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Analyzing Time Series Data Using Generalized Additive Modeling


Analyzing Time Series Data Using Generalized Additive Modeling

This course will provide a hands-on introduction to Generalized Additive Modeling (GAMs). We will introduce methods to identify the best model given the data, and demonstrate how to visualize non-linear effects and non-linear interactions. In addition, we will address several potential problems, including, but not limited to, dealing with the common problem (for time series data) of encountering autocorrelation in the residuals of a model.

In the first part of each lecture, the theory is explained by using real experimental data to illustrate the potential of GAMs for several types of data, including articulography (tongue movement) data, eye tracking (gaze) data, pupil dilation data, and ERP (EEG) data. In the second part of each lecture, students can experiment with the methods themselves by analyzing a comparable data set on their own laptop.

Participants of the course are invited to bring their own time series data to see whether this method can help to answer their research questions. 


Course Status: Closed

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First two-week Session


Tuesday: 8:30 am-10:20 am
Friday: 8:30 am-10:20 am


We expect that students know how to use R: how to import data, manipulate data frames, and  some basic knowledge of plotting. We also expect some basic experience with and understanding  of linear mixed-effects regression (e.g., using the lme4 library in R). Students need to bring a laptop with R installed as well as the most recent version of the package mgcv.