ONLINE COURSE – Advancing in R (ADVR01) Data Wrangling, Data Viz, GLM's, GLMM's and Model Selection
https://www.prstats.org/course/advancing-in-r-advr01/ 25th - 29th March 2024 Please feel free to share! *COURSE DETAILS - *This course is designed to provide attendees with a comprehensive understanding of statistical modelling and its applications in various fields, such as ecology, biology, sociology, agriculture, and health. We cover all foundational aspects of modelling, including all coding aspects, ranging from data wrangling, visualisation and exploratory data analysis, to generalized linear mixed models, assessing goodness-of-fit and carrying out model comparison. *Course description* This course is designed to provide attendees with a comprehensive understanding of statistical modelling and its applications in various fields, such as ecology, biology, sociology, agriculture, and health. We cover all foundational aspects of modelling, including all coding aspects, ranging from data wrangling, visualisation and exploratory data analysis, to generalized linear mixed models, assessing goodness-of-fit and carrying out model comparison. *Data wrangling* For data wrangling, we focus on tools provided by R's tidyverse. Data wrangling is the art of taking raw and messy data and formatting and cleaning it so that data analysis and visualization may be performed on it. Done poorly, it can be time consuming, laborious, and error-prone. Fortunately, the tools provided by R's tidyverse allow us to do data wrangling in a fast, efficient, and high-level manner, which can have dramatic consequences for ease and speed with which we analyse data. We start with how to read data of different types into R, we then cover in detail all the dplyr tools such as select, filter, mutate, and others. Here, we will also cover the pipe operator (%>%) to create data wrangling pipelines that takes raw messy data on the one end and returns cleaned tidy data on the other. We then cover how to perform descriptive or summary statistics on our data using dplyr’s group_by and summarise functions. We then turn to combining and merging data. Here, we will consider how to concatenate data frames, including concatenating all data files in a folder, as well as cover the powerful SQL-like join operations that allow us to merge information in different data frames. The final topic we will consider is how to “pivot” data from a “wide” to “long” format and back using tidyr’s pivot_longer and pivot_wider functions. *Data visualisation* For visualisation, we focus on the ggplot2 package. We begin by providing a brief overview of the general principles data visualization, and an overview of the general principles behind ggplot. We then proceed to cover the major types of plots for visualizing distributions of univariate data: histograms, density plots, barplots, and Tukey boxplots. In all of these cases, we will consider how to visualize multiple distributions simultaneously on the same plot using different colours and "facet" plots. We then turn to the visualization of bivariate data using scatterplots. Here, we will explore how to apply linear and nonlinear smoothing functions to the data, how to add marginal histograms to the scatterplot, add labels to points, and scale each point by the value of a third variable. We then cover some additional plot types that are often related but not identical to those major types covered during the beginning of the course: frequency polygons, area plots, line plots, uncertainty plots, violin plots, and geospatial mapping. We then consider more fine grained control of the plot by changing axis scales, axis labels, axis tick points, colour palettes, and ggplot "themes". Finally, we consider how to make plots for presentations and publications. Here, we will introduce how to insert plots into documents using RMarkdown, and also how to create labelled grids of subplots of the kind seen in many published articles. *Generalized linear models* 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. *Mixed 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. *Model selection and model simplification* Throughout the course we consider the fundamental issue of how to measure model fit and a model’s predictive performance, and discuss a wide range of other major model fit measurement concepts like likelihood, log likelihood, deviance, and residual sums of squares. We thoroughly explore nested model comparison, particularly in general and generalized linear models, and their mixed effects counterparts. We discuss out-of-sample generalization, and introduce leave-one-out cross-validation and the Akaike Information Criterion (AIC). We also cover general concepts and methods related to variable selection, including stepwise regression, ridge regression, Lasso, and elastic nets. Finally, we turn to model averaging, which may represent a preferable alternative to model selection. Please email oliverhoo...@prstatistics.com with any questions. -- Oliver Hooker PhD. PR stats [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
