# Online self-study course 4: Introduction to Zero-Inflated GLMs and GLMMs.

£ 450.00 each

Course format:

• 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.

Key components:

• 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?

Detailed outline:

Module 1

• 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.

Module 2

• 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.

Module 3

• 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.

Module 4

• 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.

Module 5

• 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.

Module 6

• If we were to design a similar field study or experiment, how many clusters, and how many observations per cluster should we sample?

Pre-required knowledge:
Working knowledge of R, data exploration, linear regression and GLM (Poisson and Bernoulli). This is a non-technical course. Short revisions are provided.