This workshop will introduce Generalized Linear Models (GLMs), which allow one to model non-Gaussian (i.e., non-normal) data.

By the end of this session, participants will be familiar with the three parts of GLMs (family of distribution, linear predictor, and link function) and will be able to decide what family of distributions and link function to choose for their data. They will also be able to interpret the output of the summary() function and diagnostic plots for (H)GLMs and recognize the limitations of (H)GLMs.

Embarking on the journey of writing your thesis or dissertation? This online workshop offers valuable tools and techniques to support your thesis writing process.

This workshop will introduce Generalized Additive Models (GAMs), which allow one to fit models that are complex and nonlinear but easily interpretable, unlike many “black-box” machine learning models.

By the end of this session, participants will be able to fit GAMs in R using the mgcv package and understand the advantages of GAMs over GLMs and LMs.

This workshop will show how to interpret GAMs and how to use GAMs to make publication-level figures.

By the end of this session, participants should be able to interpret GAMs and the output of the summary() function, predict from GAMs, and make figures using GAMs.

Join us on an exciting journey into the world of data with our introductory workshops! This session will introduce participants to common tasks of initial data exploration.

Do you want to learn how to use copyrighted material in your research, and how to protect your own rights as an author? If so, this workshop is for you!

Explore the variety of ways that data can be filtered and subset in R. Working with data frames and vectors, data frames, and lists, participants will learn the nuances of extracting portions of a larger data set for analysis.

This workshop will re-visit random and fixed effects with Hierachical GAMs (HGAMs) and expand the concepts of random slopes by introducing random smooths. The workshop will also cover smooth, nonlinear interaction terms via the ti() and te() functions.

By the end of this session, participants should be able to fit HGAMs with smooth interaction terms, plot and interpret the models.

Join our online workshop, where we'll unveil strategies for crafting the methods and results sections of your thesis.