phylopars: Estimation of phylogenetic and phenotypic covariance...

In Rphylopars: Phylogenetic Comparative Tools for Missing Data and Within-Species Variation

Estimation of phylogenetic and phenotypic covariance parameters

Description

This function estimates parameters for the phylogenetic and phenotypic variance-covariance matrices for datasets with missing observations and multiple within-species observations. This function can also be used to fit altnerative evolutionary models, including Ornstein-Uhlenbeck, Early-Burst, star phylogeny, or Pagel's lambda, kappa, or delta. Reconstructed ancestral states and predicted species means (i.e., for missing data), along with prediction variances, are also provided.

Usage

phylopars(trait_data, tree, model = "BM", pheno_error, phylo_correlated = TRUE,
pheno_correlated = TRUE, REML = TRUE, full_alpha = TRUE, phylocov_start,
phenocov_start, model_par_start, phylocov_fixed, phenocov_fixed, model_par_fixed,
skip_optim = FALSE, skip_EM = FALSE, EM_Fels_limit = 1000, repeat_optim_limit = 1,
EM_missing_limit = 50, repeat_optim_tol = 0.01, model_par_evals = 10, max_delta = 10000,
EM_verbose = FALSE, optim_verbose = FALSE, npd = FALSE,
nested_optim = FALSE, usezscores = TRUE, phenocov_list = list(), ret_args = FALSE,
ret_level = 1, get_cov_CIs = FALSE)

Arguments

trait_data	A data frame with the first column labeled "species" (with species names matching tips on the phylogeny) and one column per trait. Each row corresponds to a single observation, and multiple observations for species are allowed. Missing data should be represented with NA.
tree	An object of class phylo
model	Model of evolution. Default is "BM". Alternative evolutionary models include "mvOU" (for the multivariate Ornstein-Uhlenbeck), or univariate tree transformations: "OU" "lambda", "kappa", "delta", "EB", "star".
pheno_error	If TRUE (default, unless <=1 observation per species is provided), parameters are estimated assuming within-species variation.
phylo_correlated	If TRUE (default), parameters are estimated assuming traits are correlated.
pheno_correlated	If TRUE (default), parameters are estimated assuming within-species observations traits are correlated.
REML	If TRUE (default), the algorithm will return REML estimates. If FALSE, maximum likelihood estimates will be returned.
full_alpha	Only applicable for the multivariate OU (model="mvOU"). If TRUE (default), a fully parametrized alpha matrix is fit. If FALSE, a diagonal alpha matrix is fit.
phylocov_start	Optional starting value for phylogenetic trait variance-covariance matrix. Must be of dimension n_traits by n_traits.
phenocov_start	Optional starting value for phenotypic trait variance-covariance matrix. Must be of dimension n_traits by n_traits.
model_par_start	Optional starting parameters for the evolutionary model. For model="mvOU", must be of dimension n_traits by n_traits. Otherwise, must be a single value.
phylocov_fixed	Optional fixed value for phylogenetic trait variance-covariance matrix. Must be of dimension n_traits by n_traits.
phenocov_fixed	Optional starting value for phenotypic trait variance-covariance matrix. Must be of dimension n_traits by n_traits.
model_par_fixed	Optional fixed parameter for the evolutionary model. For model="mvOU", must be of dimension n_traits by n_traits. Otherwise, must be a single value.
skip_optim	Whether to skip BFGS optimization (not recommended unless all parameters are fixed).
skip_EM	Whether to skip Expectation-Maximiation prior to generating starting parameters for BFGS optimization (not recommended unless providing fixed parameters).
EM_Fels_limit	Whether to skip Expectation-Maximiation prior to generating starting parameters for BFGS optimization (not recommended unless providing fixed parameters).
repeat_optim_limit	The number of times to repeat numerical optimization (default is 1).
EM_missing_limit	Maximum number of iterations for EM.
repeat_optim_tol	Maximum tolerance for repeated numerical optimization (only relevant if repeat_optim_limit>1).
model_par_evals	Number of times to evaluate univariate tree transformation models along the range of possible parameter values. Used to generate informed starting values for alternative evolutionary models if nested_optim=TRUE.
max_delta	Maximum allowed difference between the log-likelihood for EM-generated starting parameters and new parameters tried under numerical optimization. Extremely large deltas are likely to be numerical artifiacts. Prevents artificial convergence.
EM_verbose	Whether to print the log-likelihood during Expectation-Maximization.
optim_verbose	Whether to print log-likelihooods during numerical optimization.
npd	Whether to find the nearest positive-definite matrix for all covariance matrices during numerical optimization (slow – only set to TRUE if converging to singular matrices).
nested_optim	Only relevant if fitting a univariate alternative evolutionary model. Tries multiple tree transformation parameter values along the range of possible values to make informed starting parameters. Slower than the default (nested_optim=FALSE), in which all parameters are estimated simultaneously.
usezscores	Whether or not ot use centered and standardized data during numerical optimization (recommended).
phenocov_list	An optional named list of species-specific within-species covariance matrices to be held fixed, as in Ives et al (2007). This option forces pheno_error and pheno_correlated to be FALSE, and uses mean species values instead of raw data. Raw variance should be divided by the number of observations per species (i.e., squared standard errors). See Ives et al (2007) for more details.
ret_args	For internal use only.
ret_level	For internal use only.
get_cov_CIs	Whether to return 95-percent confidence intervals of covariance parameters (default=FALSE).

Value

An object of class phylopars. For models with phenotypic (within-species) covariance, the estimated percentage of variance explained by the phylogeny is provided as 100*(1 - phenotypic_variance/raw_variance), where raw_variance is the variance of all observations for a given trait across species (var(PPE$trait_data[,2:ncol(PPE$trait_data)],na.rm=TRUE)).

logLik	The log-likelihood of the model
pars	A list composed of phylogenetic trait covariance and phenotypic (within-species) trait covariance, if estimated
model	The model of evolution (e.g., BM, OU, lambda, etc.), and any additional evolutionary model parameters estimated. For OU models, stationary covariance is calculated from both phylogenetic covariance (Sigma) and alpha (see Supplement 1 of Clavel et al. 2015).
mu	The estimate ancestral state at the root of the tree.
npars	The total number of parameters estimated by optimization (used for AIC and BIC).
anc_recon	Reconstructed ancestral states and species means. Row names correspond to species names (for the first 1:nspecies rows), and the remaining row names correspond to node numbers on a tree with edges in postorder: reorder(tree,"postorder"). Or, if node labels were included on the original tree, row names correspond to node labels.
anc_var	Variance of reconstructed ancestral estimates and imputed species means.
anc_cov	Covariance of estimates among variables.
tree	The phylogenetic tree supplied to phylopars
trait_data	The trait data supplied to phylopars
REML	TRUE if REML, FALSE if ML.

Author(s)

Eric W. Goolsby [e-mail], Cecile Ane, Jorn Bruggeman

References

Bruggeman J, Heringa J and Brandt BW. (2009) PhyloPars: estimation of missing parameter values using phylogeny. Nucleic Acids Research 37: W179-W184. Clavel, J., Escarguel, G. & Merceron, G. (2015) mvmorph: an r package for fitting multivariate 261 evolutionary models to morphometric data. Methods in Ecology and Evolution, 6, 131-1319. Felsenstein, J. (2008) Comparative methods with sampling error and within-species variation: contrasts revisited and revised. American Naturalist, 171, 713-725. Ho L.S.T., Ane C. 2014. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63:397-408.

Examples

  # simulate data
  sim_data <- simtraits(ntaxa = 15,ntraits = 4,nreps = 3,nmissing = 10)

  # estimate parameters under Brownian motion
  # pheno_error = TRUE assumes intraspecific variation
  # pheno_correlated = FALSE assumes intraspecific variation is not correlated
  # phylo_correlated = TRUE assumed traits are correlated

  PPE <- phylopars(trait_data = sim_data$trait_data,tree = sim_data$tree,
    pheno_error = TRUE,phylo_correlated = TRUE,pheno_correlated = TRUE)

  PPE

  PPE$anc_recon # Ancestral state reconstruction and species mean prediction
  PPE$anc_var # Prediction variance


  ###NOT RUN
  # estimate parameters under multivariate OU
  # PPE_OU <- phylopars(trait_data = sim_data$trait_data,tree = sim_data$tree,
  #    model="mvOU",pheno_error = TRUE,phylo_correlated = TRUE,
  #    pheno_correlated = TRUE)
  #
  # PPE

Rphylopars documentation built on May 29, 2024, 7:08 a.m.