Type:	Package
Title:	Bayesian Fitting of Ornstein-Uhlenbeck Models to Phylogenies
Version:	2.3.2
Date:	2026-02-04
Maintainer:	Josef C. Uyeda <juyeda@vt.edu>
Description:	Fits and simulates multi-optima Ornstein-Uhlenbeck models to phylogenetic comparative data using Bayesian reversible-jump methods. See Uyeda and Harmon (2014) <doi:10.1093/sysbio/syu057>.
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Depends:	ape (≥ 3.0-6), coda, geiger(≥ 2.0), phytools, R (≥ 2.15.0)
Imports:	assertthat, denstrip, fitdistrplus, foreach, graphics, grDevices, MASS, Matrix, mnormt, Rcpp (≥ 0.10.3), stats
Suggests:	doParallel, testthat
LinkingTo:	Rcpp, RcppArmadillo
Encoding:	UTF-8
RoxygenNote:	7.3.2
NeedsCompilation:	yes
Packaged:	2026-02-11 17:53:47 UTC; juyeda
Author:	Josef C. Uyeda [aut, cre], Jon Eastman [ctb], Luke Harmon [ctb], Abhishek Hari [ctb]
Repository:	CRAN
Date/Publication:	2026-02-13 17:50:15 UTC

Bayesian Fitting of Ornstein-Uhlenbeck Models to Phylogenies

Description

A package for inferring adaptive evolution to phylogenetic comparative data using Bayesian reversible-jump estimation of multi-optima Ornstein-Uhlenbeck models.

Details

bayou-package

Author(s)

Josef C Uyeda

Return a posterior of shift locations

Description

Return a posterior of shift locations

Usage

Lposterior(chain, tree, burnin = 0, simpar = NULL, mag = TRUE)

Arguments

Value

A data frame with rows corresponding to postordered branches. pp indicates the posterior probability of the branch containing a shift. magnitude of theta2 gives the average value of the new optima after a shift. naive SE of theta2 gives the standard error of the new optima not accounting for autocorrelation in the MCMC and rel location gives the average relative location of the shift on the branch (between 0 and 1 for each branch).

Function for calculating likelihood of an OU model in bayou using pruning algorithm or matrix inversion

Description

Function for calculating likelihood of an OU model in bayou using pruning algorithm or matrix inversion

Usage

OU.lik(pars, tree, X, SE = 0, model = "OU", invert = FALSE)

Arguments

Details

This function can be used for calculating single likelihoods using previously implemented methods. It is likely to become deprecated and replaced by bayou.lik in the future, which is based on phylolm's threepoint algorithm, which works on non-ultrametric trees and is substantially faster.

Value

A list returning the log likelihood ("loglik"), the weight matrix ("W"), the optima ("theta"), the residuals ("resid") and the expected values ("Exp").

Calculates the alpha and sigma^2 from a parameter list with supplied phylogenetic half-life and stationary variance

Description

Calculates the alpha and sigma^2 from a parameter list with supplied phylogenetic half-life and stationary variance

Usage

OU.repar(pars)

Arguments

Value

A list with values for alpha and sig2.

Experimental phenogram plotting function for set of model of model parameters

Description

Experimental phenogram plotting function for set of model of model parameters

Usage

OUphenogram(pars, tree, dat, SE = 0, regime.col = NULL, ...)

Arguments

Details

This is an experimental plotting utility that can plot a phenogram with a given regime painting from a parameter list. Note that it uses optimization of internal node states using matrix inversion, which is very slow for large trees. However, what is returned is the maximum likelihood estimate of the internal node states given the model, data and the parameter values.

Value

No return value. This function generates a phenogram plot as a side effect.

Examples

tree <- sim.bdtree(n=50)
tree$edge.length <- tree$edge.length/max(branching.times(tree))
prior <- make.prior(tree, dists=list(dk="cdpois", dsig2="dnorm",
           dtheta="dnorm"), param=list(dk=list(lambda=5, kmax=10),
             dsig2=list(mean=1, sd=0.01), dtheta=list(mean=0, sd=3)),
               plot.prior=FALSE)
pars <- priorSim(prior, tree, plot=FALSE, nsim=1)$pars[[1]]
pars$alpha <- 4
dat <- dataSim(pars, model="OU", phenogram=FALSE, tree)$dat
OUphenogram(pars, tree, dat, ftype="off")

Converts OUwie data into bayou format

Description

OUwie2bayou Converts OUwie formatted data into a bayou formatted parameter list

Usage

OUwie2bayou(tree, trait)

Arguments

Value

A bayou formatted parameter list

Calculates the alpha parameter from a QG model

Description

Calculates the alpha parameter from a QG model

Usage

QG.alpha(pars)

Arguments

Value

An alpha value according to the equation alpha = h2*P/(P+w2+P).

Calculates the sigma^2 parameter from a QG model

Description

Calculates the sigma^2 parameter from a QG model

Usage

QG.sig2(pars)

Arguments

Value

An sig2 value according to the equation alpha = h2*P/(Ne).

Deprecated functions in package bayou.

Description

The functions listed below are deprecated and will be defunct in the near future. When possible, alternative functions with similar functionality are also mentioned. Help pages for deprecated functions are available at help("-deprecated").

Value

This documentation does not return a value. It serves as a reference for deprecated functions in the bayou package.

Function for checking parameter lists, prior and models are consistent and error-free

Description

Function for checking parameter lists, prior and models are consistent and error-free

Usage

bayou.checkModel(
  pars = NULL,
  tree,
  dat,
  pred = NULL,
  SE = 0,
  prior,
  model = "OU",
  autofix = TRUE,
  verbose = TRUE
)

Arguments

Details

A series of checks are performed, run internally within bayou.makeMCMC, but can also be run on provided inputs prior to this. Errors are reported.

If autofix == TRUE, then the following errors will be automatically corrected:

Branch lengths == 0; any branches of length 0 will be given length .Machine$double.eps is.binary(tree) == FALSE; runs multi2di pars do not match prior$fixed; parameters are resimulated from prior

Value

A list of results of the checks and if 'autofix==TRUE', then ..$autofixed returns a list of all the input elements, with corrections.

Function for calculating likelihood of an OU model in bayou using the threepoint algorithm

Description

Function for calculating likelihood of an OU model in bayou using the threepoint algorithm

Usage

bayou.lik(pars, cache, X, model = "OU")

Arguments

Details

This function implements the algorithm of Ho and Ane (2014) implemented in the package phylolm for the OUfixedRoot model. It is faster than the equivalent pruning algorithm in geiger, and can be used on non- ultrametric trees (unlike OU.lik, which is based on the pruning algorithm in geiger).

Value

A list containing:

loglik

The log-likelihood value of the fitted OU model.

theta

A vector of estimated optima for each evolutionary regime.

resid

The residuals, i.e., the differences between observed and expected values.

comp

A list of computed values from the three-point algorithm, including necessary likelihood calculations.

transf.phy

The transformed phylogenetic tree with modified branch lengths based on the model parameters.

Builds a bayouMCMCFn object that can run MCMC and stepping stone analyses.

Description

Runs a reversible-jump Markov chain Monte Carlo on continuous phenotypic data on a phylogeny, sampling possible shift locations and shift magnitudes, and shift numbers.

Usage

bayou.makeMCMC(
  tree,
  dat,
  pred = NULL,
  SE = 0,
  model = "OU",
  prior,
  samp = 10,
  chunk = 100,
  control = NULL,
  tuning = NULL,
  file.dir = tempdir(),
  plot.freq = 500,
  outname = "bayou",
  plot.fn = phenogram,
  ticker.freq = 1000,
  startpar = NULL,
  moves = NULL,
  control.weights = NULL,
  lik.fn = NULL,
  perform.checks = TRUE,
  verbose = TRUE
)

Arguments

Details

By default, the alpha, sig2 (and various reparameterizations of these parameters) are adjusted with multiplier proposals, theta are adjusted with sliding window proposals, and the number of shifts is adjusted by splitting and merging, as well as sliding the shifts both within and between branches. Allowed shift locations are specified by the prior function (see make.prior()).

Value

A bayouMCMCFn object (list).

Converts bayou data into OUwie format

Description

bayou2OUwie Converts a bayou formatted parameter list into OUwie formatted tree and data table that can be analyzed in OUwie

Usage

bayou2OUwie(pars, tree, dat)

Arguments

Value

A list with an OUwie formatted tree with mapped regimes and an OUwie formatted data table

Conditional Poisson distribution

Description

cdpois calculates the probability density of a value k from a Poisson distribution with a maximum kmax. rdpois draws random numbers from a conditional Poisson distribution.

Usage

cdpois(k, lambda, kmax, log = TRUE)

rdpois(n, lambda, kmax, ...)

Arguments

Value

A numeric value representing the probability (or log-probability) of observing 'k' under the truncated Poisson distribution, or a vector of random values drawn from a truncated Poisson distribution.

Examples

cdpois(10,1,10)
cdpois(11,1,10)
rdpois(5,10,10)

Combine mcmc chains

Description

Combine mcmc chains

Usage

combine.chains(chain.list, thin = 1, burnin.prop = 0)

Arguments

Value

A combined bayouMCMC chain

Simulates data from bayou models

Description

This function simulates data for a given set of parameter values.

Usage

dataSim(
  pars,
  model,
  tree,
  map.type = "pars",
  SE = 0,
  phenogram = TRUE,
  verbose = TRUE,
  ...
)

Arguments

Details

dataSim Simulates data for a given bayou model and parameter set

Value

A list with the following components:

W

Weight matrix based on the OU model and simulated shifts.

E.th

Expected trait values for each tip given the parameter set.

dat

A named vector of simulated continuous trait values.

Half cauchy distribution taken from the R package LaplacesDemon (Hall, 2012).

Description

dhalfcauchy returns the probability density for a half-Cauchy distribution

Usage

dhalfcauchy(x, scale = 25, log = FALSE)

phalfcauchy(q, scale = 25)

qhalfcauchy(p, scale = 25)

rhalfcauchy(n, scale = 25)

Arguments

Value

A numeric vector:

If 'log = FALSE'

The probability density of 'x' under the half-Cauchy distribution.

If 'log = TRUE'

The logarithm of the probability density.

Probability density function for the location of the shift along the branch

Description

Since unequal probabilities are incorporated in calculating the density via dsb, all branches are assumed to be of unit length. Thus, the dloc function simply returns 0 if log=TRUE and 1 if log=FALSE.

Usage

dloc(loc, min = 0, max = 1, log = TRUE)

rloc(k, min = 0, max = 1)

Arguments

Details

dloc calculates the probability of a shift occuring at a given location along the branch assuming a uniform distribution of unit length rloc randomly generates the location of a shift along the branch

Value

A numeric vector:

If 'log = TRUE'

Returns '0', as the log probability of a uniform distribution is always 'log(1) = 0'.

If 'log = FALSE'

Returns '1', indicating a uniform probability distribution along the branch.

Probability density functions for bayou

Description

This function provides a means to specify the prior for the location of shifts across the phylogeny. Certain combinations are not allowed. For example, a maximum shift number of Inf on one branch cannot be combined with a maximum shift number of 1 on another. Thus, bmax must be either a vector of 0's and Inf's or a vector of 0's and 1's. Also, if bmax == 1, then all probabilities must be equal, as bayou cannot sample unequal probabilities without replacement.

Usage

dsb(sb, ntips = ntips, bmax = 1, prob = 1, log = TRUE)

rsb(k, ntips = ntips, bmax = 1, prob = 1, log = TRUE)

Arguments

Details

dsb calculates the probability of a particular arrangement of shifts for a given set of assumptions.

Value

The log density of the particular number and arrangement of shifts.

Examples

n=10
tree <- sim.bdtree(n=n)
tree <- reorder(tree, "postorder")
nbranch <- 2*n-2
sb <- c(1,2, 2, 3)

# Allow any number of shifts on each branch, with probability
# proportional to branch length
dsb(sb, ntips=n, bmax=Inf, prob=tree$edge.length)

# Disallow shifts on the first branch, returns -Inf because sb[1] = 1
dsb(sb, ntips=n, bmax=c(0, rep(1, nbranch-1)), prob=tree$edge.length)

# Set maximum number of shifts to 1, returns -Inf because two shifts
# are on branch 2
dsb(sb, ntips=n, bmax=1, prob=1)

# Generate a random set of k branches
rsb(5, ntips=n, bmax=Inf, prob=tree$edge.length)

Calculate Gelman's R statistic

Description

Calculate Gelman's R statistic

Usage

gelman.R(parameter, chain1, chain2, freq = 20, start = 1, plot = TRUE, ...)

Arguments

Value

A data frame with two columns giving the R statistic and its 95 percent upper CI.

Identify shifts on branches of a phylogenetic tree

Description

This is a convenience function for mapping regimes interactively on the phylogeny. The method locates the nearest branch to where the cursor is clicked on the plot and records the branch number and the location selected on the branch.

Usage

identifyBranches(tree, n, fixed.loc = TRUE, plot.simmap = TRUE)

Arguments

Details

identifyBranches opens an interactive phylogeny plot that allows the user to specify the location of shifts in a phylogenetic tree.

Value

Returns a list with elements "sb" which contains the branch numbers of all selected branches with length "n". If "fixed.loc=TRUE", then the list also contains a vector "loc" which contains the location of the selected shifts along the branch.

Loads a bayou object

Description

load.bayou loads a bayouFit object that was created using bayou.mcmc()

Usage

load.bayou(
  bayouFit,
  saveRDS = TRUE,
  file = NULL,
  cleanup = FALSE,
  ref = FALSE,
  verbose = TRUE
)

Arguments

Details

If both save.Rdata is FALSE and cleanup is TRUE, then load.bayou will trigger a warning and ask for confirmation. In this case, if the results of load.bayou() are not stored in an object, the results of the MCMC run will be permanently deleted.

Value

A list of class '"bayouMCMC"' containing:

gen

A numeric vector of sampled MCMC generations.

lnL

A numeric vector of log-likelihood values.

prior

A numeric vector of prior probabilities.

sb

A list of shift locations sampled across the MCMC chain.

loc

A list of relative shift locations on branches.

t2

A list of new optima values after shifts.

rjpars

A list of reversible-jump parameters sampled at each step.

(Other parameters)

Additional parameters specific to the model used in 'bayou.mcmc()'.

Makes a power posterior function in bayou

Description

This function generates a power posterior function for estimation of marginal likelihood using the stepping stone method

Usage

make.powerposteriorFn(Bk, priorFn, refFn, model)

Arguments

Details

For use in stepping stone estimation of the marginal likelihood using the method of Fan et al. (2011).

Value

A function of class "powerposteriorFn" that returns a list of four values: result (the log density of the power posterior), lik (the log likelihood), prior (the log prior), ref the log reference density.

Make a prior function for bayou

Description

This function generates a prior function to be used for bayou according to user specifications.

Usage

make.prior(
  tree,
  dists = list(),
  param = list(),
  fixed = list(),
  plot.prior = TRUE,
  model = "OU"
)

Arguments

Details

Default distributions and parameter values are given as follows: OU: list(dists=list("dalpha"="dlnorm","dsig2"="dlnorm", "dk"="cdpois","dtheta"="dnorm","dsb"="dsb","dloc"="dunif"), param=list("dalpha"=list(),"dsig2"=list(),"dtheta"=list(), "dk"=list(lambda=1,kmax=2*ntips-2),"dloc"=list(min=0,max=1),"dsb"=list())) QG: list(dists=list("dh2"="dbeta","dP"="dlnorm","dw2"="dlnorm","dNe"="dlnorm", "dk"="cdpois","dtheta"="dnorm","dsb"="dsb","dloc"="dunif"), param=list("dh2"=list(shape1=1,shape2=1),"dP"=list(),"dw2"=list(),"dNe"=list(),"dtheta"=list(), "dk"=list(lambda=1,kmax=2*ntips-2),"dloc"=list(min=0,max=1),"dsb"=list())) OUrepar: list(dists=list("dhalflife"="dlnorm","dVy"="dlnorm", "dk"="cdpois","dtheta"="dnorm","dsb"="dsb","dloc"="dunif"), param=list("dhalflife"=list("meanlog"=0.25,"sdlog"=1.5),"dVy"=list("meanlog"=1,"sdlog"=2), "dk"=list(lambda=1,kmax=2*ntips-2),"dtheta"=list(),"dloc"=list(min=0,max=1)),"dsb"=list())

dalpha, dsig2, dh2, dP, dw2, dNe, dhalflife, and dVy must be positive continuous distributions and provide the parameters used to calculate alpha and sigma^2 of the OU model. dtheta must be continuous and describes the prior distribution of the optima. dk is the prior distribution for the number of shifts. For Poisson and conditional Poisson (cdpois) are provided the parameter lambda, which provides the total number of shifts expected on the tree (not the rate per unit branch length). Otherwise, dk can take any positive, discrete distribution. dsb indicates the prior probability of a given set of branches having shifts, and is generally specified by the "dsb" function in the bayou package. See the documentation for dsb for specifying the number of shifts allowed per branch, the probability of a branch having a shift, and specifying constraints on where shifts can occur."dloc" indicates the prior probability of the location of a shift within a single branch. Currently, all locations are given uniform density. All distributions are set to return log-transformed probability densities.

Value

returns a prior function of class "priorFn" that calculates the log prior density for a set of parameter values provided in a list with correctly named values.

Examples

## Load data
data(chelonia)
tree <- chelonia$phy
dat <- chelonia$dat

#Create a prior that allows only one shift per branch with equal probability 
#across branches
prior <- make.prior(tree, dists=list(dalpha="dlnorm", dsig2="dlnorm",
           dsb="dsb", dk="cdpois", dtheta="dnorm"), 
             param=list(dalpha=list(meanlog=-5, sdlog=2), 
               dsig2=list(meanlog=-1, sdlog=5), dk=list(lambda=15, kmax=200), 
                 dsb=list(bmax=1,prob=1), dtheta=list(mean=mean(dat), sd=2)))
             
#Evaluate some parameter sets
pars1 <- list(alpha=0.1, sig2=0.1, k=5, ntheta=6, theta=rnorm(6, mean(dat), 2), 
                 sb=c(32, 53, 110, 350, 439), loc=rep(0.1, 5), t2=2:6)
pars2 <- list(alpha=0.1, sig2=0.1, k=5, ntheta=6, theta=rnorm(6, mean(dat), 2),
                 sb=c(43, 43, 432, 20, 448), loc=rep(0.1, 5), t2=2:6)
prior(pars1) 
prior(pars2) #-Inf because two shifts on one branch

#Create a prior that allows any number of shifts along each branch with probability proportional 
#to branch length
prior <- make.prior(tree, dists=list(dalpha="dlnorm", dsig2="dlnorm",
           dsb="dsb", dk="cdpois", dtheta="dnorm"), 
             param=list(dalpha=list(meanlog=-5, sdlog=2), 
               dsig2=list(meanlog=-1, sdlog=5), dk=list(lambda=15, kmax=200), 
                 dsb=list(bmax=Inf,prob=tree$edge.length), 
                   dtheta=list(mean=mean(dat), sd=2)))
prior(pars1)
prior(pars2)

#Create a prior with fixed regime placement and sigma^2 value
prior <- make.prior(tree, dists=list(dalpha="dlnorm", dsig2="fixed", 
           dsb="fixed", dk="fixed", dtheta="dnorm", dloc="dunif"), 
             param=list(dalpha=list(meanlog=-5, sdlog=2), 
               dtheta=list(mean=mean(dat), sd=2)), 
                 fixed=list(sig2=1, k=3, ntheta=4, sb=c(447, 396, 29)))
                 
pars3 <- list(alpha=0.01, theta=rnorm(4, mean(dat), 2), loc=rep(0.1, 4))
prior(pars3)

##Return a list of functions used to calculate prior
attributes(prior)$functions

##Return parameter values used in prior distribution
attributes(prior)$parameters

Make a reference function in bayou

Description

This function generates a reference function from a mcmc chain for use in marginal likelihood estimation.

Usage

make.refFn(chain, model, priorFn, burnin = 0.3, plot = TRUE)

Arguments

Details

Distributions are fit to each mcmc chain and the best-fitting distribution is chosen as the reference distribution for that parameter using the method of Fan et al. (2011). For positive continuous parameters alpha, sigma^2, halflife, Vy, w2, Ne, Log-normal, exponential, gamma and weibull distributions are fit. For continuous distributions theta, Normal, Cauchy and Logistic distributions are fit. For discrete distributions, k, negative binomial, poisson and geometric distributions are fit. Best-fitting distributions are determined by AIC.

Value

Returns a reference function of class "refFn" that takes a parameter list and returns the log density given the reference distribution. If plot=TRUE, a plot is produced showing the density of variable parameters and the fitted distribution from the reference function (in red).

This function makes a bayou model object that can be used for customized allometric regression models.

Description

This function makes a bayou model object that can be used for customized allometric regression models.

Usage

makeBayouModel(
  f,
  rjpars,
  tree,
  dat,
  pred,
  prior,
  SE = 0,
  slopechange = "immediate",
  impute = NULL,
  startpar = NULL,
  moves = NULL,
  control.weights = NULL,
  D = NULL,
  shiftpars = c("sb", "loc", "t2"),
  model = "OU"
)

Arguments

Details

This function generates a list with the '$model', which provides the specifications of the regression model and '$startpar', which provides starting values to input into bayou.makeMCMC. Note that this model assumes that predictors immediately affect trait values at a shift. In other words, regardless of the past history of the predictor, only the current value affects the current expected trait value. This is only reasonable for allometric models, although it may be appropriate for other models if phylogenetic inertia is very low (short half-lives).

One predictor variable may include missing data (coded as "NA"). The model will assume the maximum-likelihood best-fit BM model and simulate the missing predictor values throughout the course of the MCMC. These values will then be used to calculate the likelihood given the parameters for each MCMC step.

Value

A list with two elements:

model

A list containing MCMC settings, likelihood functions, monitoring functions, and other parameters for the Bayesian OU model.

startpar

A list of starting parameter values for the model. If missing data is imputed, it includes 'missing.pred'.

Make a color transparent (Taken from an answer on StackOverflow by Nick Sabbe)

Description

Make a color transparent (Taken from an answer on StackOverflow by Nick Sabbe)

Usage

makeTransparent(someColor, alpha = 100)

Arguments

Value

A character vector of colors in hexadecimal format with the specified transparency applied.

Bayou Models

Description

Default bayou models. New models may be specified by providing a set of moves, control weights, tuning parameters, parameter names, RJ parameters and a likelihood function.

Usage

model.OU

Format

An object of class list of length 9.

Calculate the weight matrix of a set of regimes on a phylogeny

Description

These functions calculate weight matrices from regimes specified by a bayou formatted parameter list parmap.W calculates the weight matrix for a set of regimes from a phylogeny with a stored regime history. .parmap.W calculates the same matrix, but without checks and is generally run internally.

Usage

parmap.W(tree, pars)

Arguments

Details

.parmap.W is more computationally efficient within a mcmc and is used internally.

Value

A matrix where rows correspond to branches in the phylogenetic tree, and columns correspond to the different evolutionary regimes. Each entry in the matrix represents the weight of a given regime on a given branch.

Convert a bayou parameter list into a simmap formatted phylogeny

Description

This function converts a bayou formatted parameter list specifying regime locations into a simmap formatted tree that can be plotted using plotSimmap from phytools or the plotRegimes function from bayou.

Usage

pars2simmap(pars, tree)

Arguments

Details

pars2simmap takes a list of parameters and converts it to simmap format

Value

A list with elements: tree A simmap formatted tree, pars bayou formatted parameter list, and cols A named vector of colors.

Examples

tree <- reorder(sim.bdtree(n=100), "postorder")

pars <- list(k=5, sb=c(195, 196, 184, 138, 153), loc=rep(0, 5), t2=2:6)
tr <- pars2simmap(pars, tree)
plotRegimes(tr$tree, col=tr$col)

Plot a pheongram with the posterior density for optima values

Description

Plots a phenogram and the posterior density for optima values

Usage

phenogram.density(
  tree,
  dat,
  burnin = 0,
  chain,
  colors = NULL,
  pp.cutoff = NULL,
  K = NULL,
  ...
)

Arguments

Value

No return value, called for side effects. This function generates a phenogram plot with posterior density overlays for optima values, visualizing the distribution of evolutionary regimes across a phylogenetic tree.

S3 method for plotting bayouMCMC objects

Description

S3 method for plotting bayouMCMC objects

Usage

## S3 method for class 'bayouMCMC'
plot(x, ...)

Arguments

Value

No return value, called for side effects. This function generates diagnostic trace and density plots for MCMC chains of class 'bayouMCMC' to assess convergence and parameter distributions.

S3 method for plotting ssMCMC objects

Description

S3 method for plotting ssMCMC objects

Usage

## S3 method for class 'ssMCMC'
plot(x, ...)

Arguments

Details

Produces 4 plots. The first 3 plot the prior, reference function and likelihood. Different colors indicate different power posteriors for each. These chains should appear to be well mixed. The final plot shows the sum of the marginal likelihood across each of the steps in the stepping stone algorithm.

Value

No return value, called for side effects. This function generates diagnostic plots for an 'ssMCMC' object, including log-likelihood, log-prior, log-reference function, and cumulative marginal likelihood across power posteriors.

Plot parameter list as a simmap tree

Description

Plot parameter list as a simmap tree

Usage

plotBayoupars(pars, tree, ...)

Arguments

Value

No return value, called for side effects. This function generates a visualization of a phylogenetic tree with mapped regimes or other parameters.

A function to plot a heatmap of reconstructed parameter values on the branches of the tree

Description

A function to plot a heatmap of reconstructed parameter values on the branches of the tree

Usage

plotBranchHeatMap(
  tree,
  chain,
  variable,
  burnin = 0,
  nn = NULL,
  pal = heat.colors,
  legend_ticks = NULL,
  legend_settings = list(plot = TRUE),
  ...
)

Arguments

Details

legend_settings is an optional list of any of the following:

legend - a logical indicating whether a legend should be plotted

x - the x location of the legend

y - the y location of the legend

height - the height of the legend

width - the width of the legend

n - the number of gradations in color to plot from the palette

adjx - an x adjustment for placing text next to the legend bar

cex.lab - the size of text labels next to the legend bar

text.col - The color of text labels

locator - if TRUE, then x and y coordinates are ignored and legend is placed interactively.

Value

No return value, called for side effects. This function generates a heatmap visualization of reconstructed parameter values on a phylogenetic tree.

A function to visualize a multi-optimum OU process evolving on a phylogeny

Description

A function to visualize a multi-optimum OU process evolving on a phylogeny

Usage

plotOUtreesim(pars, tree, ptsperunit = 100, pal = rainbow, aph = 255, lwd = 1)

Arguments

Value

**No return value**, called for **side effects**. The function **generates a plot** showing the simulated trait evolution over time.

Function to plot the regimes from a simmap tree

Description

Function to plot the regimes from a simmap tree

Usage

plotRegimes(tree, col = NULL, lwd = 1, pal = rainbow, ...)

Arguments

Details

This function uses plot.phylo to generate coordinates and plot the tree, but plots the 'maps' element of phytools' simmap format. This provides much of the functionality of plot.phylo from the ape package. Currently, only types 'phylogram', 'unrooted', 'radial', and 'cladogram' are allowed. Phylogenies must have branch lengths.

Value

**No return value**, called for **side effects**. The function **generates a plot** of the phylogenetic tree with color-coded regimes.

A function to plot a list produced by shiftSummaries

Description

A function to plot a list produced by shiftSummaries

Usage

plotShiftSummaries(
  summaries,
  pal = rainbow,
  ask = FALSE,
  single.plot = FALSE,
  label.pts = TRUE,
  ...
)

Arguments

Details

For each shift, this function plots the taxa on the phylogeny that are (usually) in this regime (each taxon is assigned to the specified shifts, thus some descendent taxa may not always be in indicated regime if the shift if they are sometimes in another tipward shift with low posterior probability). The function then plots the distribution of phenotypic states and the predicted regression line, as well as density plots for the intercept and any regression coefficients in the model.

Value

**No return value**, called for **side effects**. The function **generates visualizations** of shift summaries, including:

• **Phylogenetic tree with shift locations**

• **Scatter plots of phenotype data**

• **Density plots for regression coefficients**

Plot a phylogenetic tree with posterior probabilities from a bayouMCMC chain (function adapted from phytools' plotSimmap)

Description

Plot a phylogenetic tree with posterior probabilities from a bayouMCMC chain (function adapted from phytools' plotSimmap)

Usage

plotSimmap.mcmc(
  chain,
  burnin = NULL,
  lwd = 1,
  edge.type = c("regimes", "theta", "none", "pp"),
  pal = rainbow,
  pp.cutoff = 0.3,
  circles = TRUE,
  circle.cex.max = 3,
  circle.col = "red",
  circle.pch = 21,
  circle.lwd = 0.75,
  circle.alpha = 100,
  pp.labels = FALSE,
  pp.col = 1,
  pp.alpha = 255,
  pp.cex = 0.75,
  edge.color = 1,
  parameter.sample = 1000,
  ...
)

Arguments

Value

**No return value**, called for **side effects**. The function generates a **phylogenetic tree visualization** with branches colored based on posterior probabilities, regimes, or estimated parameters.

S3 method for printing bayouFit objects

Description

S3 method for printing bayouFit objects

Usage

## S3 method for class 'bayouFit'
print(x, ...)

Arguments

Value

**No return value**, called for **side effects**. This function prints a summary of a **bayou model fit**, including posterior probabilities, model parameters, and key statistics.

S3 method for printing bayouMCMC objects

Description

S3 method for printing bayouMCMC objects

Usage

## S3 method for class 'bayouMCMC'
print(x, ...)

Arguments

Value

**No return value**, called for **side effects**. This function prints a summary of a **bayouMCMC** object, including details about the MCMC run, burn-in proportion, and key parameter statistics.

S3 method for printing priorFn objects

Description

S3 method for printing priorFn objects

Usage

## S3 method for class 'priorFn'
print(x, ...)

Arguments

Value

**No return value**, called for **side effects**. This function prints a summary of a **priorFn** object, including the expected model, required parameter list, and prior distribution functions.

S3 method for printing refFn objects

Description

S3 method for printing refFn objects

Usage

## S3 method for class 'refFn'
print(x, ...)

Arguments

Value

**No return value**, called for **side effects**. This function prints a summary of a **refFn** object, including the expected model, required parameter list, and reference distribution functions.

S3 method for printing ssMCMC objects

Description

S3 method for printing ssMCMC objects

Usage

## S3 method for class 'ssMCMC'
print(x, ...)

Arguments

Value

**No return value**, called for **side effects**. This function prints a summary of an 'ssMCMC' object, including the estimated marginal likelihood, the number of power posteriors run, and the log marginal likelihood contributions across steps.

Simulates parameters from bayou models

Description

priorSim Simulates parameters from the prior distribution specified by make.prior

Usage

priorSim(prior, tree, plot = TRUE, nsim = 1, shiftpars = "theta", ...)

Arguments

Value

A list of bayou parameter lists drawn from the prior.

Utility function for retrieving parameters from an MCMC chain

Description

Utility function for retrieving parameters from an MCMC chain

Usage

pull.pars(i, chain, model = "OU")

Arguments

Value

A bayou formatted parameter list

Adds visualization of regimes to a plot

Description

Adds visualization of regimes to a plot

Usage

regime.plot(pars, tree, cols, type = "rect", transparency = 100)

Arguments

Set the burnin proportion for bayouMCMC objects

Description

Set the burnin proportion for bayouMCMC objects

Usage

set.burnin(chain, burnin = 0.3)

Arguments

Value

A bayouMCMC chain or ssMCMC chain with burnin proportion stored in the attributes.

A function for summarizing the state of a model after a shift

Description

A function for summarizing the state of a model after a shift

Usage

shiftSummaries(chain, mcmc, pp.cutoff = 0.3, branches = NULL)

Arguments

Details

shiftSummaries summarizes the immediate parameter values after a shift on a particular branch. Parameters are summarized only for the duration that the particular shift exists. Thus, even global parameters will be different for particular shifts.

Value

A list with elements: pars = a bayoupars list giving the location of shifts specified; tree = The tree; pred = Predictor variable matrix; dat = A vector of the data; SE = A vector of standard errors; PP = Posterior probabilities of the specified shifts; model = A list specifying the model used; variables = The variables summarized; cladesummaries = A list providing the medians and densities of the distributions of regression variables for each shift; descendents = A list providing the taxa that belong to each regime regressions = A matrix providing the regression coefficients for each regime.

Calculate the weight matrix of a set of regimes on a phylogeny

Description

These functions calculate weight matrices from regimes specified in phytools' simmap format. simmapW calculates the weight matrix for a set of regimes from a phylogeny with a stored regime history. .simmap.W calculates the same matrix, but without checks and is generally run internally.

Usage

simmapW(tree, pars)

Arguments

Details

.simmap.W is more computationally efficient within a mcmc and is used internally. The value of TotExp is supplied to speed computation and reduce redundancy, and cache objects must be supplied as the phylogeny, and the parameter ntheta must be present in the list pars.

Value

A matrix where each row corresponds to a branch in the phylogenetic tree, and each column represents an evolutionary regime. The entries in the matrix indicate the weight of a given regime on a given branch.

S3 method for summarizing bayouMCMC objects

Description

S3 method for summarizing bayouMCMC objects

Usage

## S3 method for class 'bayouMCMC'
summary(object, ...)

Arguments

Value

An invisible list with two elements: statistics which provides summary statistics for a bayouMCMC chain, and branch.posteriors which summarizes branch specific data from a bayouMCMC chain.