Rphylopars-package | R Documentation |
Tools for performing phylogenetic comparative methods for datasets with with multiple observations per species (intraspecific variation or measurement error) and/or missing data (Goolsby et al. 2017). Performs ancestral state reconstruction and missing data imputation on the estimated evolutionary model, which can be specified as Brownian Motion, Ornstein-Uhlenbeck, Early-Burst, Pagel's lambda, kappa, or delta, or a star phylogeny.
Package: | Rphylopars |
Type: | Package |
Version: | 0.3.10 |
Date: | 2024-01-17 |
License: | GPL (>= 2) |
Eric W. Goolsby, Jorn Bruggeman, Cecile Ane
Maintainer: Eric W. Goolsby eric.goolsby.evolution@gmail.com
Bruggeman J, Heringa J and Brandt BW. (2009) PhyloPars: estimation of missing parameter values using phylogeny. Nucleic Acids Research 37: W179-W184.
Goolsby EW, Ane C, Bruggeman J. 2017. "Rphylopars: Fast Multivariate Phylogenetic Comparative Methods for Missing Data and Within-Species Variation." Methods in Ecology & Evolution. 2017. 8:22-27.
Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.
# 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
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.