RRpteroCov, y=log(massptero), nsim=100, clus=cc,
foldername=tempdir(),ConfInt=FALSE,cov=cov.values)
# Case 2.2. "search.trend" indicating nodes to be specifically tested for trends
search.trend(RR=RRpteroCov, y=log(massptero), nsim=100, node=143, clus=cc,
foldername=tempdir(),ConfInt=FALSE,cov=cov.values)
# Case 3. "search.trend" on multiple "RRphylo"
data("DataCetaceans")
DataCetaceans$treecet->treecet
DataCetaceans$masscet->masscet
DataCetaceans$brainmasscet->brainmasscet
DataCetaceans$aceMyst->aceMyst
drop.tip(treecet,treecet$tip.label[-match(names(brainmasscet),treecet$tip.label)])->treecet.multi
masscet[match(treecet.multi$tip.label,names(masscet))]->masscet.multi
RRphylo(tree=treecet.multi,y=masscet.multi)->RRmass.multi
RRmass.multi$aces[,1]->acemass.multi
c(acemass.multi,masscet.multi)->x1.mass
RRphylo(tree=treecet.multi,y=brainmasscet,x1=x1.mass)->RRmulti
# incorporating the effect of body size at inspecting trends in absolute evolutionary rates
search.trend(RR=RRmulti, y=brainmasscet,x1=x1.mass,clus=cc,foldername=tempdir())
# incorporating the effect of body size at inspecting trends in both absolute evolutionary
# rates and phenotypic values (by using brain versus body mass regression residuals)
search.trend(RR=RRmulti, y=brainmasscet,x1=x1.mass,x1.residuals=TRUE,clus=cc,foldername=tempdir())
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