- This event has passed.
ONLINE COURSE – Advancing in R (ADVR01) This course will be delivered live
25 March 2024 - 29 March 2024
£400.00
Monday, March 25th, 2024
Course Format
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
Course Program
TIME ZONE – Ireland local time – however all sessions will be recorded and made available allowing attendees from different time zones to follow.
Please email oliverhooker@prstatistics.com for full details or to discuss how we can accommodate you.
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.
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 a 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 consequence
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 take raw messy data on the one end and return 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.
Intended Audiences
Venue
Course Information
Availability – TBC
Duration – 3 x 1/2 days
Contact hours – Approx. 12 hours
ECT’s – Equal to 1 ECT’s
Language – English
Teaching Format
The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time at:
• 10am-12pm
• 1pm-3pm
• 4pm-6pm
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session.
If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible. For example, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub
Assumed quantitative knowledge
Assumed computer background
Equipment and software requirements
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.
Participants should be able to install additional software on their own computer during the course (please make sure you have administration rights to your computer).
A large monitor and a second screen, although not absolutely necessary, could improve the learning experience. Participants are also encouraged to keep their webcam active to increase the interaction with the instructor and other students.
Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact oliverhooker@prstatistics.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
COURSE PROGRAMME
Monday 25th
Day 1
Topic 1: Reading in data. We will begin by reading in data into R using tools such
as readr and readxl. Almost all types of data can be read into R, and here we will consider
many of the main types, such as csv, xlsx, sav, etc. Here, we will also consider how to control
how data are parsed, e.g., so that they are read as dates, numbers, strings, etc.
Topic 2: Wrangling with dplyr. We will next cover the very powerful dplyr R package. This
package supplies a number of so-called "verbs" — select, rename, slice, filter, mutate, arrange, etc. — each of which focuses on a key data manipulation tools, such as selecting or changing variables. All of these verbs can be chained together using "pipes" (represented by %>%). Together, these create powerful data wrangling pipelines that take raw data as input and return cleaned data as output. Here, we will also learn about the key concept of "tidy data", which is roughly where each row of a data frame is an observation and each column is a variable.
Topic 3: Summarizing data. The summarize and group_by tools in dplyr can be used with
great effect to summarize data using descriptive statistics.
Topic 4: Merging and joining data frames. There are multiple ways to combine data frames,
with the simplest being "bind" operations, which are effectively horizontal or vertical
concatenations. Much more powerful are the SQL-like "join" operations. Here, we will
consider the inner_join, left_join, right_join, full_join operations. In this section, we will also
consider how to use purrr to read in and automatically merge large sets of files.
Topic 5: Pivoting data. Sometimes we need to change data frames from "long" to "wide"
formats. The R package tidyr provides the tools pivot_longer and pivot_wider for doing this.
Tuesday 26th
Day 2
Topic 1: What is data visualization. Data visualization is a means to explore and understand
our data and should be a major part of any data analysis. Here, we briefly discuss why data
visualization is so important and what the major principles behind it are.
Topic 2: Introducing ggplot. Though there are many options for visualization in R, ggplot is
simply the best. Here, we briefly introduce the major principles behind how ggplot works,
namely how it is a layered grammar of graphics.
Topic 3: Visualizing univariate data. Here, we cover a set of major tools for visualizing
distributions over single variables: histograms, density plots, barplots, Tukey boxplots. In each case, we will explore how to plot multiple groups of data simultaneously using different colours and also using facet plots.
Topic 4: Scatterplots. Scatterplots and their variants are used to visualize bivariate data.
Here, in addition to covering how to visualize multiple groups using colours and facets, we
will also cover how to provide marginal plots on the scatterplots, labels to points, and how
to obtain linear and nonlinear smoothing of the plots.
Topic 5: More plot types. Having already covered the most widely used general purpose
plots, we now turn to cover a range of other major plot types: frequency polygons, area
plots, line plots, uncertainty plots, violin plots, and geospatial mapping. Each of these are
important and widely used types of plots, and knowing them will expand your repertoire.
Topic 6: Fine control of plots. Thus far, we will have mostly used the default for the plot
styles and layouts. Here, we will introduce how to modify things like the limits and scales on
the axes, the positions and nature of the axis ticks, the colour palettes that are used, and
the different types of ggplot themes that are available.
Topic 7: Plots for publications and presentations. Thus far, we have primarily focused on
data visualization as a means of interactively exploring data. Often, however, we also want
to present our plots in, for example, published articles or in slide presentations. It is simple
to save a plot in different file formats, and then insert them into a document. However, a
much more efficient way of doing this is to use RMarkdown to run the R code and
automatically insert the resulting figure into a, for example, Word document, pdf document,
html page, etc. In addition, here we will also cover how to make labelled grids of subplots
like those found in many scientific articles.
Wednesday 27th
Day 3
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: Binomial logistic regression. Here, we show how the binary logistic regression can
be extended to deal with data on discrete proportions. We will also present alternative link
functions to the logit, such as the probit and complementary log-log links.
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. Categorical logistic regression is based on an extension of the binary logistic regression case.
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.
Thursday 28th
Topic 1: Measuring model fit. Here, the concept of conditional probability of the observed
data, or of future data, is of vital importance. This is intimately related, though distinct, to
concept of likelihood and the likelihood function, which is in turn related to the concept of
the log likelihood or deviance of a model. Here, we also show how these concepts are
related to concepts of residual sums of squares, root mean square error (rmse), and
deviance residuals.
Topic 2: Nested model comparison. In this section, we cover how to do nested model
comparison in general linear models, generalized linear models, and their mixed effects
(multilevel) counterparts. First, we precisely define what is meant by a nested model. Then
we show how nested model comparison can be accomplished in general linear models with
F tests, which we will also discuss in relation to R^2 and adjusted R^2. In generalized linear
models, we can accomplish nested model comparison using deviance based chi-square tests
via Wilks’s theorem.
Topic 3: Overdispersion models. The quasi-likelihood approach for both the Poisson and
binomial models. 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. Beta-binomial
regression. The beta-binomial model is an overdispersed alternative to the binomial.
Topic 4: 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.
Topic 5: Random effects models. The defining feature of multilevel models is that they are
models of models. We begin by using a binomial random effects model to illustrate this.
Specifically, we show how multilevel models are models of the variability in models of
different clusters or groups of data.
Topic 6: Normal random effects models. Normal, as in normal distribution, random effects
models are the key to understanding the more general and widely used linear mixed effects
models. Here, we also cover the key concepts of statistical shrinkage and intraclass
correlation.
Friday 29th
Day 5
Topic 1: Out of sample predictive performance: cross validation and information criteria.
Here, we describe how to measure out of sample predictive performance, which measures
how well a model can generalize to new data. This is arguably the gold-standard for
evaluating any statistical models. A practical means to measure out of sample predictive
performance is cross-validation, especially leave-one-out cross-validation. Leave-one-out
cross-validation can, in relatively simple models, be approximated by Akaike Information
Criterion (AIC), which can be exceptionally simple to calculate. We will discuss how to
interpret AIC values, and describe other related information criteria, some of which will be
used in more detail in later sections.
Topic 2: Linear mixed effects models. Next, we turn to multilevel linear models, also known
as linear mixed effects models. We specifically deal with the cases of varying intercept
and/or varying slope linear regression models.
Topic 3: Multilevel models for nested data. Here, we will consider multilevel linear models
for nested, as in groups of groups, data. As an example, we will look at multilevel linear
models applied to data from students within classes that are themselves within different
schools, and where we model the variability of effects across the classes and across the
schools.
Topic 4: Multilevel models for crossed data. In some multilevel models, each observation
occurs in multiple groups, but these groups are not nested. For example, animals may be
members of different species and in different locations, but the species are not subsets of
locations, nor vice versa. These are known as crossed or multiclass data structures.
Topic 5: Group level predictors. In some multilevel regression models, predictor variable are
sometimes associated with individuals, and sometimes associated with their groups. In this
section, we consider how to handle these two situations.
Topic 6: Generalized linear mixed models (GLMMs). Here, we extend the linear mixed model
to the exponential family of distributions and showcase an example using the Poisson
GLMM. We also cover how to accommodate overdispersion through individual-level
random effects.
Topic 7: Bayesian multilevel models. All of the models that we have considered can be
handled, often more easily, using Bayesian models. Here, we provide an brief introduction
to Bayesian models and how to perform examples of the models that we have considered
using Bayesian methods and the brms R package.
Topic 8: Variable selection. Variable selection is a type of nested model comparison. It is
also one of the most widely used model selection methods, and variable selection of some
kind is almost always done routinely in all data analysis. In particular, we cover stepwise
regression (and its limitations), all subsets methods, ridge regression, Lasso, and elastic nets.
Topic 9: Model averaging. Rather than selecting one model from a set of candidates, it is
arguably always better perform model averaging, using all the candidates models, weighted by the predictive performance. We show how to perform model average using information
criteria.
Course Instructor
Dr. Rafael De Andrade Moral
Rafael is an Associate Professor of Statistics at Maynooth University, Ireland. With a background in Biology and a PhD in Statistics from the University of São Paulo, Rafael has a deep passion for teaching and conducting research in statistical modelling applied to Ecology, Wildlife Management, Agriculture, and Environmental Science. As director of the Theoretical and Statistical Ecology Group, Rafael brings together a community of researchers who use mathematical and statistical tools to better understand the natural world. As an alternative teaching strategy, Rafael has been producing music videos and parodies to promote Statistics in social media and in the classroom. His personal webpage can be found here