traitGmyc: Optimizes genetic and phenotypic clusters using a combination...
In thauffe/TraitGmyc: What the package does (short line)

Description Usage Arguments Value Author(s) Examples

This function fits within- and between-species branching models to reconstructed gene trees, known as the generalized mixed yule coalescent (GMYC) model, and the Brownian motion process to (continous) trait evolution with a change in evolutionary rates.

traitGmyc(
  tr,
  interval = c(0, 5),
  trait,
  meserr = NULL,
  traitmodel = "BMBM",
  quiet = TRUE,
  ncores = 1
)

`tr`	An ultrametric, dichotomous tree object in ape format.
`interval`	Upper and lower limit of estimation of scaling parameters, e.g. c(0,10)
`trait`	A matrix of trait values with rownames corresponding to tips of the tree (NA for missing traits)
`meserr`	A data.frame (or matrix) of squared standard error for each traits. Can contain NAs. row.names should match tip labels of the phylogeny.
`traitmodel`	Model of trait evolution. Either "BMBM" where a shift in rate of morphological variation is modeled. Or "BMWN" where a shift in rate together with a shift in mode of evolution from Brownian motion towards no phylogenetic signal is modeled (i.e. white noise).
`quiet`	By default shows no progress on console. Use quiet = TRUE to enable.
`ncores`	Number of cores used for fitting models of trait evolution

traitgmyc returns an object of class "traitgmyc": a list with the following elements

`method`	method used for an analysis
`likelihood`	likelihood values for each gmyc optimization
`parameters`	estimated parameters for each gmyc optimization. (lambda1, lambda2, pp1, pp2)
`entity`	numbers of entities
`cluster`	numbers of clusters
`MRCA`	index of MRCA nodes, i.e. ancestral node of each delimited cluster
`threshold.time`	optimized threshold times
`tree`	the tree
`traitsGmyc`	data.frame with likelihood and rate parameters for Brownian motion process with a shift at gmyc threshold times
`trait`	traits
`sum_likelihoods`	sum of gmyc and Brownian motion likelihood

Torsten Hauffe and Robin Ackermann

## Not run: 
N <- 10
SpeciesResult <- data.frame(Species = 1:N,
                            Individuals = sample(2:20, N, replace = TRUE))
SpeciesResult$Theta <- runif(N, min = 0.01, max = 0.7)
Tree <- pbtree(b = 0.2, n = N)
Tree <- replaceTiplabel(Tree, Newlabel = "Tip")
GmycTree <- gmycSimulatedTree(Tree, SpeciesResult, Scale = FALSE)
GmycTreePainted <- paintSpeciesBranches(GmycTree)

Ntraits <- 3
SigmasSpecies <- simSigma(Ntraits)
Cor <- cov2cor(SigmasSpecies)
PopSigmaMulti <- 2
SigmasPopulations <- simSigma(Ntraits,
                              Cor = Cor[lower.tri(Cor)],
                              Sigma2 = PopSigmaMulti * sqrt(diag(SigmasSpecies)))

Sigmas <- list(Species = SigmasSpecies, Populations = SigmasPopulations)
SimTraits <- mvSIM(GmycTreePainted, model = "BMM",
                   param = list(ntraits = Ntraits,
                                theta = rep(0, Ntraits),
                                sigma = Sigmas))
SimTraits[1,1] <- NA

Res <- traitGmyc(tr = GmycTree,
                 trait = SimTraits,
                 meserr = NULL,
                 quiet = TRUE,
                 ncores = 1)
plot(Res)

SpeciesTree <- pbtree(b = 0.27, n = 10)
GeneTree <- simGenealogy(Species = SpeciesTree,
                         Scenario = "B",
                         Ind = 5,
                         PopSize = 100000)
SimTraits <- simTraitsIndividuals(SpeciesTree,
                                  Ntraits = 4,
                                  IndPop = GeneTree$Species,
                                  Sigma2 = rep(1, 4))
Res <- traitGmyc(tr = GeneTree$Genealogy,
                 trait = SimTraits,
                 traitmodel = "BMWN",
                 meserr = NULL,
                 quiet = TRUE,
                 ncores = 1)
plot(Res, ask = FALSE)

## End(Not run)