ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM08) https://www.prstats.org/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm08/
26th - 27th February This course provides 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, and categorical logistic regression, Poisson and negative binomial regression for count variables, as well as extensions for overdispersed and zero-inflated data. 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 binomial logistic regression, and the multinomial case, which is for modelling outcomes variables that are polychotomous, i.e., have more than two categorically distinct values. We will then cover 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 extensions to accommodate overdispersion, starting with the quasi-likelihood approach, then covering the negative binomial and beta-binomial models for counts and discrete proportions, respectively. Finally, we will cover zero-inflated Poisson and negative binomial models, which are for count data with excessive numbers of zero observations. ---------------------------------------------------------------------------------------------------------------------------------------------- ONLINE COURSE – Introduction To Mixed Models Using R And Rstudio (IMMR09) https://www.prstats.org/course/introduction-to-mixed-models-using-r-and-rstudio-immr09/ 11th - 13th March This course provides a comprehensive practical and theoretical introduction to multilevel models, also known as hierarchical or mixed effects models. We will focus primarily on multilevel linear models, but also cover multilevel generalized linear models. Likewise, we will also describe Bayesian approaches to multilevel modelling. We will begin by focusing on random effects multilevel models. These models make it clear how multilevel models are in fact models of models. In addition, random effects models serve as a solid basis for understanding mixed effects, i.e. fixed and random effects, models. In this coverage of random effects, we will also cover the important concepts of statistical shrinkage in the estimation of effects, as well as intraclass correlation. We then proceed to cover linear mixed effects models, particularly focusing on varying intercept and/or varying slopes regression models. We will then cover further aspects of linear mixed effects models, including multilevel models for nested and crossed data data, and group level predictor variables. Towards the end of the course we also cover generalized linear mixed models (GLMMs), how to accommodate overdispersion through individual-level random effects, as well as Bayesian approaches to multilevel levels using the brms R package. ---------------------------------------------------------------------------------------------------------------------------------------------- If you are booking both courses, please email me and I will provide a discount code for 10% off the total cost of both courses. Please email oliverhoo...@prstatistics.com with any questions. Please feel free to share among colleagues and fiends -- Oliver Hooker PhD. PR stats To unsubscribe from this list please go to https://community.esa.org/confirm/?u=RhPWqPxFwODKvbkiT32nkIqRrsiSgulp
