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2019 Lecture/Exercise 4.6

Lecture/Exercise 5.1

Making sense of your output: assessing confidence in model selection and parameters

Marguerite Butler

 

Lecture projections

Some thoughts on development and limitations on applying the OU models, and some recommendations on how to interpret results.

Projections:

Testing Adaptation with Evolutionary Models
Massive Simulation Take Aways

Exercise materials

OU_bimac_parametricBootstrap.R

 

Audio recording

Lecture4-6.WMA

Lecture4-6.mp3

 

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:
957–981.

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.

2019 Lecture/Exercise 4.5

Lecture/Exercise 4.6

Measurement error, identifiability, and model adequacy

Brian O’Meara

 

Lecture projections

Presentation: PDF and Powerpoint

 

Exercise materials

You may need to install the following packages:

install.packages(c(“OUwie”, “plyr”, “knitr”, “ggplot2”, “rmarkdown”))

MeasurementError.Rmd

You can create the object using the knit button in an Rstudio window or rmarkdown::render(“MeasurementError.Rmd”) in R.

Audio recording

Lecture4-5.WMA

Lecture4-5.mp3

 

2019 Lecture/Exercise 4.3

Lecture 4.4

Usefulness of Brownian or OU simulation

Samantha Price

Lecture projections

UsefulnessofSimulation.pdf

Exercise materials

You will need this: SimulationExercise.pdf

You may need these:

examplesimulations.R

parrotfishtree.nex

parrotfishmorphology.txt

parrotfishecology.txt

marsupialdiettree.nex

marsupialdietmass.txt

ouwiebootoutput.txt

Audio recording

Lecture4-3.WMA

Lecture4-3.mp3

 

2019 Lecture/Exercise 3.3

Lecture/Exercise 4.1

OU processes on phylogenies and their interpretation

Marguerite Butler and Brian O’Meara

 

Butler: Testing Hypotheses of Evolution by Varying the Model

Learning Objectives:

  • 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

Projection

Notes: R_BM_OUCH_minitutorial.pdf chapter 3

Code:

bmousim.R
OU.sim.branch.R

ou2drgl.R (optional, extra)

Audio recordings:

Lecture3-3a.WMA

Lecture3-3a.mp3

 

O’Meara:

Learning objectives

  • 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!

R packages

OUwie: install.packages(“OUwie”) or bleeding edge remotes::install_github(“thej022214/OUwie”).

Lecture projections
Presentation: PDF and PowerPoint

Exercise materials
O’Meara: Exercise and Answers

Audio recording

Lecture3-3b.WMA

Lecture3-3b.mp3