# Online course: Introduction to Zero Inflated Models. Using frequentist tools

This online course consists of 5 modules representing a total of approximately 40 hours of work. Each module consists of video files with short theory presentations, followed by exercises using real data sets, and video files discussing the solutions and R code. All video files are on-demand and can be watched online, as often as you want, at any time of the day, within a 12 month period.

You can ask course-related questions on the Discussion Board or in a live chatbox. The course fee includes a 1-hour face-to-face video chat with the instructors. During this meeting, you can ask any questions (e.g. about your own data analysis).

**Course format**

Some universities and institutes organise it as a 5-day 'Live online teaching' course for 20-25 participants, typically from 09.00-16.00 (times may differ per university).

Highland Statistics also runs it once or twice per year as an open online course.

You can also do this course with self-study.

**Course content**

The course starts with a short revision of data exploration, multiple linear regression and Poisson GLM. We then discuss 3 more models for the analysis of count data, namely the negative binomial, generalised Poisson and Conway-Maxwell-Poisson GLMs. After a short theory presentation in which we explain how to extend these models towards zero-inflated models, we apply them to various data sets. We also use the Tweedie GLM and the zero-altered Gamma GLM for the analysis of zero-inflated continuous data. In the second part of the course, we start with a short revision of linear mixed-effects models. This is followed by a series of exercises in which we analyse zero-inflated count data, continuous data, and proportional data using zero-inflated GLMMs.

Throughout the course we will use the glmmTMB package in R.

**Keywords**: Zero-inflated GLMs. Zero-inflated GLMMs with random effects. Overdispersion and solutions. GLMs, GLMMs, and zero-inflated GLMMs using the Poisson, negative binomial, generalised Poisson, Conway-Maxwell-Poisson, Tweedie, Gamma, beta and binomial distributions for count data, continuous data, and proportional data with an excessive number of zeros. Dependency. Pseudo-replication. glmmTMB.

**Detailed outline**

A detailed outline of the course is provided below. All exercises consist of a data set, a video describing the data and the questions, R solution code, and a video discussing the R solution file.

**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 (NB), generalised Poisson (GP), and Conway-Maxwell-Poisson GLMs for the analysis of count data.
- Model validation using DHARMa.
- Module 1 consists of 6 on-demand videos,

**Module 2**

- Theory presentation on zero-inflated models.
- Three exercises using zero-inflated Poisson, zero-inflated negative binomial, zero-inflated generalised Poisson, and zero-inflated Conway-Maxwell GLMs for the analysis of data sets with an excessive number of zeros in the counts.
- Module 2 consists of 4 on-demand videos,

**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 a 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 3 consists of 5 on-demand videos,

**Module 4**

- Revision of linear mixed-effects models and one exercise.
- Exercise using a zero-inflated Poisson GLMM to analyse count data.
- Exercise using a zero-inflated negative binomial GLMM to analyse count data.
- Module 4 consists of 4 on-demand videos,

**Module 5**

- Exercise using a zero-inflated binomial GLMM to analyse proportional data with an excessive number of zeros.
- Exercise using a zero-inflated beta GLMM to analyse proportional data with an excessive number of zeros.
- Exercise using a Tweedie GLMM and a zero-altered Gamma GLMM to analyse continuous data with an excessive number of zeros.
- What to present in a paper.
- Module 5 consists of 4 on-demand videos,

**Free 1-hour face-to-face video meeting**: The course fee includes a 1-hour face-to-face video meeting with one or both instructors. The meeting needs to take place within 6 months after the last live zoom meeting. You can discuss your own data but the statistical topics need to be within our field of expertise. The 1-hour needs to be consumed in one session and will take place at a mutually convenient time.

**Discussion Board**: You can use the Discussion Board to ask any questions related to the course material.

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

**Cancellation policy**: What if you are not able to participate? Once participants are given access to course exercises with R solution codes, pdf files of certain book chapters, pdf files of PowerPoint or Prezi presentations and video solution files, all course fees are non-refundable and non-transferable to another participant.

**Copyright**: Sharing the access details of the course website or the pdf files of our course material is prohibited. Video files cannot be downloaded, but they can be watched in the same way as on **Netflix**.