This month we’ve got two great talks lined up, so we hope you’ll join us in hearing about Shiny apps, and
gamms models. The meeting will be held on Wednesday 18th of October, at 5.30pm, in G.06, 50 George Square (on the ground floor - see map below). As usual, the meeting will be followed by drinks and chat in the Potting Shed. Meetings are open for all to attend and newcomers / beginners are very welcome.
Our first speaker is Dr. Riinu Ots, who works as Data Manager at the Surgical Informatics Research Group. She will be telling us about:
Shiny reporting apps for crowdsourced data projects (get slides here)
My presentation includes a very brief but practical introduction to Shiny and how to make your first Shiny app. I will then introduce some of the Shiny apps we at the Surgical Informatics Research Group have written to help manage, evaluate, and report on our globally crowdsourced surgical outcomes data projects (GlobalSurg). The Shiny apps presented at this talk are publicly available at my GitHub and can be applied on different projects and datasets.
Our second speaker is Dr. Josef Fruehwald, who works as lecturer in Linguistics and English Language, at the School of Philosophy, Psychology and Language Sciences (PPLS, University of Edinburgh). He will be discussing:
Studying Pronunciation Changes with gamms (slides here)
Pronunciations play out across two radically different time scales. The first is on the order of milliseconds, from the beginning of pronouncing a speech sound to the end, during which your tongue and other articulators carry out carefully detailed and coordinated gestures. The second is across generational time, as the conventional pronunciation of some sounds very gradually shifts. In this talk, I’ll be presenting work I have recently been doing to try to model and understand these two time domains of pronunciation simultaneously using generalized additive mixed effects models (gamms). I’ll briefly cover how it is possible to engage in this kind of research through the use of archival recordings, how to specify a gamm model to account for the random effects structure and autocorrelation of measurements, and (if there’s time) how to simulate samples from the posterior to estimate credible intervals for parameters of interest.
For any newcomers, here’s a map of where we’ll be.
See you there!