The first course in our 8 part R-series is a comprehensive introduction to GLM's.
ONLINE COURSE – Introduction to generalised linear models using R and Rstudio (IGLM06) https://www.prstatistics.com/course/introduction-to-generalised-linear-models-using-r-and-rstudio-iglm06/ 3rd - 5th October Please feel free to share! Limited early bird tickets available prioced @ £150.00 Courses are recorded to accommodate different time zones. All attendees will have access to recordings for a further 3 months after the course to revisit any of the classes. *COURSE OVERVIEW - *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, 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. 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. We will then begin 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. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR statistics [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
