Methods using a large number of parameters risk being overfit. This usually translates in poor fitting with data and trees other than the those originally used. With RRphylo methods this risk is usually very low. However, the user can assess how robust the results got by applying
PGLS_fossil are by running
overfitRR. With the latter, the original tree and data are subsampled by specifying a
s parameter, that is the proportion of tips to be removed from the tree. Internally,
overfitRR further shuffles the tree by using the function
swapONE. Thereby, both the potential for overfit and phylogenetic uncertainty are accounted for straight away.
overfitRR always takes an object generated by
RRphylo and all the data used to produce it (besides necessary phenotypic data, any other argument such as covariate, predictor, and so on, passed to
RRphylo). The arguments
swap.args can be used to set the intensity of subsampling and phylogenetic alterations to be applied. Depending on which tool is under testing, the user supplies to the funcion one or more among
conv.args, each of them being a list of arguments specific to the namesake function (see the examples below).
The output of
overfitRR is a
RRphyloList object whose elements are different depending on the case under testing (see below).
In some cases, removing as many tips as imposed by
s would delete too many tips right in clades and/or states under testing. In these cases, the function maintains no less than 5 species at least in each clade/state under testing (or all species if there is less), reducing the sampling parameter
s if necessary. Thus, the first element of the output list (
$mean.sampling) is the mean proportion of species actually removed over the iterations.
In any case, the function returns a
multiPhylo and a
RRphyloList object including the modified phylogenies (
$tree.list) and the outputs of
RRphylo performed on them (
$RR.list), respectively. Both object are treated as regular lists.
overfitRR also derives the 95% confidence interval around the original phenotypic value estimated at the tree root (
$rootCI) and the regression parameters describing the relation between the original values at internal nodes and the corresponding figure after subsampling and swapping (
$ace.regressions). A regression slope close to one indicates a better matching between original and subsampled values, suggesting the estimation is robust to phylogenetic uncertainty and subsampling.
When the robustness of
search.shift is tested, the function returns separate results for
sparse conditions (
$shift.results). The first (clade) includes the proportion of simulations producing significant and positive (p.shift+) or significant and negative (p.shift-) rate shifts for each single node, and for all the clades taken as a whole (see Testing rate shifts pertaining to entire clades for further details). Under the
sparse condition (sparse), the same figures as before are reported for each state category compared to the rest of the tree and for all possible pair of categories (see Testing rate shifts pertaining to phylogenetically unrelated species for further details)
When testing for
overfitRR returns results for both the entire tree and specific clades if indicated (
$trend.results). Results for the entire tree (tree) summarize the proportion of simulations producing significant and positive (p.slope+) or significant and negative (p.slope-) trends in either phenotypes or absolute rates versus time regressions. Such evaluations is based on p.random only (see Temporal trends on the entire tree,for further details). When specific clades are under testing, the same set of results as for the whole tree is returned for each node (node). In this case, for phenotype versus age regression through nodes, the proportion of significant and positive/negative slopes (p.slope+ and p.slope-) is accompanied by the same figures for the estimated marginal mean differences (p.emm+ and p.emm-). As for the temporal trend in absolute rates through node, the proportion of significant and positive/negative estimated marginal means differences (p.emm+ and p.emm-) and the same figure for slope difference (p.slope+ and p.slope-) are reported (see Temporal trends at clade level). Finally when more than one node is tested, the
$trend.results object also includes results for the pairwise comparison between nodes.
Results for robustness of
$conv.results) include separate objects for convergence between
clades or between/within
states. Under the first case (clade), the proportion of simulations producing significant instance of convergence (p.ang.bydist) or convergence and parallelism (p.ang.conv) between selected clades are returned (see Morphological convergence between clades for further details). As for convergence between/within discrete categories (state),
overfitRR reports the proportion of simulations producing significant instance of convergence either accounting (p.ang.state.time) or not accounting (p.ang.state) for the time intervening between the tips in the focal state Morphological convergence within/between categories for explanations).
Results for robustness of
$pgls.results) include separate objects for the pgls performed on the original tree (i.e. fitting Pagel’s lambda in the regression for univariate data or using the tree variance covariance matrix in the multivariate case;
$tree) or on the tree rescaled according to
RRphylo rates (i.e. tree branches rescaled to the absolute branch-wise rate values while keeping the total evolutionary time constant;
library(ape) # load the RRphylo example dataset including Ornithodirans tree and data $treedino->treedino # phylogenetic tree DataOrnithodirans$massdino->massdino # body mass data DataOrnithodirans$statedino->statedino # locomotory type data DataOrnithodirans ### Testing search.shift # perform RRphylo Ornithodirans tree and data RRphylo(tree=treedino,y=massdino)->dinoRates # perform search.shift under both "clade" and "sparse" condition search.shift(RR=dinoRates, status.type= "clade",filename=tempdir())->SSnode search.shift(RR=dinoRates, status.type= "sparse", state=statedino, filename=tempdir())->SSstate # test the robustness of search.shift results overfitRR(RR=dinoRates,y=massdino,swap.args =list(si=0.2,si2=0.2), shift.args = list(node=rownames(SSnode$single.clades),state=statedino), nsim=10) ### Testing search.trend # Extract Pterosaurs tree and data extract.clade(treedino,748)->treeptero # phylogenetic tree match(treeptero$tip.label,names(massdino))]->massptero # body mass data massdino[match(treeptero$tip.label,names(massptero))]->massptero massptero[ # perform RRphylo and search.trend on Pterosaurs tree and data # by specifying a clade to be tested RRphylo(tree=treeptero,y=log(massptero))->RRptero search.trend(RR=RRptero, y=log(massptero),node=143,filename=tempdir(), cov=NULL,ConfInt=FALSE)->STnode # test the robustness of search.trend results overfitRR(RR=RRptero,y=log(massptero),trend.args = list(node=143),nsim=10) ### Applying overfitRR on multiple RRphylo # load the RRphylo example dataset including Cetaceans tree and data data("DataCetaceans") $treecet->treecet # phylogenetic tree DataCetaceans$masscet->masscet # logged body mass data DataCetaceans$brainmasscet->brainmasscet # logged brain mass data DataCetaceans$aceMyst->aceMyst # known phenotypic value for the most recent DataCetaceans# common ancestor of Mysticeti # cross-reference the phylogenetic tree and body and brain mass data. Remove from # both the tree and vector of body sizes the species whose brain size is missing drop.tip(treecet,treecet$tip.label[-match(names(brainmasscet), $tip.label)])->treecet1 treecetmatch(treecet1$tip.label,names(masscet))]->masscet1 masscet[ # peform RRphylo on the variable (body mass) to be used as additional predictor RRphylo(tree=treecet1,y=masscet1)->RRmass $aces[,1]->acemass1 RRmass # create the predictor vector: retrieve the ancestral character estimates # of body size at internal nodes from the RR object ($aces) and collate them # to the vector of species' body sizes to create c(acemass1,masscet1)->x1.mass # peform RRphylo and search.trend on the brain mass # by using the body mass as additional predictor RRphylo(tree=treecet1,y=brainmasscet,x1=x1.mass)->RRmulti search.trend(RR=RRmulti, y=brainmasscet,x1=x1.mass,filename=tempdir())->STcet # test the robustness of search.trend results overfitRR(RR=RRmulti,y=brainmasscet,trend.args = list(),x1=x1.mass,nsim=10) ### Testing PGLS_fossil # peform RRphylo on cetaceans brain mass RRphylo(tree=treecet1,y=brainmasscet)->RRbrain # perform PGLS_fossil by using the original tree PGLS_fossil(y~x,data=list(y=brainmasscet,x=masscet1),tree=treecet1)->pgls_noRR # perform PGLS_fossil rescaling the tree according to RRphylo rates PGLS_fossil(y~x,data=list(y=brainmasscet,x=masscet1),tree=RRbrain$tree,RR=RRbrain)->pgls_RR # test the robustness of PGLS_fossil results overfitRR(RR=RRbrain,y=brainmasscet, pgls.args=list(modform=y~x,data=list(y=brainmasscet,x=masscet1),tree=TRUE,RR=TRUE), nsim=10) ### Testing search.conv # load the RRphylo example dataset including Felids tree and data data("DataFelids") $PCscoresfel->PCscoresfel # mandible shape data DataFelids$treefel->treefel # phylogenetic tree DataFelids$statefel->statefel # conical-toothed or saber-toothed condition DataFelids # perform RRphylo on Felids tree and data RRphylo(tree=treefel,y=PCscoresfel)->RRfel # search for morphologicl convergence between clades (automatic mode) # and within the category search.conv(RR=RRfel, y=PCscoresfel, min.dim=5, min.dist="node9", filename = tempdir())->SC.clade as.numeric(c(rownames(SC.clade[]),as.numeric(as.character(SC.clade[][1,1]))))->conv.nodes search.conv(tree=treefel, y=PCscoresfel, state=statefel, filename = tempdir())->SC.state # test the robustness of seach.conv results overfitRR(RR=RRfel, y=PCscoresfel,conv.args= list(node=conv.nodes,state=statefel,declust=TRUE),nsim=10)