Influential clades

Sensitivity analysis for influential clades (Primates data):

The clade method can be used to estimate and test the influence of specific clades on parameter estimates.

1. clade_phylm(): Phylogenetic linear regression

• Additional arguments:
  clade.col: The name of a column in the provided data frame with clades specification (a character vector with clade names).
  n.species: Minimum number of species in the clade in order to include this clade in the leave-one-out deletion analysis. Default is 5.
  n.sim: The number of repetitions for the randomization test

data(primates)
# run analysis:
clade <- clade_phylm(log(sexMaturity) ~ log(adultMass), phy = primates$phy[[1]],
                     data = primates$data, clade.col = "family")
# To check summary results and most influential clades:
summary(clade)
# Visual diagnostics for clade removal:
sensi_plot(clade, "Cercopithecidae")
sensi_plot(clade, "Cebidae")

sensi_plot(clade, "Cercopithecidae")

sensi_plot(clade, "Cebidae")

2. clade_physig(): Phylogenetic signal

# Logtransform data
alien.data$logMass <- log(alien.data$adultMass) 
# Run sensitivity analysis:
clade <- clade_physig(trait.col = "logMass", data = alien.data, n.sim = 100,
                      phy = alien.phy[[1]], clade.col = "family", method = "K")
summary(clade)
sensi_plot(clade, "Bovidae")
sensi_plot(clade, "Sciuridae")

sensi_plot(clade, "Bovidae")

sensi_plot(clade, "Sciuridae")

3. clade_continuous() and clade_discrete: Trait Evolution Continous and Discrete Characters

Continuous characters

# Load data:
data("primates")
# Model trait evolution accounting for influential clades
clade_cont <- clade_continuous(data=primates$data, phy = primates$phy[[1]], 
model="OU", trait.col = "adultMass", clade.col="family", n.sim=30, n.species=10, 
n.cores = 2, track=TRUE)

# Check summary statistics for the transitions rates, aic-values and (if applicable) optimisation parameter
summary(clade_cont)

# Visual diagnostics
sensi_plot(clade_cont,graph="all")
# Plot only one clade and one parameter
sensi_plot(clade_cont,clade="Cebidae",graph = "optpar")

sensi_plot(clade_cont,clade="Cebidae",graph = "optpar")

Different evolutionary models from fitContinuous can be used: BM,OU, EB, trend, lambda, kappa, delta and drift.

Discrete characters

# Load data:
data("primates")

# Create a binary trait factor 
primates$data$adultMass_binary<-ifelse(primates$data$adultMass > 7350, "big", "small")
# Model trait evolution accounting for influential clades
clade_disc <- clade_discrete(data=primates$data,phy = primates$phy[[1]],
                             model="ARD",transform="kappa",
                             trait.col = "adultMass_binary",clade.col="family",n.sim = 30,
                             n.species=8,n.cores = 2)

# Check summary statistics:
summary(clade_disc)

# Visual diagnostics
sensi_plot(clade_disc)

sensi_plot(clade_disc)

Different character models from fitDiscrete can be fit by changing the model argument. These include ER (equal-rates), SYM (symmetric), ARD (all-rates-different) and meristic (stepwise fashion). Similarly, all transformations to the phylogenetic tree from fitDiscrete can be used: none, EB, lambda, kappa and delta.