Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/transformPhylo.MCMC.R
Fits Bayesian models for various models of continuous character evolution using a MetropolisHastings Markov Chain Monte Carlo (MCMC) approach
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  transformPhylo.MCMC(
y,
phy,
model,
mcmc.iteration = 1000,
burn.in = 0.1,
hiddenSpeciation = FALSE,
full.phy = NULL,
lowerBound = NULL,
upperBound = NULL,
useMean = FALSE,
random.start = TRUE,
meserr = NULL,
covPIC = TRUE,
lambdaEst = FALSE,
nodeIDs = NULL,
branchLabels = NULL,
acdcScalar = FALSE,
sample.every = 10
)

y 
A matrix of trait values. 
phy 
An object of class 
model 
The model of trait evolution (see details). 
mcmc.iteration 
Integer  the number of generations for which to run the MCMC chain 
burn.in 
The proportion of the chain (as given by mcmc.iteration) which to discard as 'burnin' 
hiddenSpeciation 
Logical. If TRUE the psi model will include nodes that are on the 'full.phy' but not the tree pruned of trait data 
full.phy 
The full phylogeny containing the species that do not contain trait data so are not included in 'phy' 
lowerBound 
Minimum value for parameter estimates 
upperBound 
Maximum value for parameter estimates 
useMean 
Logical. Use the branchbased estimates of extinction of mean (TRUE, default) for the "psi" and "multispi" models only applicable if "hiddenSpeciation" = TRUE 
random.start 
Use a random starting value for the MCMC run (TRUE), or use the environment set.seed() value 
meserr 
A vector (or matrix) of measurement error for each tip. This is only applicable to univariate analyses 
covPIC 
Logical. For multivariate analyses, allow for covariance between traits rates (TRUE) or no covariance in trait rates (FALSE). If FALSE, only the trait variances not covariances are used. 
lambdaEst 
Logical. Estimate lambda alongside parameter estimates to reduce data noise. Only applicable for models "kappa", "delta", "OU", "psi", "multispi", and "ACDC". Default=FALSE. 
nodeIDs 
Integer  ancestral nodes of clades applicable to rate heterogenous and nested models of evolution (see details) 
branchLabels 
Branches on which different psi parameters are estimated in the "multipsi" model 
acdcScalar 
Logical. For nested EB rate model, simultaneously estimated a rate scalar alongside EB model. Default=FALSE. 
sample.every 
Number specifying the every nth that is sampled in the MCMC chain (default = 1). 
The method estimates posterior probabilities using a MetropolisHastings MCMC approach that places a prior bounded uniform distribution on all parameters with an independence sampler. These prior distributions can be altered by changing the upperBound and lowerBound arguments. The MCMC model will estimate the posterior probability for the following models:
model="kappa" fits Pagel's kappa by raising all branch lengths to the power kappa. As kappa approaches zero, trait change becomes focused at branching events. For complete phylogenies, if kappa approaches zero this infers speciational trait change. Default bounds from ~0  1.
model="lambda" fits Pagel's lambda to estimate phylogenetic signal by multiplying all internal branches of the tree by lambda, leaving tip branches as their original length (root to tip distances are unchanged). Default bounds from ~0  1.
model="delta" fits Pagel's delta by raising all node depths to the power delta. If delta <1, trait evolution is concentrated early in the tree whereas if delta >1 trait evolution is concentrated towards the tips. Values of delta above one can be difficult to fit reliably. Default bounds from ~0  5.
model="OU" fits an OrnsteinUhlenbeck model  a random walk with a central tendency proportional to alpha. High values of alpha can be interpreted as evidence of evolutionary constraints, stabilising selection or weak phylogenetic signal. It is often difficult to distinguish among these possibilities. Default bounds from ~0  10.
model="psi" fits a accelerationdeacceleration model to assess to the relative contributions of speciation and gradual evolution to a trait's evolutionary rate (Ingram 2010).
model="ACDC" fits a model to in which rates can exponentially increased or decrease through time (Blomberg et al. 2003). If the upper bound is < 0, the model is equivalent to the 'Early Burst' model of Harmon et al. 2010. Default rate parameter bounds from ln(1e10) ~ ln(20) divided by the root age.
median The median estimate of the posterior for the parameter
95.HPD The 95 percent Highest Posterior Density for the parameter
ESS Effective Sample Size for the posterior
acceptance.rate The ratio for which new proposals were accepted during the MCMC chain
mcmc.chain Full MCMC chain containing all iterations (including burnin)
Mark Puttick, Gavin Thomas
transformPhylo.ML
, transformPhylo.ll
, transformPhylo
1 2 3 4 5 6 7 8 9 10 11 12  data(anolis.tree)
data(anolis.data)
attach(anolis.data)
male.length < matrix(Male_SVL, dimnames=list(rownames(anolis.data)))
sortedData < sortTraitData(anolis.tree, male.length)
phy < sortedData$phy
male.length < sortedData$trait
phy.clade < extract.clade(phy, 182)
male.length.clade < as.matrix(male.length[match(phy.clade$tip.label, rownames(male.length)),])
## please note, this model will be need to run for longer to achieve convergence
lambda.mcmc < transformPhylo.MCMC(y=male.length.clade, phy=phy.clade,
model="lambda", mcmc.iteration=100, burn.in=0.1)

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