Course Overview:
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 IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES
Email oliverhooker@prstatistiucs.com with any questions

Other upcoming courses

Species Distribution Modeling using R (SDMR04)
www.prstatistics.com/course/species-distribution-modeling-using-r-sdmr04/
21 September 2021 - 30 September 2021

Introduction to eco-phylogenetics and comparative analyses using R (ECPH01) This course will be delivered live https://www.prstatistics.com/course/introduction-to-eco-phylogenetics-and-comparative-analyses-using-r-ecph01/
22 September 2021 - 28 September 2021
Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)
www.prstatistics.com/course/multivariate-analysis-of-ecological-communities-in-r-with-the-vegan-package-vgnr03/
4 October 2021 - 8 October

Introduction to Data Wrangling and Data Visualization using R (DWDV01)
www.prstatistics.com/course/introduction-to-data-wrangling-and-data-visualization-using-r-dwdv01/
4 October 2021 - 8 October 2021

Species distribution modelling with Bayesian statistics in R (SDMB03)
www.prstatistics.com/course/species-distribution-modelling-with-bayesian-statistics-in-r-sdmb03/
6 December 2021 - 10 December 2021

Introduction to generalised linear models using R and Rstudio (IGLM04)

3 November 2021 - 4 November 2021

https://www.prstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm04/

Course Overview:

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 IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES

Email oliverhooker@prstatistiucs.com with any questions

Other upcoming courses

Species Distribution Modeling using R (SDMR04)

www.prstatistics.com/course/species-distribution-modeling-using-r-sdmr04/

21 September 2021 - 30 September 2021

Introduction to eco-phylogenetics and comparative analyses using R (ECPH01) This course will be delivered live

https://www.prstatistics.com/course/introduction-to-eco-phylogenetics-and-comparative-analyses-using-r-ecph01/

22 September 2021 - 28 September 2021

Multivariate analysis of ecological communities in R with the VEGAN package (VGNR03)

www.prstatistics.com/course/multivariate-analysis-of-ecological-communities-in-r-with-the-vegan-package-vgnr03/

4 October 2021 - 8 October

Introduction to Data Wrangling and Data Visualization using R (DWDV01)

www.prstatistics.com/course/introduction-to-data-wrangling-and-data-visualization-using-r-dwdv01/

4 October 2021 - 8 October 2021

Introduction to Bayesian modelling with INLA (BMIN02)

https://www.prstatistics.com/course/introduction-to-bayesian-modelling-with-inla-bmin02/

4 October 2021 - 8 October 2021

Landscape genetic data analysis using R (LNDG05)

https://www.prstatistics.com/course/landscape-genetic-data-analysis-using-r-lndg05/

18 October 2021 - 27 October 2021

FREE 1 DAY INTRO TO R AND R STUDIO (FIRR01)

https://www.prstatistics.com/course/free-1-day-intro-to-r-and-r-studio-firr01/

20 October 2021

Introduction to generalised linear models using R and Rstudio (IGLM04)

https://www.prstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm04/

3 November 2021 - 4 November 2021

Introduction to mixed models using R and Rstudio (IMMR05)

https://www.prstatistics.com/course/introduction-to-mixed-models-using-r-and-rstudio-immr05/

10 November 2021 - 11 November 2021

Introduction to Machine Learning and Deep Learning using R (IMDL02)

https://www.prstatistics.com/course/introduction-to-machine-learning-and-deep-learning-using-r-imdl02/

17 November 2021 - 18 November 2021

Model selection and model simplification (MSMS02)

https://www.prstatistics.com/course/model-selection-and-model-simplification-msms02/

24 November 2021 - 25 November 2021

Species distribution modelling with Bayesian statistics in R (SDMB03)

www.prstatistics.com/course/species-distribution-modelling-with-bayesian-statistics-in-r-sdmb03/

6 December 2021 - 10 December 2021

Introduction to Hidden Markov and State Space models (HMSS01)

https://www.prstatistics.com/course/introduction-to-hidden-markov-and-state-space-models-hmss01/

8 December 2021 - 9 December 2021

Time Series Data Analysis (TSDA01)

https://www.prstatistics.com/course/time-series-data-analysis-tsda01/

14 December 2021 - 17 December 2021

Bayesian Data Analysis (BADA01)

https://www.prstatistics.com/course/bayesian-data-analysis-bada01/

10th January 2022 - 14th January 2022

Introduction to Stan for Bayesian Data Analysis (ISBD01)

https://www.prstatistics.com/course/introduction-to-stan-for-bayesian-data-analysis-isbd01/

18th January 2022 - 20th January 2022

Stable Isotope Mixing Models using SIBER, SIAR, MixSIAR (SIMM08)

https://www.prstatistics.com/course/stable-isotope-mixing-models-using-r-simm08/

1st February 2022 - 4th February 2022