- Self-study course.
- On-demand access to all video content online within a 12-month period.
- Daily interaction on the Discussion Board for detailed questions.
- Live chat for quick queries.
- Course fee includes a 1-hour video chat with instructors for personalized questions and data assistance.
- Analysis of count data, continuous data, and proportional data with an excessive number of zeros.
- Applying zero-inflated Poisson, negative binomial, generalised Poisson, binomial, and beta GLMs and GLMMs using glmmTMB.
- Applying Tweedie GLM(M)s and hurdle models using glmmTMB.
- Bonus material: If we were to design a similar field study or experiment, how many clusters, and how many observations per cluster should we sample?
- General introduction.
- Short revision of data exploration and linear regression in R.
- Introduction to matrix notation.
- Revision Poisson GLM for the analysis of count data.
- Introducing the negative binomial, generalised Poisson, and Conway-Maxwell-Poisson GLMs for the analysis of count data.
- Model validation using DHARMa.
- Theory presentation on zero-inflated models.
- Three exercises using the zero-inflated GLMs for the analysis of data sets with an excessive number of zeros in the count data.
- Theory presentation on hurdle models for the analysis of zero-inflated count data. This presentation also covers zero-truncated models.
- One exercise using zero-altered Poisson and zero-altered negative binomial models for the analysis of count data with an excessive number of zeros.
- Theory presentation on the GLM with the Tweedie distribution.
- Application of a Tweedie GLM on zero-inflated continuous data. We will also explain the zero-altered Gamma model.
- Revision of linear mixed-effects models.
- Exercise on linear mixed-effects models.
- Exercise using a zero-inflated Poisson GLMM to analyse count data.
- Exercise using a zero-inflated negative binomial GLMM to analyse count data.
- Exercise using a zero-inflated binomial GLMM to analyse proportional data.
- Exercise using a zero-inflated beta GLMM to analyse proportional.
- Exercise using a Tweedie GLMM and a zero-altered Gamma GLMM to analyse continuous data with an excessive number of zeros.
- If we were to design a similar field study or experiment, how many clusters, and how many observations per cluster should we sample?
Working knowledge of R, data exploration, linear regression and GLM (Poisson and Bernoulli). This is a non-technical course. Short revisions are provided.