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.
W&S = Whitlock and Schluter, G&W = Grolemund and Wickham, U/P for linked pdfs = biol607
Lecture: How do we use data to understand how the world works?
Lab: Intro to R. Matrices, Lists and Data Frames. Introduction to Markdown
Reading: G&W Preface, Intro, Workflow basics, Vectors, and Markdown Chapters, RMarkdown Cheat Sheet
In Class Code: R Intro Code from Lab
Lecture: Sampling and Simulation for Estimation. Descriptive statistics, and the creation of good observational sampling designs.
Lab Topic: Sampling and simulation. Libraries in R. Dplyr.
Reading: W&S 1,3-4, G&W Chapter 5, 11, 18, Dplyr cheat sheet
Optional Reading: Cumming et al. 2007 on SDs, SEs, and CIs, Simpler Coding with Pipes, Managing Data Frames with the Dplyr package
In Class Code: Code from Lab
Lecture: Data visualization.
Lab Topic: Data import and introduction to ggplot2. Forcats and factors. Data for lab here.
Reading: W&S Chapter 2, Unwin 2008, G&W Chapters on Data Vizualization and Graphics for Communication, Ggplot2 cheat sheet
Optional Reading: Friendly 2008 on History of Data Viz
In Class Code: Loading Data, Intro to ggplot2
Lecture: Frequentist Hypothesis Testing, NHST, Z-Tests, and Power
Lab Topic: Distributions in R, Frequentist Hypothesis testing via simulation
Reading: W&S 5-7, G&W Chapter 7, 16, Abraham Lincoln and Confidence Intervals and links therein
In Class Code: Distributions and Power
In Class Code: Distributions and Power
Lecture: T tests, χ2 tests, and p
Lab Topic: Statistical analysis functions for t and \(\chi^2\) in R, data
Reading: W&S 8-12, G&W Chapter 10, 20
Discussion Reading: ASA Statement on P-Values, And choose one of the accompanying rejoinders (sign up in here) (also feel free to read them all) Additional Readings on P-Values: Peaceful negotiation in the face of so-called ‘methodological terrorism’, P-value madness: A puzzle about the latest test ban (or ‘don’t ask, don’t tell’), The Paradox of Replication, and the vindication of the P-value (but she can go deeper)
In Class Code: t and chi square
Lecture: Least Squares Linear Regression: Correlation and Regression, Fit and Power
Lab Topic: Linear regression, diagnostics, visualization, and data
Reading: W&S 16-17, G&W on model basics, model building
In Class Code: lm
Lecture: Linear Model Power Analysis, Likelihood, Fitting a line with Likelihood
Lab Topic: Calculating and visualizing Likelihoods, fitting a line with bbmle
Reading: W&S 20, G&W Chapter Iteration
In Class Code: power analysis, linear models with likelihood
Lecture: Bayesian Inference, Fitting a line with Bayesian techniques
Lab Topic: Bayesian computation in R, Fitting a line with Bayesian techniques
Reading: Ellison 1996, Statistical Rethinking Chapter 1 and Chapter 2, R Users will Now Inevitably Become Bayesians
Additional Reading on rstanarm: How to use it, Linear Models in rstanarm, more vignettes, rstanarm and more, Bayesian basics with R
In Class Code: Bayesian Data Analysis
Lecture: Joins, Tidy data
Reading: 10 Commandments for Good Data Managament, G&W Chapters on tidy data, Strings, and Dates
In Class Code: Tidy, markdown options
Lectures: Experimental Design in a Multicausal World - Multiway ANOVA, Factorial ANOVA
Lab Topic: Discussion of Hurlbert, Factorial ANOVA
__Lab Data:__ Multiple Files
Reading: W&S 18, Hurlbert 1984, Cottingham et al. 2005
In Class Code: lots of anova
Lecture: The General Linear Model: ANCOVA, Multiple Regression, and Interaction Effects, Information Theoretic Approaches
Lab Topic: Multiple Regression, Multimodel Inference - data files
Readings: Symonds and Moussalli 2010
Optional Readings: The whole Ecology Special Section on P Values is incredible reading.
In Class Code: ancova, mlr, and aic
Lecture: Entering a non-normal world - Modeling count data with Genearlized linear models. Overdispersed continuous data.
Lab Topic: Generalized Linear Models. Diagnostics with DHARMa.
Reading: O’Hara 2009 through section on GLMs, O’Hara and Kotze 2010, Wharton and Hui 2011, Hartig DHARMa vignette
Lecture: Class’s Choice
Lab Topic: Class’s Choice, Final Presentation Open Lab
Lecture: Final Presentations