Working With multiPhylo Objects in treedata.table

Working with multiphylo objects

treedata.table further allows the matching of multiple phylogenies (multiPhylo) against a single dataset (data.frame). Below, we modified the anole dataset to explain the extended functionality of treedata.table with multiPhylo objects. Note that all the trees in the multiPhylo must have exactly the same taxa.

We first load the sample dataset.

library(ape)
library(treedata.table)

# Load example data
data(anolis)
#Create treedata.table object with as.treedata.table
td <- as.treedata.table(tree = anolis$phy, data = anolis$dat)

## Tip labels detected in column: X

## Phylo object detected

## All tips from original tree/dataset were preserved

We then create a multiPhylo object including only two phylo objects. Users can provide any number of phylo objects within the multiPhylo object. However, trees can only differ in their topology. In other words, all trees must have the same tip labels.

We also note that both the provided multiPhylo and data.frame should partially overlap

trees<-list(anolis$phy,anolis$phy)
class(trees) <- "multiPhylo"
trees

## 2 phylogenetic trees

Now, we create our treedata.table object by combining the trait data (data.frame) and the newly generated multiPhylo object. Note that there is only a single character matrix.

td <- as.treedata.table(tree=trees, data=anolis$dat)

## Tip labels detected in column: X

## Multiphylo object detected

## All tips from original tree/dataset were preserved

The resulting td object now returns a multiPhylo object under phy. This objectcontains only the overlapping taxa between the multiphylo objects and the input dataset.

class(td$phy);td$phy

## [1] "multiPhylo"

## 2 phylogenetic trees

Please note that all the basic treedata.table functions highlighted above for phylo objects are still functional when treedata.table objects include multiPhylo objects.

td[, head(.SD, 1), by = "ecomorph"]

## $phy 
## 2 phylogenetic trees
## 
## $dat 
##    ecomorph   tip.label      SVL  PCI_limbs  PCII_head PCIII_padwidth_vs_tail
##      <char>      <char>    <num>      <num>      <num>                  <num>
## 1:       TG        ahli 4.039125 -3.2482860  0.3722519             -1.0422187
## 2:       GB  ophiolepis 3.637962  0.7915117  1.4585760             -1.3152005
## 3:       CG     garmani 4.769473 -0.7735264  0.9371249              0.2594994
## 4:       TC    opalinus 3.838376 -1.7794371 -0.3245381              1.5569939
## 5:       TW valencienni 4.321524  2.9424139 -0.8846007              1.8543308
## 6:        U  reconditus 4.482607 -2.7270416 -0.2104066             -2.3534242
##    PCIV_lamella_num awesomeness   hostility    attitude      island
##               <num>       <num>       <num>       <num>      <char>
## 1:       -2.4147423 -0.24165170 -0.17347691  0.64437708        Cuba
## 2:       -2.2377514  0.35441877  0.05366142 -0.09389530        Cuba
## 3:        0.1051149  0.16779131  0.67675600 -0.69460080 Puerto Rico
## 4:        0.9366501  1.48302162 -0.90826653  0.72613483     Jamaica
## 5:        0.1288233 -0.08837008  0.46528679 -0.56754896     Jamaica
## 6:       -0.7992905  0.26096544 -0.27169792  0.01367143     Jamaica

Functions can also be run on any treedata.table object with multiphylo data. For instance, the following line will fit a phenogram for SVL on each of the trees we provided in the multiPhylo object.

tdt(td, geiger::fitContinuous(phy, extractVector(td, 'SVL'), model="BM", ncores=1))

## Multiphylo object detected. Expect a list of function outputs

## [[1]]
## GEIGER-fitted comparative model of continuous data
##  fitted 'BM' model parameters:
##  sigsq = 0.136160
##  z0 = 4.065918
## 
##  model summary:
##  log-likelihood = -4.700404
##  AIC = 13.400807
##  AICc = 13.524519
##  free parameters = 2
## 
## Convergence diagnostics:
##  optimization iterations = 100
##  failed iterations = 0
##  number of iterations with same best fit = 100
##  frequency of best fit = 1.000
## 
##  object summary:
##  'lik' -- likelihood function
##  'bnd' -- bounds for likelihood search
##  'res' -- optimization iteration summary
##  'opt' -- maximum likelihood parameter estimates
## 
## [[2]]
## GEIGER-fitted comparative model of continuous data
##  fitted 'BM' model parameters:
##  sigsq = 0.136160
##  z0 = 4.065918
## 
##  model summary:
##  log-likelihood = -4.700404
##  AIC = 13.400807
##  AICc = 13.524519
##  free parameters = 2
## 
## Convergence diagnostics:
##  optimization iterations = 100
##  failed iterations = 0
##  number of iterations with same best fit = 100
##  frequency of best fit = 1.000
## 
##  object summary:
##  'lik' -- likelihood function
##  'bnd' -- bounds for likelihood search
##  'res' -- optimization iteration summary
##  'opt' -- maximum likelihood parameter estimates

The output is an object of class list with each element corresponding to the output function of each tree in the provided multiPhylo object.