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Highland Statistics Ltd

On demand: Introduction to Zero Inflated Models using R-INLA

Online course with on-demand video and live Zoom meetings: Introduction to Zero Inflated Models using R-INLA

This online course consists of 5 modules representing a total of approximately 40 hours of work. Each module consists of multiple video files with short theory presentations, followed by exercises using real data sets, and video files discussing the solutions. All video files are on-demand and can be watched online, as often as you want, at any time of the day, within a 6 month period.

A discussion board allows for daily interaction between instructors and participants. The course also contains 5 2-hour live web meetings (max. 12 participants per meeting) in which we summarise the theory and the exercises. Attending these live web meetings is optional. We will run the web meetings in different time zones. You can also use a Discussion board to ask any questions on the course material. The course fee includes a 1-hour face-to-face video chat with the instructors. You can use this video chat to ask questions about your own data.

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.

Course content

Suppose you want to study hippos and the effect of habitat variables on their distribution. When sampling, you may count zero hippos at many sites and therefore zero-inflated models should be used.

During the course, several case studies are presented, in which the statistical theory for zero-inflated models is integrated with applied analyses in a clear and understandable manner. Zero-inflated models consist of two integrated GLMs and therefore we will start with a revision of GLM.

Zero-inflated GLMMs for nested data (repeated measurements, short time series, clustered data, etc.) are discussed in the second part of the course. We will focus on zero-inflated count data and zero-inflated continuous data.

All models are executed using R-INLA so that extensions towards zero-inflated temporal, spatial and spatial-temporal data can easily be made.

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.

Keywords: Zero-inflated GLMs. Zero-inflated GLMMs with random effects. Overdispersion and solutions. Poisson, negative binomial, Bernoulli and gamma distributions for count data and continuous data with an excessive number of zeros. Dependency. Pseudo-replication. Bayesian analysis. R-INLA.

 

Module 1: Revision linear regression and introduction to Bayesian statistics

  • A video with a general introduction.
  • Revision exercise for multiple linear regression.
  • A short video explaining basic matrix algebra.
  • A video presentation with an introduction to Bayesian statistics.
  • A video presentation showing the role of priors.
  • A video presentation explaining the basic principles of INLA.
  • One exercise showing how to execute a linear regression model in INLA.

Web meeting 1: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 1.

 

Module 2: Models for zero-inflated count data.

  • A video explaining the Poisson, negative binomial, Bernoulli and gamma distributions.
  • A video with a short revision of Poisson and negative binomial GLM for count data.
  • A video with a short revision of Bernoulli GLM for absence and presence data.
  • Exercise showing how to execute a Poisson and negative binomial GLM in R-INLA.
  • Exercise showing how to execute a Bernoulli GLM in R-INLA.
  • A video with a theory presentation on models for zero-inflated count data (ZIP, ZINB, ZAP, and ZANB models).
  • Two exercises on the analysis of zero-inflated count data using R-INLA.

Web meeting 2: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 2.

 

Module 3: Revision linear-mixed effects models and models for zero-inflated continuous data.

  • A video with a short revision of mixed-effects models.
  • An exercise showing how to fit a Poisson GLMM in R-INLA.
  • A video discussing models for zero-inflated continuous data.
  • One exercise on the analysis of zero-inflated continuous data using a GLMM in R-INLA.

Web meeting 3: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 3.

 

Module 4: GLMM applied to zero-inflated data.

  • A GLMM exercise on the analysis of zero-inflated and 1-way nested count data using R-INLA.
  • A GLMM exercise on the analysis of zero-inflated and 2-way nested count data using R-INLA.
  • A GLMM exercise on the analysis of zero-inflated count data with 1-way nested and crossed random effects using R-INLA.

Web meeting 4: A 2-hour web-meeting will be scheduled. In this web meeting, we will summarise module 4.

 

Module 5: A brief introduction to models with zero-inflation and spatial correlation.

  • A video presentation giving a brief overview of how to apply a zero-inflated model with spatial correlation. Note that this is only a brief overview.

Web meeting 5: A 2-hour web-meeting will be scheduled. In this web meeting, we will catch up with any remaining issues and we will also summarise module 5.

 

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 3 months after the last live zoom meeting. You can discuss your own data but the statistical topics need to be within the content of the course. The 1-hour needs to be consumed in one session and will take place at a mutually convenient time.

Web meetings: Web meetings are hosted on zoom.us. Click here for recommended internet speed (see the text under 'Recommended bandwidth for Webinar Attendees'). We will record the meetings and make them available on the course website.

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 GLM (Poisson, negative binomial, Bernoulli). 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.