Rphylopars (version 0.3.10)

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

Description

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.

Author

Eric W. Goolsby, Jorn Bruggeman, Cecile Ane

Maintainer: Eric W. Goolsby

Details

Package:	Rphylopars
Type:	Package
Version:	0.3.10
Date:	2024-01-17
License:	GPL (>= 2)

References

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.

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