Introduction to Generalised Linear Models (GLME01) Shape data rarely follow normal distributions—and that’s where *Generalised Linear Models (GLMs)* shine. Whether you're analysing morphological variation, shape–function relationships, or environmental influences on form, GLMs offer a flexible, interpretable way to model non-normal outcomes commonly encountered in geometric morphometrics. What you'll learn
- How to use GLMs to model binary outcomes (e.g. trait presence), counts (e.g. number of cusps, spines), and proportions derived from shape data - Practical use of the glm() function in R to relate shape variables or morphometric scores (e.g. PC axes) to explanatory variables - How to handle overdispersion, non-normality, and zero-inflated data—common challenges in morphometric analyses - Model diagnostics, interpretation, and visualisation strategies for communicating findings clearly Course format - Live online sessions over 10 days, 4 hours a day, combining lectures and hands-on coding in R - Recordings available for all sessions—accessible across time zones - Next session: September 8-12 & 15-19, 2025 - Course fee: First 10 places £400 - Normal price £450 Prerequisites - Experience using R and RStudio for data import, manipulation, and basic plotting - Understanding of basic statistics (mean, variance, correlation, linear regression) - No prior experience with GLMs required—concepts are introduced from first principles, with an emphasis on practical interpretation over complex mathematics ------------------------------ *Register now or learn more!* For questions or group bookings, contact: *[email protected]* -- Oliver Hooker PhD. PR stats -- You received this message because you are subscribed to the Google Groups "Morphmet" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion visit https://groups.google.com/d/msgid/morphmet2/CAEsSYzzb1btRLzJCpAr%2Bqp7kQwMjn8M9cMwC2PT%3DbZjEQZeARA%40mail.gmail.com.
