The course is aimed at biologists with a basic to moderate knowledge in R.
Delivered by Dr. Luc Bussiere and Dr. Tom Houslay, the course content is designed to bridge the gap between basic R coding and more advanced statistical modelling. This five day course will consist of a series of modules, each lasting roughly half a day and comprised of lectures and practicals designed to either build required skills for future modules or to perform a family of analyses that is frequently encountered in the biological literature.
Course content is as follows
Day 1 Course introduction
- Techniques for data manipulation, aggregation, and visualisation; introduction to linear regression. Packages: {tidyr}, {dplyr}, {ggplot2}
Day 2 Linear models
- Diagnostics, collinearity, scaling, plotting fitted values); fitting and interpreting interaction terms; model selection and simplification; general linear models and ANCOVA.
- Packages: {stats}, {car}
Day 3 Generalized linear models
- Logistic and Poisson regression; predicting using model objects and visualizing model fits.
- Packages: {broom}, {visreg}, {ggplot2}
Day 4 Mixed effects models
- Theory and practice of mixed effect models; visualising fixed and random effects.
- Packages: {lme4}, {broom}, {ggplot2}, {sjPlot}
Day 5 Fitting nonlinear functions
- Polynomial & Mechanistic models; brief introduction to more advanced topics & combining methods (e.g., generalised linear mixed effects, nonlinear mixed effects, and zero-inflated and zero-altered models).
- Packages: {nlsTools}.
- Afternoon to discuss own data if time permits
Please email any enquiries to oliverhooker@prstatistics.com or visit the website