Package 'POUMM' reference manual

Title:	The Phylogenetic Ornstein-Uhlenbeck Mixed Model
Description:	The Phylogenetic Ornstein-Uhlenbeck Mixed Model (POUMM) allows to estimate the phylogenetic heritability of continuous traits, to test hypotheses of neutral evolution versus stabilizing selection, to quantify the strength of stabilizing selection, to estimate measurement error and to make predictions about the evolution of a phenotype and phenotypic variation in a population. The package implements combined maximum likelihood and Bayesian inference of the univariate Phylogenetic Ornstein-Uhlenbeck Mixed Model, fast parallel likelihood calculation, maximum likelihood inference of the genotypic values at the tips, functions for summarizing and plotting traces and posterior samples, functions for simulation of a univariate continuous trait evolution model along a phylogenetic tree. So far, the package has been used for estimating the heritability of quantitative traits in macroevolutionary and epidemiological studies, see e.g. Bertels et al. (2017) <doi:10.1093/molbev/msx246> and Mitov and Stadler (2018) <doi:10.1093/molbev/msx328>. The algorithm for parallel POUMM likelihood calculation has been published in Mitov and Stadler (2019) <doi:10.1111/2041-210X.13136>.
Authors:	Venelin Mitov [aut, cre, cph] (<a href="https://venelin.github.io">venelin.github.io</a>), Matt Dowle [ctb]
Maintainer:	Venelin Mitov <[email protected]>
License:	GPL (>= 3.0)
Version:	2.1.7
Built:	2025-02-23 05:18:46 UTC
Source:	https://github.com/venelin/poumm

Extract data from an MCMC chain This is an internal function.

Description

Extract data from an MCMC chain This is an internal function.

Usage

analyseMCMCs(
  chains,
  stat = NULL,
  statName = "logpost",
  start,
  end,
  thinMCMC,
  as.dt = FALSE,
  k = NA,
  N = NA,
  ...
)
analyseMCMCs(
  chains,
  stat = NULL,
  statName = "logpost",
  start,
  end,
  thinMCMC,
  as.dt = FALSE,
  k = NA,
  N = NA,
  ...
)

Arguments

chains, stat, statName, start, end, thinMCMC, as.dt, k, N, ...

internal use.

Node indices of the direct descendants of n in the phylogeny.

Description

Node indices of the direct descendants of n in the phylogeny.

Usage

chld(tree, n)
chld(tree, n)

Arguments

`tree`	an object of class phylo
`n`	an index of a node (root, internal or tip) in tree

Value

An integer vector.

Extract maximum likelihood fitted parameters (coefficients) from a fitted POUMM model.

Description

Extract maximum likelihood fitted parameters (coefficients) from a fitted POUMM model.

Usage

## S3 method for class 'POUMM'
coef(object, mapped = FALSE, ...)
## S3 method for class 'POUMM'
coef(object, mapped = FALSE, ...)

Arguments

`object`	An object of class POUMM.
`mapped`	Logical indicating whether the standard POUMM parameters should also be extracted.
`...`	Not used; added for compatibility with generic function coef.

Details

The parameters extracted are the ones used as input to the model's parMapping function.

Value

A named vector with the fitted parameters of the model.

A vectorized expected covariance function for a given tree and a fitted POUMM model

Description

A vectorized expected covariance function for a given tree and a fitted POUMM model

Usage

covFunPOUMM(object, corr = FALSE)
covFunPOUMM(object, corr = FALSE)

Arguments

`object`	an S3 object of class POUMM
`corr`	logical indicating if an expected correlation function should be returned For non-ultrametric trees, usually the mean root-tip distance is used.

Value

a function of three numerical parameters: tau - the phylogenetic distance between a two tips; tanc - the distance from the root to their most recent common ancestor. t - the root-tip distance (assuming that the two tips are at equal distance from the root)

A vectorized function returning HPD intervals of the expected covariance for a given tree and a fitted POUMM model

Description

A vectorized function returning HPD intervals of the expected covariance for a given tree and a fitted POUMM model

Usage

covHPDFunPOUMM(object, prob = 0.95, corr = FALSE, ...)
covHPDFunPOUMM(object, prob = 0.95, corr = FALSE, ...)

Arguments

`object`	an S3 object of class POUMM
`prob`	a Numerical between 0 and 1
`corr`	logical indicating if an expected correlation HPD interval function should be returned.
`...`	additional parameters passed to summary.POUMM

Value

a function of a numerica matrix x with 3 columns corresponding to tau, tanc and t (see covFunPOUMM). The function reteruns a two-colun matrix with the lower and upper limit of the HPD for each row in the input matrix.

Expected covariance of two tips at given root-tip time and phylogenetic distance

Description

Expected covariance of two tips at given root-tip time and phylogenetic distance

Usage

covPOUMM(
  alpha,
  sigma,
  sigmae,
  t,
  tau,
  tanc = t - tau/2,
  corr = FALSE,
  as.matrix = FALSE
)
covPOUMM(
  alpha,
  sigma,
  sigmae,
  t,
  tau,
  tanc = t - tau/2,
  corr = FALSE,
  as.matrix = FALSE
)

Arguments

`alpha`, `sigma`, `sigmae`	POUMM parameters
`t`	A non-negative number or vector time from the beginning of the POUMM process (root-tip distance). If a vector, the evaluation is done on each couple (row) from cbind(t, tau).
`tau`	A non-negative number or vector indicating the phylogenetic distance between two tips, each of them located at time t from the root. If a vector, the evaluation is done on each couple (row) from cbind(t, tau).
`tanc`	A non-negative number or vector indication the root-mrca distance for a couple of tips. Defaults to t-tau/2 corresponding to an ultrametric tree.
`corr`	Logical indicating whether correlation should be returned instead of covariance.
`as.matrix`	Logical indicating if a variance-covariance matrix should be returned.

Details

The function assumes that the two tips are at equal distance t from the root. This implies that the root-tip distance of their mrca is t - tau/2.

Value

If as.matrix == FALSE, a number. Otherwise a two by two symmetric matrix. If t or tau is a vector of length bigger than 1, then a vector of numbers or a list of matrices.

Variance covariance matrix of the values at the tips of a tree under an OU process

Description

Variance covariance matrix of the values at the tips of a tree under an OU process

Usage

covVTipsGivenTreePOUMM(
  tree,
  alpha = 0,
  sigma = 1,
  sigmae = 0,
  tanc = NULL,
  tauij = NULL,
  corr = FALSE
)
covVTipsGivenTreePOUMM(
  tree,
  alpha = 0,
  sigma = 1,
  sigmae = 0,
  tanc = NULL,
  tauij = NULL,
  corr = FALSE
)

Arguments

`tree`	A phylo object.
`alpha`, `sigma`	Non-negative numeric values, parameters of the OU process.
`sigmae`	Non-negative numeric value, environmental standard deviation at the tips.
`tanc`	Numerical matrix with the time-distance from the root of the tree to the mrca of each tip-couple. If NULL it will be calculated.
`tauij`	Numerical matrix with the time (patristic) distance between each pair of tips. If NULL, it will be calculated.
`corr`	Logical indicating if a correlation matrix shall be returned.

Value

a variance covariance or a correlation matrix of the tips in tree.

References

(Hansen 1997) Stabilizing selection and the comparative analysis of adaptation.

Multivariate density of observed values along a tree given an OU process of evolution and root-value

Description

Calculates the conditional probability density of observed values at the tips and internal nodes of a tree, given that tree, the value at the root, z[N+1], where N is the number of tips in the tree, known measurement error e for each value in z, and a POUMM model of evolution. This function is mostly used to calculate the likelihood of simulated data under known model parameters.

Usage

dVNodesGivenTreePOUMM(
  z,
  tree,
  alpha,
  theta,
  sigma,
  sigmae = 0,
  e = rep(0, length(z)),
  log = TRUE
)
dVNodesGivenTreePOUMM(
  z,
  tree,
  alpha,
  theta,
  sigma,
  sigmae = 0,
  e = rep(0, length(z)),
  log = TRUE
)

Arguments

`z`	A numeric vector of size length(tree$tip.label)+tree$Nnode representing the observed values at the tips, root and internal nodes.
`tree`	An object of class phylo.
`alpha`, `theta`, `sigma`	Numeric values, parameters of the OU model.
`sigmae`	Numeric non-negative value or vector of length(z) elements (default 0). Specifies the standard deviation of random environmental contribution (and eventually measurement error) to be added to the values. Note that if measurement standard error, se, is known and needs to be added to the environmental contribution, the right way to specify the parameter would be sqrt(sigmae^2+se^2), not sigmae+se.
`e`	Numeric vector of size length(z) representing exactly known error (sum of environmental contribution and measurement error). Defaults to a vector of zeroes.
`log`	Logical indicating whether a log-likelihood should be returned instead of a likelihood. Default is TRUE.

Value

A numeric value, the multivariate probability density of z under the given parameters.

Edge indices of the edges in tree starting from n

Description

Edge indices of the edges in tree starting from n

Usage

edgesFrom(tree, n)
edgesFrom(tree, n)

Arguments

`tree`	an object of class phylo
`n`	an index of a node (root, internal or tip) in tree

Value

An integer vector.

Extract maximum likelihood expected genotypic values at the tips of a tree, to which a POUMM model has been previously fitted

Description

Extract maximum likelihood expected genotypic values at the tips of a tree, to which a POUMM model has been previously fitted

Usage

## S3 method for class 'POUMM'
fitted(object, vCov = FALSE, ...)
## S3 method for class 'POUMM'
fitted(object, vCov = FALSE, ...)

Arguments

`object`	An object of class POUMM.
`vCov`	A logical indicating whether a list with the genotypic values and their variance covariance matrix should be returned or only a vector of the genotypic values (default is FALSE).
`...`	Not used; added for compatibility with generic function fitted.

Value

If vCov == TRUE, a list with elements g - the genotypic values and vCov - the variance-covariance matrix of these values for the specific tree, observed values z and POUMM ML-fit. If vCov == FALSE, only the vector of genotypic values corresponding to the tip-labels in the tree is returned.

Distribution of the genotypic values under a POUMM fit

Description

Distribution of the genotypic values under a POUMM fit

Usage

gPOUMM(z, tree, g0, alpha, theta, sigma, sigmae)
gPOUMM(z, tree, g0, alpha, theta, sigma, sigmae)

Arguments

`z`	A numeric vector of size length(tree$tip.label) representing the trait values at the tip-nodes.
`tree`	an object of class phylo
`g0`	A numeric value at the root of the tree, genotypic contribution.
`alpha`, `theta`, `sigma`	Numeric values, parameters of the OU model.
`sigmae`	Numeric non-negative value (default 0). Specifies the standard deviation of random environmental contribution (white noise) included in z.

Value

a list with elements V.g, V.g_1, mu.g, V.e, V.e_1, mu.e, V.g.poumm, mu.g.poumm.

Phylogenetic heritability estimated at time t

Description

Phylogenetic heritability estimated at time t

Usage

H2(alpha, sigma, sigmae, t = Inf, tm = 0)
H2(alpha, sigma, sigmae, t = Inf, tm = 0)

Arguments

`alpha`, `sigma`	numeric, parameters of the OU process acting on the genetic contributions
`sigmae`	numeric, environmental standard deviation
`t`	time from the beginning of the process at which heritability should be calculated, i.e. epidemiologic time
`tm`	average time for within host evolution from getting infected until getting measured or passing on the infection to another host

Density of observed tip-values given a tree, assuming Ornstein-Uhlenbeck process for the genetic contributions along the tree and normally distributed environmental deviations.

Description

Calculates the (log-)probability density of trait values at the tip-nodes given the tree and assuming that the trait value at the tips is the sum of a genetic contribution, g, that evolved on the tree according to an OU process with parameters alpha, theta, sigma and an environmental deviation, e, that is distributed normally and independently between the tips of the tree. Note: Without additional assumptions for the distribution of the value at the root of the tree, the likelihood is not defined at alpha=0, although this corresponds to the limiting Brownian motion process with mean value theta and unit time variance sigma^2. Considering the observed data and tree as a fixed parameter and the POUMM parameters as variables, this function is interpreted as the POUMM likelihood.

Usage

likPOUMMGivenTreeVTips(
  z,
  tree,
  alpha,
  theta,
  sigma,
  sigmae = 0,
  g0 = NA,
  g0Prior = NULL,
  log = TRUE,
  pruneInfo = pruneTree(tree, z),
  usempfr = 0,
  maxmpfr = 2,
  precbits = 128,
  debug = FALSE
)
likPOUMMGivenTreeVTips(
  z,
  tree,
  alpha,
  theta,
  sigma,
  sigmae = 0,
  g0 = NA,
  g0Prior = NULL,
  log = TRUE,
  pruneInfo = pruneTree(tree, z),
  usempfr = 0,
  maxmpfr = 2,
  precbits = 128,
  debug = FALSE
)

Arguments

`z`	A numeric vector of size length(tree$tip.label) representing the trait values at the tip-nodes.
`tree`	an object of class phylo
`alpha`	the strength of the selection
`theta`	long term mean value of the OU process
`sigma`	the unit-time standard deviation of the random component in the OU process.
`sigmae`	the standard deviation of the environmental deviation added to the genetic contribution at each tip, by default 0, meaning no environmental deviation.
`g0`	Numeric, NA or NaN, either a fixed genotypic value at the root of tree or NA or NaN. A NA "Not Available" will cause to analytically calculate the value of g0 that would maximize the conditional likelihood of the data given g0. A NaN "Not a Number" will cause integration over g0 taking values in (-Inf,+Inf) assuming that g0 is normally distributed with mean g0Prior$mean and variance g0Prior$var (see parameter g0Prior).
`g0Prior`	Either NULL or a list with named numeric or character members "mean" and "var". Specifies a prior normal distribution for the parameter g0. If characters, the members mean and var are evaluated as R-expressions.
`log`	Logical indicating whether log-likelihood should be returned instead of likelihood, default is TRUE.
`pruneInfo`	list returned by pruneTree(tree) to be passed in explicit calls to dVGivenTreeOU.
`usempfr`	integer indicating if and how mpfr should be used for small parameter values (any(c(alpha, sigma, sigmae) < 0.01)). Using the mpfr package can be forced by specifying an integer greater or equal to 2. Setting usempfr=0 (default) causes high precision likelihood calculation to be done on each encounter of parameters with at least 1 bigger log-likelihood value than any of the currently found maximum log-likelihood or the previously calculated log-likelihood value Requires the Rmpfr package. Note that using mpfr may increase the time for one likelihood calculation more than 100-fold. Set usempfr to -1 or less to completely disable Rmpfr functionality.
`maxmpfr`	integer (not used)
`precbits`	integer specifying precision bits for mpfr. Default is 512.
`debug`	logical, if set to TRUE some debugging information is printed during likelihood calculation

Value

A numeric with attributes "g0" and "g0LogPrior".

Fast (parallel) POUMM likelihood calculation using the SPLITT library

Description

Fast (log-)likelihood calculation using C++ and OpenMP based parallelization.

Usage

likPOUMMGivenTreeVTipsC(
  integrator,
  alpha,
  theta,
  sigma,
  sigmae,
  g0 = NA,
  g0Prior = NULL,
  log = TRUE
)
likPOUMMGivenTreeVTipsC(
  integrator,
  alpha,
  theta,
  sigma,
  sigmae,
  g0 = NA,
  g0Prior = NULL,
  log = TRUE
)

Arguments

`integrator`	An Rcpp module object from the class POUMM_AbcPOUMM. This object is to be created using the function pruneTree (see example). This object contains the data and tree (arguments z and tree of the equivalent function dVTipsGivenTreeVTips.).
`alpha`	the strength of the selection
`theta`	long term mean value of the OU process
`sigma`	the unit-time standard deviation of the random component in the OU process.
`sigmae`	the standard deviation of the environmental deviation added to the genetic contribution at each tip, by default 0, meaning no environmental deviation.
`g0`	Numeric, NA or NaN, either a fixed genotypic value at the root of tree or NA or NaN. A NA "Not Available" will cause to analytically calculate the value of g0 that would maximize the conditional likelihood of the data given g0. A NaN "Not a Number" will cause integration over g0 taking values in (-Inf,+Inf) assuming that g0 is normally distributed with mean g0Prior$mean and variance g0Prior$var (see parameter g0Prior).
`g0Prior`	Either NULL or a list with named numeric or character members "mean" and "var". Specifies a prior normal distribution for the parameter g0. If characters, the members mean and var are evaluated as R-expressions.
`log`	Logical indicating whether log-likelihood should be returned instead of likelihood, default is TRUE.

Details

This function is the C++ equivalent of dVTipsGivenTreePOUMM (aliased also as likPOUMMGivenTreeVTips). Currently, the function does not support multiple precision floating point operations (supported in dVTipsGivenTreePOUMM). The C++ implementation is based on the library for parallel tree traversal "SPLITT" (https://github.com/venelin/SPLITT.git).

Value

A numeric with attributes "g0" and "g0LogPrior".

References

Mitov, V., and Stadler, T. (2017). Fast and Robust Inference of Phylogenetic Ornstein-Uhlenbeck Models Using Parallel Likelihood Calculation. bioRxiv, 115089. https://doi.org/10.1101/115089 Mitov, V., & Stadler, T. (2017). Fast Bayesian Inference of Phylogenetic Models Using Parallel Likelihood Calculation and Adaptive Metropolis Sampling. Systematic Biology, 235739. http://doi.org/10.1101/235739

Examples

## Not run: 
N <- 100
tr <- ape::rtree(N)
z <- rVNodesGivenTreePOUMM(tr, 0, 2, 3, 1, 1)[1:N]
pruneInfo <- pruneTree(tr, z)
microbenchmark::microbenchmark(
  likCpp <- likPOUMMGivenTreeVTipsC(pruneInfo$integrator, 2, 3, 1, 1),
  likR <- likPOUMMGivenTreeVTips(z, tr, 2, 3, 1, 1, pruneInfo = pruneInfo))

# should be the same values
likCpp
likR

## End(Not run)

## Not run: 
N <- 100
tr <- ape::rtree(N)
z <- rVNodesGivenTreePOUMM(tr, 0, 2, 3, 1, 1)[1:N]
pruneInfo <- pruneTree(tr, z)
microbenchmark::microbenchmark(
  likCpp <- likPOUMMGivenTreeVTipsC(pruneInfo$integrator, 2, 3, 1, 1),
  likR <- likPOUMMGivenTreeVTips(z, tr, 2, 3, 1, 1, pruneInfo = pruneInfo))

# should be the same values
likCpp
likR

## End(Not run)

Processing of the root value and calculation of the maximum log-likelihood for the given coefficients abc, and parameters theta, g0 and g0Prior. This is an internal function.

Description

Processing of the root value and calculation of the maximum log-likelihood for the given coefficients abc, and parameters theta, g0 and g0Prior. This is an internal function.

Usage

loglik_abc_g0_g0Prior(abc, alpha, theta, sigma, g0, g0Prior)
loglik_abc_g0_g0Prior(abc, alpha, theta, sigma, g0, g0Prior)

Arguments

`abc`	a vector of 3 numerical values denoting the corresponding coefficients in the POUMM likelihood presented as exp(a g0^2 + b g0 + c).
`alpha`, `theta`, `sigma`	parameters of the OU-process.
`g0`	initial value at the root of the tree (can be NA). See argument parMapping in ?specifyPOUMM.
`g0Prior`	list object. See parameter g0Prior in ?specifyPOUMM.

Extract maximum likelihood and degrees of freedom from a fitted POUMM model

Description

Extract maximum likelihood and degrees of freedom from a fitted POUMM model

Usage

## S3 method for class 'POUMM'
logLik(object, ...)
## S3 method for class 'POUMM'
logLik(object, ...)

Arguments

`object`	An object of class POUMM.
`...`	not used; included for compliance with generic function logLik.

Find a maximum likelihood fit of the POUMM model

Description

Find a maximum likelihood fit of the POUMM model

Usage

maxLikPOUMMGivenTreeVTips(
  loglik,
  pruneInfo,
  parLower,
  parUpper,
  parInitML = NULL,
  control = list(factr = 1e+08, fnscale = -1),
  verbose = FALSE,
  debug = FALSE,
  ...
)
maxLikPOUMMGivenTreeVTips(
  loglik,
  pruneInfo,
  parLower,
  parUpper,
  parInitML = NULL,
  control = list(factr = 1e+08, fnscale = -1),
  verbose = FALSE,
  debug = FALSE,
  ...
)

Arguments

`loglik`	function(par, memo, parFixedNoAlpha)
`pruneInfo`	a list-object returned by the pruneTree(tree, z) function.
`parLower`, `parUpper`	Two named numeric vectors indicating the boundaries of the search region. Default values are parLower = c(alpha = 0, theta = 0, sigma = 0, sigmae = 0) and parUpper = c(alpha = 100, theta = 10, sigma = 20, sigmae = 10).
`parInitML`	A named vector (like parLower and parUpper) or a list of such vectors - starting points for optim.
`control`	List of parameters passed on to optim, default list(factr = 1e8, fnscale = -1), see ?optim.
`verbose`	A logical indicating whether to print informative messages on the standard output.
`debug`	A logical indicating whether to print debug messages (currently not implemented).
`...`	currently not used.

Value

a list containing an element par and an element value as well as the parameters passed

MCMC-sampling from a posterior distribution of a P(OU)MM model given tree, values at the tips and a prior distribution

Description

MCMC-sampling from a posterior distribution of a P(OU)MM model given tree, values at the tips and a prior distribution

Usage

mcmcPOUMMGivenPriorTreeVTips(
  loglik,
  fitML = NULL,
  parMapping,
  parInitMCMC,
  parPriorMCMC,
  parScaleMCMC,
  nSamplesMCMC,
  nAdaptMCMC,
  thinMCMC,
  accRateMCMC,
  gammaMCMC,
  nChainsMCMC,
  samplePriorMCMC,
  pruneInfo,
  ...,
  verbose = FALSE,
  parallelMCMC = FALSE
)
mcmcPOUMMGivenPriorTreeVTips(
  loglik,
  fitML = NULL,
  parMapping,
  parInitMCMC,
  parPriorMCMC,
  parScaleMCMC,
  nSamplesMCMC,
  nAdaptMCMC,
  thinMCMC,
  accRateMCMC,
  gammaMCMC,
  nChainsMCMC,
  samplePriorMCMC,
  pruneInfo,
  ...,
  verbose = FALSE,
  parallelMCMC = FALSE
)

Arguments

`loglik`	a log-likelihood function.
`fitML`	an object returned by the maxLikPOUMMGivenTreeVTips
`parMapping`	a function(numeric-vector) transforming a sampled vector on the scale of the parameters alpha, theta, sigma, sigmae and g0.
`parInitMCMC`	a function(chainNumber) returning the starting point of the MCMC as a vector.
`parPriorMCMC`	a function(numeric-vector) returning the log-prior of the supplied vector
`parScaleMCMC`	numeric matrix indicating the initial jump-distribution matrix
`nSamplesMCMC`	integer indicating how many iterations should the mcmc-chain contain
`nAdaptMCMC`	integer indicating how many initial iterations should be used for adaptation of the jump-distribution matrix
`thinMCMC`	integer indicating the thinning interval of the mcmc-chain
`accRateMCMC`	(MCMC) numeric between 0 and 1 indicating the target acceptance rate Passed on to adaptMCMC::MCMC.
`gammaMCMC`	(MCMC) controls the speed of adaption. Should be in the interval (0.5,1]. A lower gammaMCMC leads to faster adaption. Passed on to adaptMCMC::MCMC.
`nChainsMCMC`	integer indicating the number of chains to run. Defaults to 1.
`samplePriorMCMC`	logical indicating if only the prior distribution should be sampled. This can be useful to compare with mcmc-runs for an overlap between prior and posterior distributions.
`pruneInfo`	a list-object returned from the pruneTree(tree, z) function.
`...`	Additional arguments. Currently not used except for the following: If ... includes debug = TRUE, some debug messages will be written also outside of the call to loglik.
`verbose`	Logical indicating if some informal messages should be written during run. This parameter is passed to loglik.
`parallelMCMC`	Logical indicating if chains should be run in parallel.

Details

Currently, this function calls the MCMC function from the adaptMCMC package.

Value

a list of coda objects

Number of tips in a phylogenetic tree, POUMM has been fit on.

Description

Number of tips in a phylogenetic tree, POUMM has been fit on.

Usage

## S3 method for class 'POUMM'
nobs(object, ...)
## S3 method for class 'POUMM'
nobs(object, ...)

Arguments

`object`	An object of class POUMM.
`...`	Not used; added for compatibility with generic function nobs.

Value

The number of tips in the tree, POUMM has been called on

Calculate the time from the root to each node of the tree

Description

Calculate the time from the root to each node of the tree

Usage

nodeTimes(tree, tipsOnly = FALSE)
nodeTimes(tree, tipsOnly = FALSE)

Arguments

`tree`	An object of class phylo.
`tipsOnly`	Logical indicating whether the returned results should be truncated only to the tips of the tree.

Value

A vector of size the number of nodes in the tree (tips, root, internal) containing the time from the root to the corresponding node in the tree.

Distribution of an Ornstein-Uhlenbeck Process at Time $t$ , Given Initial State at Time $0$

Description

An Ornstein-Uhlenbeck (OU) process represents a continuous time Markov chain parameterized by an initial state $x_0$ , selection strength $\alpha>0$ , long-term mean $\theta$ , and time-unit variance $\sigma^2$ . Given $x_0$ , at time $t$ , the state of the process is characterized by a normal distribution with mean $x_0 exp(-\alpha t) + \theta (1 - exp(-\alpha t))$ and variance $\sigma^2 (1-exp(-2 \alpha t)) / (2 \alpha)$ . In the limit $\alpha -> 0$ , the OU process converges to a Brownian motion process with initial state $x_0$ and time-unit variance $\sigma^2$ (at time $t$ , this process is characterized by a normal distribution with mean $x_0$ and variance $t \sigma^2$ .

Usage

dOU(z, z0, t, alpha, theta, sigma, log = TRUE)

rOU(n, z0, t, alpha, theta, sigma)

meanOU(z0, t, alpha, theta)

varOU(t, alpha, sigma)

sdOU(t, alpha, sigma)
dOU(z, z0, t, alpha, theta, sigma, log = TRUE)

rOU(n, z0, t, alpha, theta, sigma)

meanOU(z0, t, alpha, theta)

varOU(t, alpha, sigma)

sdOU(t, alpha, sigma)

Arguments

`z`	Numeric value or vector of size n.
`z0`	Numeric value or vector of size n, initial value(s) to condition on.
`t`	Numeric value or vector of size n, denoting the time-step.
`alpha`, `theta`, `sigma`	Numeric values or n-vectors, parameters of the OU process; alpha and sigma must be non-negative. A zero alpha is interpreted as the Brownian motion process in the limit alpha -> 0.
`log`	Logical indicating whether the returned density should is on the logarithmic scale.
`n`	Integer, the number of values to sample.

Details

Similar to dnorm and rnorm, the functions described in this help-page support single values as well as vectors for the parameters z, z0, t, alpha, theta and sigma.

Value

dOU returns the conditional probability density(ies) of the elements in z, given the initial state(s) z0, time-step(s) t and OU-parameters by alpha, theta and sigma.

rOU returns a numeric vector of length n, a random sample from the conditional distribution(s) of one or n OU process(es) given initial value(s) and time-step(s).

meanOU returns the expected value of the OU-process at time t.

varOU returns the expected variance of the OU-process at time t.

sdOU returns the standard deviation of the OU-process at time t.

Functions

dOU: probability density
rOU: random generator
meanOU: mean value
varOU: variance
sdOU: standard deviation

Examples

z0 <- 8
t <- 10
n <- 100000
sample <- rOU(n, z0, t, 2, 3, 1)
dens <- dOU(sample, z0, t, 2, 3, 1)
var(sample)  # around 1/4
varOU(t, 2, 1) 

z0 <- 8
t <- 10
n <- 100000
sample <- rOU(n, z0, t, 2, 3, 1)
dens <- dOU(sample, z0, t, 2, 3, 1)
var(sample)  # around 1/4
varOU(t, 2, 1)

Phylogenetic Heritability

Description

The phylogenetic heritability, $H^2$ , is defined as the ratio of the genetic variance over the total phenotypic variance expected at a given evolutionary time t (measured from the root of the tree). Thus, the phylogenetic heritability connects the parameters alpha, sigma and sigmae of the POUMM model through a set of equations. The functions described here provide an R-implementation of these equations.

Usage

alpha(H2, sigma, sigmae, t = Inf)

sigmaOU(H2, alpha, sigmae, t = Inf)

sigmae(H2, alpha, sigma, t = Inf)

H2e(z, sigmae, tree = NULL, tFrom = 0, tTo = Inf)
alpha(H2, sigma, sigmae, t = Inf)

sigmaOU(H2, alpha, sigmae, t = Inf)

sigmae(H2, alpha, sigma, t = Inf)

H2e(z, sigmae, tree = NULL, tFrom = 0, tTo = Inf)

Arguments

`H2`	Phylogenetic heritability at time t.
`sigmae`	Numeric, environmental phenotypic deviation at the tips.
`t`	Numeric value denoting evolutionary time (i.e. distance from the root of a phylogenetic tree).
`alpha`, `sigma`	Numeric values or n-vectors, parameters of the OU process; alpha and sigma must be non-negative. A zero alpha is interpreted as the Brownian motion process in the limit alpha -> 0.
`z`	Numerical vector of observed phenotypes.
`tree`	A phylo object.
`tFrom`, `tTo`	Numerical minimal and maximal root-tip distance to limit the calculation.

Details

The function sigmae uses the formula H2 = varOU(t, alpha, sigma) / (varOU(t, alpha, sigma) + sigmae^2)

Value

All functions return numerical values or NA, in case of invalid parameters

Functions

alpha: Calculate alpha given time t, H2, sigma and sigmae
sigmaOU: Calculate sigma given time t, H2 at time t, alpha and sigmae
sigmae: Calculate sigmae given alpha, sigma, and H2 at time t
H2e: "Empirical" phylogenetic heritability estimated from the empirical variance of the observed phenotypes and sigmae

Note

This function is called sigmaOU and not simply sigma to avoid a conflict with a function sigma in the base R-package.

Examples

# At POUMM stationary state (equilibrium, t=Inf)
H2 <- H2(alpha = 0.75, sigma = 1, sigmae = 1, t = Inf)     # 0.4
alpha <- alpha(H2 = H2, sigma = 1, sigmae = 1, t = Inf)    # 0.75
sigma <- sigmaOU(H2 = H2, alpha = 0.75, sigmae = 1, t = Inf) # 1
sigmae <- sigmae(H2 = H2, alpha = 0.75, sigma = 1, t = Inf) # 1

# At finite time t = 0.2
H2 <- H2(alpha = 0.75, sigma = 1, sigmae = 1, t = 0.2)     # 0.1473309
alpha <- alpha(H2 = H2, sigma = 1, sigmae = 1, t = 0.2)    # 0.75
sigma <- sigmaOU(H2 = H2, alpha = 0.75, sigmae = 1, t = 0.2) # 1
sigmae <- sigmae(H2  =  H2, alpha = 0.75, sigma = 1, t = 0.2) # 1

   
# At POUMM stationary state (equilibrium, t=Inf)
H2 <- H2(alpha = 0.75, sigma = 1, sigmae = 1, t = Inf)     # 0.4
alpha <- alpha(H2 = H2, sigma = 1, sigmae = 1, t = Inf)    # 0.75
sigma <- sigmaOU(H2 = H2, alpha = 0.75, sigmae = 1, t = Inf) # 1
sigmae <- sigmae(H2 = H2, alpha = 0.75, sigma = 1, t = Inf) # 1

# At finite time t = 0.2
H2 <- H2(alpha = 0.75, sigma = 1, sigmae = 1, t = 0.2)     # 0.1473309
alpha <- alpha(H2 = H2, sigma = 1, sigmae = 1, t = 0.2)    # 0.75
sigma <- sigmaOU(H2 = H2, alpha = 0.75, sigmae = 1, t = 0.2) # 1
sigmae <- sigmae(H2  =  H2, alpha = 0.75, sigma = 1, t = 0.2) # 1

Plots of a POUMM-fit

Description

Plots of a POUMM-fit

Usage

## S3 method for class 'POUMM'
plot(
  x,
  type = c("MCMC"),
  doPlot = TRUE,
  interactive = TRUE,
  stat = c("alpha", "theta", "sigma", "sigmae", "g0", "H2tMean"),
  chain = NULL,
  startMCMC = NA,
  endMCMC = NA,
  thinMCMC = 1000,
  statFunctions = statistics(x),
  doZoomIn = FALSE,
  zoomInFilter = paste0("(stat %in% c('H2e','H2tMean','H2tInf','H2tMax') & ",
    "(value >= 0 & value <= 1) ) |",
    "( !stat %in% c('H2e','H2tMean','H2tInf','H2tMax') & ",
    "(value <= median(HPDUpper) + 4 * (median(HPDUpper) - median(HPDLower)) &",
    "value >= median(HPDLower) - 4 * (median(HPDUpper) - median(HPDLower))))"),
  prettyNames = TRUE,
  showUnivarDensityOnDiag = FALSE,
  ...
)
## S3 method for class 'POUMM'
plot(
  x,
  type = c("MCMC"),
  doPlot = TRUE,
  interactive = TRUE,
  stat = c("alpha", "theta", "sigma", "sigmae", "g0", "H2tMean"),
  chain = NULL,
  startMCMC = NA,
  endMCMC = NA,
  thinMCMC = 1000,
  statFunctions = statistics(x),
  doZoomIn = FALSE,
  zoomInFilter = paste0("(stat %in% c('H2e','H2tMean','H2tInf','H2tMax') & ",
    "(value >= 0 & value <= 1) ) |",
    "( !stat %in% c('H2e','H2tMean','H2tInf','H2tMax') & ",
    "(value <= median(HPDUpper) + 4 * (median(HPDUpper) - median(HPDLower)) &",
    "value >= median(HPDLower) - 4 * (median(HPDUpper) - median(HPDLower))))"),
  prettyNames = TRUE,
  showUnivarDensityOnDiag = FALSE,
  ...
)

Arguments

`x`	An object of class POUMM.
`type`	A character indicating the type of plot(s) to be generated. Defaults to "MCMC", resulting in a trace and density plot for the selected statistics (see argument stat).
`doPlot`	Logical indicating whether a plot should be printed on the currently active graphics device or whether to return a list of ggplot objects for further processing. Defaults to TRUE.
`interactive`	Logical indicating whether the user should press a key before generating a next plot (when needed to display two or more plots). Defaults to TRUE. Meaningless if doPlot = FALSE.
`stat`	A character vector with the names of statistics to be plotted. These should be names from the stats-list (see argument statFunctions). Defaults to c("alpha", "theta", "sigma", "sigmae", "H2tMean", "H2tInf").
`chain`	A vector of integers indicating the chains to be plotted.
`startMCMC`, `endMCMC`, `thinMCMC`	Integers used to extract a sample from the MCMC-chain; passed to summary().
`statFunctions`	Named list of statistics functions; passed to summary().
`doZoomIn`	(type MCMC only) A logical value indicating whether the produced plots should have a limitation on the x-axis according to an expression set in zoomInFilter (see below). Default value is FALSE.
`zoomInFilter`	A character string which evaluates as logical value. If doZoomIn is set to TRUE, this filter is applied to each point in each MCMC chain and the data-point is filtered out if it evaluates to FALSE. This allows to zoomIn the x-axis of density plots but should be used with caution, since filtering out points from the MCMC-sample can affect the kernel densities. Unfortunately, filtering out values is currently the only way to affect the limits of individual facets in ggplot2. The default value is a complicated expression involving the HPD from all MCMC chains (normally one chain from the prior and 2 chains from the posterior): zoomInFilter = paste0("(stat "(value >= 0 & value <= 1) ) \|", "( !stat "(value <= median(HPDUpper) + 4 * (median(HPDUpper) - median(HPDLower)) &", "value >= median(HPDLower) - 4 * (median(HPDUpper) - median(HPDLower))))"). The identifiers in this expression can be any column names found in a summary of a POUMM object.
`prettyNames`	A logical indicating if greek letters and sub/superscripts should be used for the names of columns in the posterior density pairs-plot.
`showUnivarDensityOnDiag`	A logical indicating if univariate density plots should be displaied on the main diagonal in the bivariate posterior plot. Defaults to FALSE, in which case the column names are displayed on the diagonal.
`...`	not used, needed for consistency with the generic plot-function.

Value

If doPlot==FALSE, a named list containing a member called data of class data.table and several members of class ggplot.

Plot a summary of a POUMM fit

Description

Plot a summary of a POUMM fit

Usage

## S3 method for class 'summary.POUMM'
plot(
  x,
  type = c("MCMC"),
  doPlot = TRUE,
  stat = c("alpha", "theta", "sigma", "sigmae", "g0", "H2tMean"),
  chain = NULL,
  doZoomIn = FALSE,
  zoomInFilter = paste0("(stat %in% c('H2e','H2tMean','H2tInf','H2tMax') & ",
    "(value >= 0 & value <= 1) ) |",
    "( !stat %in% c('H2e','H2tMean','H2tInf','H2tMax') & ",
    "(value <= median(HPDUpper) + 4 * (median(HPDUpper) - median(HPDLower)) &",
    "value >= median(HPDLower) - 4 * (median(HPDUpper) - median(HPDLower))))"),
  palette = c("#999999", "#0072B2", "#CC79A7", "#E69F00", "#D55E00", "#56B4E9",
    "#009E73", "#F0E442"),
  prettyNames = TRUE,
  ...
)
## S3 method for class 'summary.POUMM'
plot(
  x,
  type = c("MCMC"),
  doPlot = TRUE,
  stat = c("alpha", "theta", "sigma", "sigmae", "g0", "H2tMean"),
  chain = NULL,
  doZoomIn = FALSE,
  zoomInFilter = paste0("(stat %in% c('H2e','H2tMean','H2tInf','H2tMax') & ",
    "(value >= 0 & value <= 1) ) |",
    "( !stat %in% c('H2e','H2tMean','H2tInf','H2tMax') & ",
    "(value <= median(HPDUpper) + 4 * (median(HPDUpper) - median(HPDLower)) &",
    "value >= median(HPDLower) - 4 * (median(HPDUpper) - median(HPDLower))))"),
  palette = c("#999999", "#0072B2", "#CC79A7", "#E69F00", "#D55E00", "#56B4E9",
    "#009E73", "#F0E442"),
  prettyNames = TRUE,
  ...
)

Arguments

`x`	An object of class POUMM.
`type`	A character indicating the type of plot(s) to be generated. Defaults to "MCMC", resulting in a trace and density plot for the selected statistics (see argument stat). Currently, only 'MCMC' type is supported.
`doPlot`	Logical indicating whether a plot should be printed on the currently active graphics device or whether only to return a list of plot- objects for further processing. Defaults to TRUE.
`stat`	A character vector with the names of statistics to be plotted. These should be names from the stats-list (see argument statFunctions). Defaults to c("alpha", "theta", "sigma", "sigmae", "H2tMean", "H2tInf").
`chain`	A vector of integers indicating the chains to be plotted.
`doZoomIn`	(type MCMC only) A logical value indicating whether the produced plots should have a limitation on the x-axis according to an expression set in zoomInFilter (see below). Default value is FALSE.
`zoomInFilter`	A character string which evaluates as logical value. If doZoomIn is set to TRUE, this filter is applied to each point in each MCMC chain and the data-point is filtered out if it evaluates to FALSE. This allows to zoomIn the x-axis of density plots but should be used with caution, since filtering out points from the MCMC-sample can affect the kernel densities. Unfortunately, filtering out values is currently the only way to affect the limits of individual facets in ggplot2. The default value is a complicated expression involving the HPD from all MCMC chains (normally one chain from the prior and 2 chains from the posterior): zoomInFilter = paste0("stat The identifiers in this expression can be any column names found in a summary of a POUMM object.
`palette`	A vector of colors (can be character strings) corresponding to the different chains (in their order 1 (prior), 2, 3). Defaults to c("#999999", "#0072B2", "#CC79A7", "#E69F00", "#D55E00", "#56B4E9", "#009E73", "#F0E442"), which is a color-blind friendly.
`prettyNames`	A logical indicating if greek letters and sub/superscripts should be used for the names of columns in the posterior density pairs-plot.
`...`	Not used; included for compatibility with the generic function plot.

Value

If doPlot==TRUE, the function returns nothing and produces output on the current graphics device as a side-effect. Otherwise, the function returns a list of plot-objects: traceplot and densplot.

Examples

## Not run: 
library(POUMM)

set.seed(1)

N <- 1000

# create a random non-ultrametric tree of N tips
tree <- ape::rtree(N)  

# Simulate the evolution of a trait along the tree
z <- rVNodesGivenTreePOUMM(
  tree, g0 = 8, alpha = 1, theta = 4, sigma = 1.2, sigmae = .8)

fit <- POUMM(z[1:N], tree, spec = list(nSamplesMCMC = 4e5))

# Summarize the results from the fit in a table:
summary(fit)

# Create plots for some of the inferred parameters/statistics:
pl <- plot(fit, stat = c("alpha", "theta", "sigma", "sigmae", "H2tMean"), 
           doZoomIn = TRUE, 
           zoomInFilter = paste("!(stat %in% c('alpha', 'sigma', 'sigmae')) |",
                                "(value >= 0 & value <= 8)"),
           doPlot = FALSE)

pl$traceplot
pl$densplot

## End(Not run)

## Not run: 
library(POUMM)

set.seed(1)

N <- 1000

# create a random non-ultrametric tree of N tips
tree <- ape::rtree(N)  

# Simulate the evolution of a trait along the tree
z <- rVNodesGivenTreePOUMM(
  tree, g0 = 8, alpha = 1, theta = 4, sigma = 1.2, sigmae = .8)

fit <- POUMM(z[1:N], tree, spec = list(nSamplesMCMC = 4e5))

# Summarize the results from the fit in a table:
summary(fit)

# Create plots for some of the inferred parameters/statistics:
pl <- plot(fit, stat = c("alpha", "theta", "sigma", "sigmae", "H2tMean"), 
           doZoomIn = TRUE, 
           zoomInFilter = paste("!(stat %in% c('alpha', 'sigma', 'sigmae')) |",
                                "(value >= 0 & value <= 8)"),
           doPlot = FALSE)

pl$traceplot
pl$densplot

## End(Not run)

The Phylogenetic (Ornstein-Uhlenbeck) Mixed Model

Description

This is the high-level entry point to the POUMM method. The POUMM function fits the POUMM method to a tree and observed trait-values at its tips and returns an object of class "POUMM".

Usage

POUMM(
  z,
  tree,
  se = 0,
  zName = "z",
  treeName = "tree",
  parDigits = 6,
  usempfr = 0,
  useCpp = TRUE,
  ...,
  spec = NULL,
  doMCMC = TRUE,
  likPOUMM_lowLevelFun = likPOUMMGivenTreeVTipsC,
  verbose = FALSE,
  debug = FALSE
)
POUMM(
  z,
  tree,
  se = 0,
  zName = "z",
  treeName = "tree",
  parDigits = 6,
  usempfr = 0,
  useCpp = TRUE,
  ...,
  spec = NULL,
  doMCMC = TRUE,
  likPOUMM_lowLevelFun = likPOUMMGivenTreeVTipsC,
  verbose = FALSE,
  debug = FALSE
)

Arguments

`z`	Either a numeric vector containing the phenotypic values at the tips of tree or a named list containing named elements z - a numeric vector and tree - a phylo object (it is possible to specify different element names using the arguments zName and treeName).
`tree`	A phylo object or NULL in case z is a list.
`se`	A non-negative numerical vector (or single number) indicating known measurement standard error (defaults to 0). Note the elements of this vector are assumed to describe the measurement error at individual nodes independent of the environmental contribution (described by the parameter sigmae). The total error standard deviation is thus sqrt(sigmae2+se^2).
`zName`, `treeName`	Character strings used when the parameter z is a list; indicate the names in the list of the values-vector and the tree. Default: 'z' and 'tree'.
`parDigits`	Integer specifying rounding to be done on the parameter vector before likelihood calculation. Defaults to 6 decimal digits. This can be useful during maximum likelihood optimization to prevent likelihood calculation on very small but positive values of alpha, but should be used with caution since specifying a small number of digits, i.e. 2 or 3 can result in an infinite loop during optim. Specify a negative number to disable rounding.
`usempfr`	integer indicating if and how mpfr should be used for small parameter values ('any(c(alpha, sigma, sigmae) < 0.01)'). Using the mpfr package can be forced by specifying an integer greater or equal to 2. Setting usempfr=0 (default) causes high precision likelihood calculation to be done on each encounter of parameters with at least 1 bigger log-likelihood value than any of the currently found maximum log-likelihood or the previously calculated log-likelihood value Requires the Rmpfr package. Note that using mpfr may increase the time for one likelihood calculation more than 100-fold. Set usempfr to -1 or less to completely disable Rmpfr functionality.
`useCpp`	Logical indicating whether C++ likelihood calculation should be used for faster vector operations. Defaults to TRUE. Since the C++ likelihood implementation does not support mpfr, useCpp gets disabled when usempfr is bigger than 0.
`...`	additional arguments passed to the 'likPOUMMGivenTreeVTips()' function ('?dVGivenTreeOU' for details).
`spec`	A named list specifying how the ML and MCMC fit should be done. See '?specifyPOUMM'.
`doMCMC`	Deprecated - replaced by specifying nSamplesMCMC as a member of spec instead (see '?specifyPOUMM'). logical: should a MCMC fit be performed. An MCMC fit provides a sample from the posterior distribution of the parameters given a prior distribution and the data. Unlike the ML-fit, it allows to estimate confidence intervals for the estimated parameters. This argument is TRUE by default. The current implementation uses the adaptive Metropolis sampler from the package 'adaptMCMC' written by Andreas Scheidegger. To obtain meaningful estimates MCMC may need to run for several millions of iterations (parameter nSamplesMCMC set to 1e5 by default). See parameters ending at MCMC in '?specifyPOUMM' for details.
`likPOUMM_lowLevelFun`	the low-level function used for POUMM - likelihood calculation. Default value is likPOUMMGivenTreeVTipsC.
`verbose`, `debug`	Logical flags indicating whether to print informative and/or debug information on the standard output (both are set to to FALSE by default).

Value

An object of S3 class 'POUMM'. This object can be analyzed using S3 generic functions: summary, plot, AIC, BIC, coef, logLik, fitted.

References

Scheidegger, A. (2012). adaptMCMC: Implementation of a generic adaptive Monte Carlo Markov Chain sampler. http://CRAN.R-project.org/package=adaptMCMC

Examples

## Not run: 
# Please, read the package vignette for more detailed examples.
N <- 500
tr <- ape::rtree(N)
z <- rVNodesGivenTreePOUMM(tr, 0, 2, 3, 1, 1)[1:N]
fit <- POUMM(z, tr, spec = specifyPOUMM(nSamplesMCMC = 5e4))
plot(fit)
summary(fit)
AIC(fit)
BIC(fit)
coef(fit)
logLik(fit)
fitted(fit)
plot(resid(fit))
abline(h=0)

# fit PMM to the same data and do a likelihood ratio test
fitPMM <- POUMM(z, tr, spec = specifyPMM(nSamplesMCMC = 5e4))
lmtest::lrtest(fitPMM, fit)

## End(Not run)

## Not run: 
# Please, read the package vignette for more detailed examples.
N <- 500
tr <- ape::rtree(N)
z <- rVNodesGivenTreePOUMM(tr, 0, 2, 3, 1, 1)[1:N]
fit <- POUMM(z, tr, spec = specifyPOUMM(nSamplesMCMC = 5e4))
plot(fit)
summary(fit)
AIC(fit)
BIC(fit)
coef(fit)
logLik(fit)
fitted(fit)
plot(resid(fit))
abline(h=0)

# fit PMM to the same data and do a likelihood ratio test
fitPMM <- POUMM(z, tr, spec = specifyPMM(nSamplesMCMC = 5e4))
lmtest::lrtest(fitPMM, fit)

## End(Not run)

Check if the POUMM version correpsonds to a dev release

Description

We define a dev release as having a sub-release, eg 0.9.15.5 is one whereas 0.9.16 is not.

Usage

POUMMIsADevRelease()
POUMMIsADevRelease()

Value

a logical

Extract information for fast likelihood calculation using the breadth-first pruning algorithm.

Description

Extract information for fast likelihood calculation using the breadth-first pruning algorithm.

Usage

pruneTree(tree, z, se = 0)
pruneTree(tree, z, se = 0)

Arguments

`tree`	a phylo object
`z`	Numeric vector with length(tree$tip.label) values corresponding to tree$tip.label.
`se`	Non-negative numerical or N-vector indicating known standard measurement error.

Value

a list of objects used for likelihood evaluation

Extract maximum likelihood environmental contributions (residuals) at the tips of a tree, to which a POUMM model has been fitted.

Description

Extract maximum likelihood environmental contributions (residuals) at the tips of a tree, to which a POUMM model has been fitted.

Usage

## S3 method for class 'POUMM'
residuals(object, ...)
## S3 method for class 'POUMM'
residuals(object, ...)

Arguments

`object`	An object of class POUMM.
`...`	Not used; added for compatibility with generic function residuals.

Value

The vector of e-values (residuals) corresponding to the tip-labels in the tree.

Generation of a random trajectory of an OU process starting from a given initial state

Description

Generates a trajectory xt given initial state z0 according to an Ornstein-Uhlenbeck process.

Usage

rTrajectoryOU(z0, t, alpha, theta, sigma, steps = 1)
rTrajectoryOU(z0, t, alpha, theta, sigma, steps = 1)

Arguments

`z0`	Numeric value, initial state.
`t`	Numeric value or vector of size steps, denoting the time-step(s).
`alpha`, `theta`, `sigma`	Numeric values, parameters of the OU process.
`steps`	Integer, number of steps.

Value

A numeric vector of length steps containing the generated values at times 0+t, 0+2t, ..., 0+steps*t.

Examples

z0 <- 0
nSteps <- 100
t <- 0.01
trajectory <- rTrajectoryOU(z0, t, 2, 2, 1, steps = nSteps)
plot(trajectory, type = 'l')

z0 <- 0
nSteps <- 100
t <- 0.01
trajectory <- rTrajectoryOU(z0, t, 2, 2, 1, steps = nSteps)
plot(trajectory, type = 'l')

Generation of a random trajectory of an OU process starting from a given initial state (only for test purpose)

Description

Generation of a random trajectory of an OU process starting from a given initial state (only for test purpose)

Usage

rTrajectoryOUDef(z0, t, alpha, theta, sigma, steps = 1)
rTrajectoryOUDef(z0, t, alpha, theta, sigma, steps = 1)

Arguments

`z0`	Numeric value, initial state.
`t`	Numeric value or vector of size steps, denoting the time-step(s).
`alpha`	Numeric values, parameters of the OU process.
`theta`	Numeric values, parameters of the OU process.
`sigma`	Numeric values, parameters of the OU process.
`steps`	Integer, number of steps.

Details

Used for test purpose only. This is an internal function and is appropriate for small time-steps only.

Random generation of values along a phylogenetic tree following a branching OU process

Description

Random generation of values along a phylogenetic tree following a branching OU process

Usage

rVNodesGivenTreePOUMM(tree, z0, alpha, theta, sigma, sigmae = 0)
rVNodesGivenTreePOUMM(tree, z0, alpha, theta, sigma, sigmae = 0)

Arguments

`tree`	An object of class phylo (package ape).
`z0`	Numeric value indicating the initial state at the root of the tree.
`alpha`, `theta`, `sigma`	Numeric values, parameters of the OU process.
`sigmae`	Numeric non-negative value (default 0). Specifies the standard deviation of random environmental contribution and or measurement standard error to be added to the values (white noise). Note that if measurement standard error, se, is known and needs to be added to the environmental contribution, the right way to specify the parameter would be sqrt(sigmae^2+se^2).

Value

A numeric vector containing the generated values at all nodes (internal and tips) of the phylogenetic tree.

Writes verbose messages of the order of tree traversal during likelihood calculation

Description

Writes verbose messages of the order of tree traversal during likelihood calculation

Usage

simulatePOUMMLikelihoodMainLoop(tree)
simulatePOUMMLikelihoodMainLoop(tree)

Arguments

tree

A phylo object.

Value

Nothing

Simulate a trait on a tree under a ML fit of the POUMM model

Description

Use the maximum likelihood parameters of the model to simulate trait values on a phylogenetic tree.

Usage

simulateTrait(object, tree = NULL)
simulateTrait(object, tree = NULL)

Arguments

`object`	an S3 object of class POUMM
`tree`	a phylo object. If NULL (default) the trait is simulated on the tree, on which the POUMM object has been fit.

Details

This function is a shortcut to calling rVNodesGivenTreePOUMM, which will map the inferred parameters of the model back to the original POUMM parameters alpha, theta, sigma, sigmae, and g0.

Value

a numerical vector containing the simulated trait value for each tip in the tree.

Specifying a POUMM fit

Description

Specification and validation of POUMM/PMM settings.

Usage

specifyPOUMM(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE,
  validateSpec = TRUE
)

specifyPOUMM_ATS(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE,
  sigmaeFixed = 0
)

specifyPOUMM_ATSG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE,
  sigmaeFixed = 0
)

specifyPOUMM_ATSSeG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPMM(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPMM_SSeG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPOUMM_ATH2tMeanSe(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPOUMM_ATH2tMeanSeG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPMM_H2tMeanSe(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPMM_H2tMeanSeG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)
specifyPOUMM(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE,
  validateSpec = TRUE
)

specifyPOUMM_ATS(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE,
  sigmaeFixed = 0
)

specifyPOUMM_ATSG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE,
  sigmaeFixed = 0
)

specifyPOUMM_ATSSeG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPMM(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPMM_SSeG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPOUMM_ATH2tMeanSe(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPOUMM_ATH2tMeanSeG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPMM_H2tMeanSe(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

specifyPMM_H2tMeanSeG0(
  z = NULL,
  tree = NULL,
  zMin = -10,
  zMean = 0,
  zMax = 10,
  zVar = 4,
  zSD = sqrt(zVar),
  tMin = 0.1,
  tMean = 2,
  tMax = 10,
  parMapping = NULL,
  parLower = NULL,
  parUpper = NULL,
  g0Prior = NULL,
  parInitML = NULL,
  control = NULL,
  parPriorMCMC = NULL,
  parInitMCMC = NULL,
  parScaleMCMC = NULL,
  nSamplesMCMC = 1e+05,
  nAdaptMCMC = nSamplesMCMC,
  thinMCMC = 100,
  accRateMCMC = 0.01,
  gammaMCMC = 0.50001,
  nChainsMCMC = 3,
  samplePriorMCMC = TRUE,
  parallelMCMC = FALSE
)

Arguments

`z`, `tree`	a numeric vector and a phylo object on which the fit is to be done. These arguments are used in order to guess meaningful values for the parLower, parUpper and parPriorMCMC arguments. See also, zMin,zMean,...,tMax below.
`zMin`, `zMean`, `zMax`, `zVar`, `zSD`, `tMin`, `tMean`, `tMax`	summary statistics of the observed tip-values (z) and root-tip distances (t). Some of these values are used for constructing default parameter values and limits; These arguments are given default values which will most likely be meaningless in your specific use-case. The default values will be overwritten with the corresponding statistics from the z and tree arguments if these were specified. If none of tree and z, nor these parameters are specified, then the arguments parLower, parUpper, parPriorMCMC must be specified explicitly.
`parMapping`	An R-function that can handle, both, a numeric vector or a numeric matrix as argument. This function should transform the input vector or each row-vector (if the input is matrix) into a (row-)vector of the POUMM parameters alpha, theta, sigma, sigmae, g0. For a vector input the function should return a vector with named elements alpha, theta, sigma, sigmae, g0. For a matrix input the function should return a matrix with the same number of rows and columns alpha, theta, sigma, sigmae, g0. Only finite non-negative values are allowed for alpha, sigma, and sigmae. Returning Inf, -Inf, NA or NaN for any of these parameters will result in an error during likelihood calculation. Only finite numerical values are allowed for theta. The parameter g0 is treated in a special way and can assume either a finite numerical value or one of NA or NaN. If g0 = finite value, this value is used together with the corresponding values of alpha, theta, sigma, and sigmae for likelihood calcuation. If g0 = NA (meaing value Not Avaiable), the value of g0 is calculated analytically during likelihood calculation in order to maximise one of the following: if a normal prior for g0 was specified (see g0Prior), $pdf(z \| \alpha, \theta, \sigma, \sigma_e, g0, tree) x prior(g0)$ . otherwise, $pdf(z \| \alpha, \theta, \sigma, \sigma_e, g0, tree)$ . If g0 = NaN (meaning Not a Number), then the likelihood is marginalized w.r.t. the g0's prior distribution (see g0Prior), i.e. the likelihood returned is: $pdf(z \| \alpha, \theta, \sigma, \sigma_e, tree) = Integral(pdf(z\|\alpha,\theta,\sigma,\sigma_e,g0) x pdf(g0) d g0; g0 from -\infty to +\infty)$ In this case (g0=NaN), if g0Prior is not specified, it is assumed that g0Prior is the stationary OU normal distribution with mean, theta, and variance, varOU(Inf, alpha, sigma). Examples: # Default for POUMM: identity for alpha, theta, sigma, sigmae, NA for g0. parMapping = function(par) { if(is.matrix(par)) { atsseg0 <- cbind(par[, 1:4, drop = FALSE], NA) colnames(atsseg0) <- c("alpha", "theta", "sigma", "sigmae", "g0") } else { atsseg0 <- c(par[1:4], NA) names(atsseg0) <- c("alpha", "theta", "sigma", "sigmae", "g0") } atsseg0 }
`parLower`, `parUpper`	two named numeric vectors of the same length indicating the boundaries of the search region for the ML-fit. Calling parMapping on parLower and parUpper should result in appropriate values of the POUMM parameters alpha, theta, sigma sigmae and g0. By default, the upper limit for alpha is set to 69.31 / tMean, which corresponds to a value of alpha so big that the time for half-way convergence towards theta from any initial trait value is 100 times shorter than the mean root-tip distance in the tree. Examples: # Default for POUMM: parLower = c(alpha = 0, theta = zMin - 2 * (zMax - zMin), sigma = 0, sigmae = 0) parUpper = c(alpha = 69.31 / tMean, theta = zMax + 2 * (zMax - zMin), sigma = sigmaOU(H2 = .99, alpha = 69.31 / tMean, sigmae = 2 * zSD, t = tMean), sigmae = 2 * zSD)
`g0Prior`	Either NULL or a list with named numeric or character members "mean" and "var". Specifies a prior normal distribution for the parameter g0. If characters, the members "mean" and "var" are evaluated as R-expressions - useful if these are functions of some of other parameters. Note that if g0Prior is not NULL and g0 is not NaN (either a fixed number or NA), then the likelihood maximization takes into account the prior for g0, that is, the optimization is done over the product p(g0) x lik(data\|g0, other parameters and tree). This can be helpful to prevent extremely big or low estimates of g0. To avoid this behavior and always maximize the likelihood, use g0Prior = NULL.
`parInitML`	A named vector (like parLower and parUpper) or a list of such vectors - starting points for optim.
`control`	List of parameters passed on to optim in the ML-fit, default list(factr=1e9), see ?optim.
`parPriorMCMC`	A function of a numeric parameter-vector returning the log-prior for this parameter vector. Example: # Default for POUMM: parPriorMCMC = function(par) { dexp(par[1], rate = tMean / 6.931, TRUE) + dnorm(par[2], zMean, 10 * zSD, TRUE) + dexp(par[3], rate = sqrt(tMean / (zVar * 0.6931)), TRUE) + dexp(par[4], rate = 2 / zSD, TRUE) }
`parInitMCMC`	a function(chainNo, fitML) returning an initial state of an MCMC as a vector. The argument fitML can be used to specify an initial state, close to a previously found likelihood optimum. Example: # Default for POUMM: parInitMCMC = function(chainNo, fitML) { if(!is.null(fitML)) { parML <- fitML$par } else { parML <- NULL } init <- rbind( c(alpha = 0, theta = 0, sigma = 1, sigmae = 0), parML, c(alpha = 0, theta = 0, sigma = 1, sigmae = 1) ) init[(chainNo - 1) %% nrow(init) + 1, ] }
`parScaleMCMC`	Numeric matrix indicating the initial jump-distribution matrix for the MCMC fit. Default for POUMM is diag(4);
`nSamplesMCMC`	Integer indicating the length of each MCMC chain. Defaults to 1e5.
`nAdaptMCMC`	Logical indicating whether adaptation of the MCMC jump distribution should be done with respect to the target acceptance rate (accRateMCMC) or integer indicating how many initial MCMC iterations should be used for adaptation of the jump-distribution matrix (see details in ?POUMM). Defaults to nSamplesMCMC meaning continuous adaptation throughout the MCMC.
`thinMCMC`	Integer indicating the thinning interval of the mcmc-chains. Defaults to 100.
`accRateMCMC`	numeric between 0 and 1 indicating the target acceptance rate of the adaptive Metropolis sampling (see details in ?POUMM). Default 0.01.
`gammaMCMC`	controls the speed of adaption. Should be in the interval (0.5,1]. A lower gamma leads to faster adaption. Default value is 0.50001.
`nChainsMCMC`	integer indicating the number of chains to run. Defaults to 3 chains, from which the first one is a sample from the prior distribution (see samplePriorMCMC).
`samplePriorMCMC`	Logical indicating if sampling from the prior should be done for the first chain (see nChainsMCMC). This is useful to compare mcmc's for an overlap between prior and posterior distributions. Default is TRUE.
`parallelMCMC`	Logical indicating whether the MCMC chains should be run in parallel. Setting this option to TRUE results in using `foreach::foreach() %dopar% { }` construct for the MCMC fit. In order for parallel execution to be done, you should create a computing cluster and register it as parallel back-end (see example in package vignette and the web-page https://github.com/tobigithub/R-parallel/wiki/R-parallel-Setups).
`validateSpec`	Logical indicating whether the passed parameters should be validated. This parameter is used internally and should always be TRUE.
`sigmaeFixed`	fixed value for the sigmae parameter (used in specifyPOUMM_ATS and specifyPOUMM_ATSG0).

Value

A named list to be passed as a spec argument to POUMM.

Functions

specifyPOUMM: Specify parameters for fitting a POUMM model. Parameter vector is c(alpha, theta, sigma, sigmae). Default model settings.
specifyPOUMM_ATS: Fitting a POU model with fixed sigmae. Parameter vector is c(alpha, theta, sigma).
specifyPOUMM_ATSG0: Fitting a POU model with fixed sigmae. Parameter vector is c(alpha, theta, sigma, g0).
specifyPOUMM_ATSSeG0: Fitting a POUMM model with sampling of g0. Parameter vector is c(alpha, theta, sigma, sigmae, g0).
specifyPMM: Specify parameter for fitting a PMM model. Parameter vector is c(sigma, sigmae)
specifyPMM_SSeG0: Specify parameter for fitting a PMM model with sampling of g0. Parameter vector is c(sigma, sigmae, g0).
specifyPOUMM_ATH2tMeanSe: Fitting a POUMM model with a uniform prior for the phylogenetic heritability at mean root-tip distance. Parameter vector is c(alpha, theta, H2tMean, sigmae).
specifyPOUMM_ATH2tMeanSeG0: Fitting a POUMM model with a uniform prior for the phylogenetic heritability at mean root-tip with sampling of g0. Parameter vector is c(alpha, theta, H2tMean, sigmae, g0).
specifyPMM_H2tMeanSe: Fitting a PMM model with a uniform prior for the phylogenetic heritability at mean root-tip distance. Parameter vector is c(H2tMean, sigmae).
specifyPMM_H2tMeanSeG0: Fitting a PMM model with a uniform prior for the phylogenetic heritability at mean root-tip distance with sampling of G0. Parameter vector is c(H2tMean, sigmae, g0).

Extract statistics from sampled or inferred parameters of a POUMM fit

Description

Extract statistics from sampled or inferred parameters of a POUMM fit

Usage

statistics(object)

## S3 method for class 'POUMM'
statistics(object)
statistics(object)

## S3 method for class 'POUMM'
statistics(object)

Arguments

object

An object of class "POUMM".

Details

This is a generic method.

Methods (by class)

POUMM: Relevant statistics from the sampled parameters of a POUMM fit

Summarize the results of a POUMM-fit

Description

Summarize the results of a POUMM-fit

Usage

## S3 method for class 'POUMM'
summary(
  object,
  ...,
  startMCMC = NA,
  endMCMC = NA,
  thinMCMC = 1000,
  stats = statistics(object),
  mode = c("short", "long", "expert")
)
## S3 method for class 'POUMM'
summary(
  object,
  ...,
  startMCMC = NA,
  endMCMC = NA,
  thinMCMC = 1000,
  stats = statistics(object),
  mode = c("short", "long", "expert")
)

Arguments

`object`	a POUMM object returned by POUMM-function (see ?POUMM).
`...`	Not used, but declared for consistency with the generic method summary.
`startMCMC`, `endMCMC`	integers indicating the range of the MCMC chains to be used for the analysis (excluding the initial warm-up phase)
`thinMCMC`	thinning interval of the MCMC chain to avoid strong autocorrelation between sampled elements;
`stats`	a named list of functions of the form function(par) number , which are called for each sample of each mcmc chain in object. Defaults to a call of statistics(object) returning a list of statistics functions relevant for the object. See also statistics.
`mode`	a character indicating the desired format of the returned summary as follows: 'short' - a data.table with the ML and MCMC estimates of heritability, model parameters, root-value and other statistics. 'long' - same information as in 'short' but including also the samples, which can be convenient for

Validate a POUMM specification

Description

Validate a POUMM specification

Usage

validateSpecPOUMM(spec)
validateSpecPOUMM(spec)

Arguments

spec

A list object returned by one of the specifyPOUMM or specifyPMM functions with possibly modified entries afterwards.

Value

The function either returns TRUE or exits with an error message if it finds a problem with the specificaiton.

Validate phenotypic values and phylogenetic tree

Description

Validate phenotypic values and phylogenetic tree

Usage

validateZTree(z, tree)
validateZTree(z, tree)

Arguments

`z`	trait (phenotypic) values at the tips of the tree
`tree`	A phylo object with the same number of tips as the length of z.

Value

The function either returns TRUE or exits with an error message if it finds a problem with the specificaiton.

Cached objects for the POUMM vignettes and examples

Description

A list containing a simulated tree, trait-values and POUMM objects (model fits). To use these objects in examples you can load them into the global workspace with the command: 'data(vignetteCachedResults); list2env(vignetteCachedResults, globalenv());'.

Usage

vignetteCachedResults
vignetteCachedResults

Format

This is a list containing the following named elements:

g, z, e: numeric vectors of simulated genotypic values, phenotypic values and measurement errors.
tree: a simulated phylogenetic tree.
fitPOUMM, fitPOUMM2, fitH2tMean: POUMM fit objects to tree and z.

Package 'POUMM'

Help Index

Extract data from an MCMC chain This is an internal function.

Description

Usage

Arguments

Node indices of the direct descendants of n in the phylogeny.

Description

Usage

Arguments

Value

Extract maximum likelihood fitted parameters (coefficients) from a fitted POUMM model.

Description

Usage

Arguments

Details

Value

A vectorized expected covariance function for a given tree and a fitted POUMM model

Description

Usage

Arguments

Value

A vectorized function returning HPD intervals of the expected covariance for a given tree and a fitted POUMM model

Description

Usage

Arguments

Value

Expected covariance of two tips at given root-tip time and phylogenetic distance

Description

Usage

Arguments

Details

Value

Variance covariance matrix of the values at the tips of a tree under an OU process

Description

Usage

Arguments

Value

References

Multivariate density of observed values along a tree given an OU process of evolution and root-value

Description

Usage

Arguments

Value

Edge indices of the edges in tree starting from n

Description

Usage

Arguments

Value

Extract maximum likelihood expected genotypic values at the tips of a tree, to which a POUMM model has been previously fitted

Description

Usage

Arguments

Value

Distribution of the genotypic values under a POUMM fit

Description

Usage

Arguments

Value

Phylogenetic heritability estimated at time t

Description

Usage

Arguments

Density of observed tip-values given a tree, assuming Ornstein-Uhlenbeck process for the genetic contributions along the tree and normally distributed environmental deviations.

Description

Usage

Arguments

Value

Fast (parallel) POUMM likelihood calculation using the SPLITT library

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Processing of the root value and calculation of the maximum log-likelihood for the given coefficients abc, and parameters theta, g0 and g0Prior. This is an internal function.

Description

Usage

Distribution of an Ornstein-Uhlenbeck Process at Time $t$ , Given Initial State at Time $0$