Here is a link to photos taken in June 2019 by Steve Arnold and others
Applying all of this to your data
Marguerite Butler, Brian O’Meara, Samantha Price, Josef Uyeda, Steve Arnold, and Joe Felsenstein
This session consisted of small group discussions about individual topics chosen by the participants, about methods applicable to their data.
Making sense of your output: assessing confidence in model selection and parameters
Some thoughts on development and limitations on applying the OU models, and some recommendations on how to interpret results.
A Few References
Ané C. 2008. Analysis of comparative data with hierarchical autocorrelation. Ann. Appl. Stat. 2:1078–1102.
Boettiger C., Coop G., Ralph P. 2012. Is your phylogeny informative? Measuring the power of comparative methods. Evolution 66: 2240–2251.
Bonine K.E., Gleeson T.T., and Garland T. 1999. Sprint performance of phrynosomatid lizards, measured on a high-speed treadmill, correlates with hindlimb length. J. Zool. 40: 1–18.
Cressler C., Butler M.A., and King A. A. 2015. Detecting adaptive evolution in phylogenetic comparative analysis using the Ornstein-Uhlenbeck model. Sys. Bio. 64(6):953-968. DOI: 10.1093/sysbio/syv043
Ho L.S.T., Ané C. 2013. Asymptotic theory with hierarchical autocorrelation: Ornstein-Uhlenbeck tree models. Ann. Stat. 41:
Scales J.A., King A.A., and Butler M.A. 2009. Running for your life or running for your dinner: What drives fiber type evolution in lizard locomotor muscles? Am. Nat. 173: 543–553.
Measurement error, identifiability, and model adequacy
You may need to install the following packages:
install.packages(c(“OUwie”, “plyr”, “knitr”, “ggplot2”, “rmarkdown”))
You can create the object using the knit button in an Rstudio window or rmarkdown::render(“MeasurementError.Rmd”) in R.
Usefulness of Brownian or OU simulation
You will need this: SimulationExercise.pdf
You may need these:
OU processes on phylogenies and their interpretation
Marguerite Butler and Brian O’Meara
Butler: Testing Hypotheses of Evolution by Varying the Model
- Gain appreciation for how models can be used to test evolutionary hypotheses
- Building intuition about BM and OU processes by making your own simulations
- adding stochastic components to trait values through discrete time (sigma)
- adding trends toward optimal values (theta and alpha)
- adding branching
- changing the parameters along the tree
ou2drgl.R (optional, extra)
- Understand connection between OU methods
- Be able to compare models
- Understand potential problems with your particular analysis (more on this tomorrow)
- Parameter estimation for the win!
OUwie: install.packages(“OUwie”) or bleeding edge remotes::install_github(“thej022214/OUwie”).