While the topics covered are broad, each week will feature different examples from genetics, ecology, molecular, and evolutionary biology highlighting uses of each individual set of techniques. Each topic will have accompanying readings highlighting a general introduction to a technique (required), and one or more general references or examples. For access to blocked readings, biol609.
To see the code of a lecture, load the slides, then change the file extension in your browser bar to .Rmd to get the code. Or go to this link for all of the code.
1/28/2019
Lecture: Why Advanced Data Analysis?, Re-Introduction to Bayes
Reading: McElreath Ch. 1-2
Etherpad: https://etherpad.wikimedia.org/p/609-bayes-2019
2/4/2019
Lecture: Sampling from your Posterior, Linear Bayesian Models,
Reading: McElreath Chapter 3-4, Bayesian Basics
Etherpad: https://etherpad.wikimedia.org/p/609-linreg-2019
Homework: HW1
2/11/2019
Lecture: Multiple Predictor Variables (including Categoricals), Bayesian Multimodel Inference
Reading: McElreath Chapter 5-6
Further Reading: Aho et al 2013 Ecology, Gelman et al. 2013 on WAIC and LOO
Etherpad: https://etherpad.wikimedia.org/p/609-mmi-2019
2/18/2019
Lecture: Interaction Effects, Markov Chain Monte-Carlo Approaches
Reading: McElreath Chapter 7-8
Etherpad: https://etherpad.wikimedia.org/p/609-mcmc_entropy-2019
2/25/2019
Lecture: MaxEnt and GLMs - a review, Bayesian GLMs, GLM lab
Reading: McElreath Chapter 9-10, fitting Gamma models multiple ways, Gamma hurdle model
Further Readings: O’Hara 2009 through section on GLMs, O’Hara and Kotze 2010, Wharton and Hui 2011, Hartig DHARMa vignette
Files: usethis::use_course("https://biol609.github.io/lectures/glm_data.zip")
Etherpad: https://etherpad.wikimedia.org/p/609-glm-2019
3/11/2019
Lecture: Overdispersed Models, Zero Inflated Models
Reading: McElreath Chapter 11, Introduction to glmmTMB, ver Hoef and Boveng 2007 on overdispersion, Roset et al 2006 on Overdispersion
Further Reading: Zuur chapter on zero inflation, Getting started with glmmTMB, [Troubleshooting with glmmTMB]
Files: Fishing Duration CSV
Etherpad: https://etherpad.wikimedia.org/p/609-zig_zag-2019
3/18/2019
Lecture: Random Effects, Mixed Models
Etherpad: https://etherpad.wikimedia.org/p/609-ranef-2019
Reading: Gelman and Hill Ch. 12 and/or Zuur on Random Effects, A Practical Guide to Generalized Linear Mixed Models
Writings on visualization: Random regression coefficients using lme4, Making mixed model plots look fancy
A Little More Reading: R2 for mixed models (from Jon Lefcheck), lme4 converge warnings and solutions
Your One Stop FAQ: Ben Bolker’s Mixed Model’s FAQ
Files: Mussels, Uneven Mussels, RIKZ Beach data, growth data
R Packages: lme4, lmerTest, merTools, RLRsim, cAIC4
3/25/2019
Lecture: Varying Intercept Mixed Models in a Bayesian Context, Varying Slope Models
Etherpad: https://etherpad.wikimedia.org/p/609-mixed_bayesian-2019
Reading: McElreath Chapter 12, 13
Further Reading: Gelman and Hill Ch. 12 (see the bit on one model written 5 ways), Gelman and Hill Ch. 13, Gelman on multiple comparisons in mixed models
4/1/2019
Lecture: brms and Prediction with Mixed Models, Group-Mean Centering and Other Model Structures
Etherpad: https://etherpad.wikimedia.org/p/609-mixed_bayesian-2019
Reading: McNeish and Kelly 2018, Causal Model Structures and Random Effects, Poe Lecture Slides, Bell et al. 2018a, Bell et al 2018b
4/8/2019
Lecture: Temporal Autocorrelation, Spatial Autocorrelation
Etherpad: https://etherpad.wikimedia.org/p/609-autocorrelation-2019
Reading: Zuur et al. on temporal autocorrelation, Why OLS is an Unbiased Estimator for GLS, Zuur et al. on spatial autocorrelation, Hawkings on the 8 1/2 deadly sins of spatial data analysis
Further Reading: Intro to INLA for spatial and spatiotemporal modeling, Spatial Data Analysis with R-INLA, inlabru: a package for Baysianspatial modelling from ecological survey data, Introduction to INLA, more R-INLA examples, Hierarchical analysis of spatially autocorrelated ecological data using integrated nested Laplace approximation with code appendix
Files: Birds, Plankton, Boreal Forests, Irish EPA Data R Packages: nlme
4/15/2019
Lecture: Gaussian Proces Models, Generalized Additive Models (GAMs)
Etherpad: https://etherpad.wikimedia.org/p/609-beyond-2019
Files: predicted GP script
Reading: Spatial autoregressive models for statistical inference from ecological data, McElreath Chapter 14, Roberts et al. 2012, GAMs: an Introduction, Michael Clark’s GAM Introduction, Hierarchical Generalized Additive Models in ecology: an introduction with mgcv
Further Readings: Methods to account for spatial autocorrelation in the analysis of species distributional data: a review
4/22/2019
Lecture: Measurement Error and Missing Data, Model II and Quantile Regression
Etherpad: https://etherpad.wikimedia.org/p/609-alt_regression-2019
Reading: Warton et al. SMATR, Warton et al. 2006 Model II Review, Cade and Noon 2003 on Quantile Regression, Brennen et al. 2015 quantile regression example, Colonescu Ch. 8 on Heteroskedasticity
Files: Elk data, Clam data
R Packages: Quantreg
4/29/2019
Lecture: Causal Modeling
Readings: A Second Change to Get Causal Inference Right, Causal analysis in control impact studies, Pearl 2010
Further Reading: Causal Inference
5/6/2019
Lecture: Intro to Structural Equation Models, SEM Implementation
Etherpad: https://etherpad.wikimedia.org/p/609-sem-2019
Reading: Grace et al 2012 Ecosphere, Lefcheck 2016
_Further Reading: Notes from Jon Lefcheck
Helpful Websites: piecewiseSEM, lavaan, Jim Grace’s Tutorials
5/13/2019
Final Work Party Week!
Final Presentations: Friday May 17th
Final Papers Due: May 21st