Introduction To Generalised Linear Models Using R And Rstudio (IGLMPR)

Pre Recorded

In this two day course, we provide a comprehensive practical and theoretical introduction to generalized linear models using R. Generalized linear models are generalizations of linear regression models for situations where the outcome variable is, for example, a binary, or ordinal, or count variable, etc. The specific models we cover include binary, binomial, ordinal, and categorical logistic regression, Poisson and negative binomial regression for count variables. We will also cover zero-inflated Poisson and negative binomial regression models. On the first day, we begin by providing a brief overview of the normal general linear model. Understanding this model is vital for the proper understanding of how it is generalized in generalized linear models. Next, we introduce the widely used binary logistic regression model, which is is a regression model for when the outcome variable is binary. Next, we cover the ordinal logistic regression model, specifically the cumulative logit ordinal regression model, which is used for the ordinal outcome data. We then cover the case of the categorical, also known as the multinomial, logistic regression, which is for modelling outcomes variables that are polychotomous, i.e., have more than two categorically distinct values. On the second day, we begin by covering Poisson regression, which is widely used for modelling outcome variables that are counts (i.e the number of times something has happened). We then cover the binomial logistic and negative binomial models, which are used for similar types of problems as those for which Poisson models are used, but make different or less restrictive assumptions. Finally, we will cover zero inflated Poisson and negative binomial models, which are for count data with excessive numbers of zero observations.

This course is aimed at anyone who is interested in using R for data science or statistics. R is widely used in all areas of academic scientific research, and also widely throughout the public, and private sector.

Last Up-Dated – 04:11:2021

Duration – Approx. 15 hours

ECT’s – Equal to 1 ECT’s

Language – English

There will be morning lectures based on the modules outlined in the course timetable. In the afternoon there will be practicals based on the topics covered that morning. Data sets for computer practicals will be provided by the instructors, but participants are welcome to bring their own data.

A basic understanding of statistical concepts. Specifically, generalised linear regression models, statistical significance, hypothesis testing.

Familiarity with R. Ability to import/export data, manipulate data frames, fit basic statistical models & generate simple exploratory and diagnostic plots.

A laptop computer with a working version of R or RStudio is required. R and RStudio are both available as free and open source software for PCs, Macs, and Linux computers. R may be downloaded by following the links here https://www.r-project.org/. RStudio may be downloaded by following the links here: https://www.rstudio.com/.

All the R packages that we will use in this course will be possible to download and install during the workshop itself as and when they are needed, and a full list of required packages will be made available to all attendees prior to the course.

A working webcam is desirable for enhanced interactivity during the live sessions, we encourage attendees to keep their cameras on during live zoom sessions.

Although not strictly required, using a large monitor or preferably even a second monitor will improve he learning experience

**PLEASE READ – CANCELLATION POLICY**

Cancellations/refunds are accepted as long as the course materials have not been accessed,.

There is a 20% cancellation fee to cover administration and possible bank fess.

If you need to discuss cancelling please contact oliverhooker@prstatistics.com.

**If you are unsure about course suitability, please get in touch by email to find out more**** ****oliverhooker@prstatistics.com**

**Day 1 – approx. 6 hours**

Topic 1: The general linear model. We begin by providing an overview of the normal, as in normal distribution, general linear model, including using categorical predictor variables. Although this model is not the focus of the course, it is the foundation on which generalized linear models are based and so must be understood to understand generalized linear models.

Topic 2: Binary logistic regression. Our first generalized linear model is the binary logistic regression model, for use when modelling binary outcome data. We will present the assumed theoretical model behind logistic regression, implement it using R’s glm, and then show how to interpret its results, perform predictions, and (nested) model comparisons.

Topic 3: Ordinal logistic regression. Here, we show how the binary logistic regresion can be extended to deal with ordinal data. We will present the mathematical model behind the so-called cumulative logit ordinal model, and show how it is implemented in the clm command in the ordinal package.

Topic 4: Categorical logistic regression. Categorical logistic regression, also known as multinomial logistic regression, is for modelling polychotomous data, i.e. data taking more than two categorically distinct values. Like ordinal logistic regression, categorical logistic regression is also based on an extension of the binary logistic regression case.

**Day 2 – approx. 6 hours**

Topic 5: Poisson regression. Poisson regression is a widely used technique for modelling count data, i.e., data where the variable denotes the number of times an event has occurred.

Topic 6: Binomial logistic regression. When the data are counts but there is a maximum number of times the event could occur, e.g. the number of items correct on a multichoice test, the data is better modelled by a binomial logistic regression rather than a Poisson regression.

Topic 7: Negative binomial regression. The negative binomial model is, like the Poisson regression model, used for unbounded count data, but it is less restrictive than Poisson regression, specifically by dealing with overdispersed data.

Topic 8: Zero inflated models. Zero inflated count data is where there are excessive numbers of zero counts that can be modelled using either a Poisson or negative binomial model. Zero inflated Poisson or negative binomial models are types of latent variable models.

**Dr. Mark Andrews****Works At**

Senior Lecturer, Psychology Department, Nottingham Trent University, England**Teaches**- Free 1 day intro to r and r studio (FIRR)
- Introduction To Statistics Using R And Rstudio (IRRS03)
- Introduction to generalised linear models using r and rstudio (IGLM)
- Introduction to mixed models using r and rstudio (IMMR)
- Nonlinear regression using generalized additive models (GAMR)
- Introduction to hidden markov and state space models (HMSS)
- Introduction to machine learning and deep learning using r (IMDL)
- Model selection and model simplification (MSMS)
- Data visualization using gg plot 2 (r and rstudio) (DVGG)
- Data wrangling using r and rstudio (DWRS)
- Reproducible data science using rmarkdown, git, r packages, docker, make & drake, and other tools (RDRP)
- Introduction/fundamentals of bayesian data analysis statistics using R (FBDA)
- Bayesian data analysis (BADA)
- Bayesian approaches to regression and mixed effects models using r and brms (BARM)
- Introduction to stan for bayesian data analysis (ISBD)
- Introduction to unix (UNIX01)
- Introduction to python (PYIN03)
- Introduction to scientific, numerical, and data analysis programming in python (PYSC03)
- Machine learning and deep learning using python (PYML03)
- Python for data science, machine learning, and scientific computing (PDMS02)

Personal website

Google Scholar

Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.