physalia-forecasting-course

Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}

https://github.com/nicholasjclark/physalia-forecasting-course

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Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}

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Ecological forecasting with mvgam and
brms
 


Physalia-Courses
https://www.physalia-courses.org/


Nicholas J Clark

2428th March, 2025



COURSE OVERVIEW
Time series analysis and forecasting are standard goals in applied
ecology. But most time series courses focus only on traditional
forecasting models such as ARIMA or Exponential Smoothing. These models
cannot handle features that dominate ecological data, including
overdispersion, clustering, missingness, discreteness and nonlinear
effects. Using the flexible and powerful Bayesian modelling software
Stan, we can now meet this complexity head on. Packages
such as {mvgam} and {brms} can build Stan code
to specify ecologically appropriate models that include nonlinear
effects, random effects and dynamic processes, all with simple
interfaces that are familiar to most R users. In this
course you will learn how to wrangle, visualize and explore ecological
time series. You will also learn to use the {mvgam} and
{brms} packages to analyse a diversity of ecological time
series to gain useful insights and produce accurate forecasts. All
course materials (presentations, practical exercises, data files, and
commented R scripts) will be provided electronically to
participants.


TARGET AUDIENCE AND ASSUMED BACKGROUND
This course is aimed at higher degree research students and early
career researchers working with time series data in the natural sciences
(with particular emphasis on ecology) who want to extend their knowledge
by learning how to add dynamic processes to model temporal
autocorrelation. Participants should ideally have some knowledge of
regression including linear models, generalized linear models and
hierarchical (random) effects. But well briefly recap these as we
connect them to time series modelling.
Participants should be familiar with RStudio and have some fluency in
programming R code. This includes an ability to import,
manipulate (e.g.modify variables) and visualise data. There will be a
mix of lectures and hands-on practical exercises throughout the
course.


LEARNING OUTCOMES

Understand how dynamic GLMs and GAMs work to capture both nonlinear
covariate effects and temporal dependence
Be able to fit dynamic GLMs and GAMs in R using the
{mvgam} and {brms} packages
Understand how to critique, visualize and compare fitted dynamic
models
Know how to produce forecasts from dynamic models and evaluate their
accuracies using probabilistic scoring rules



COURSE PREPARATION
Please be sure to have at least version 4.2  and preferably
version 4.3 or above  of R installed. Note that
R and RStudio are two different things: it is
not sufficient to just update RStudio, you also need to
update R by installing new versions as they are
released.
To install RStudio, go to https://posit.co/download/rstudio-desktop/ and follow
the instructions.
To download R go to the CRAN Download page and follow the
links to download R for your operating system:

Windows
MacOS X
Linux

To check what version of R you have installed, you can
run
version
in R and look at the version.string entry
(or the major and minor entries).
We will make use of several R packages that youll need
to have installed. Prior to the start of the course, please run the
following code to update your installed packages and then install the
required packages:
# update any installed R packages
update.packages(ask = FALSE, checkBuilt = TRUE)

# install the development version of brms, including its dependencies
install.packages("remotes")
remotes::install_github("paul-buerkner/brms", dependencies = TRUE)

# install mvgam and a few other packages we will use for plotting
install.packages(c("mvgam", "gratia", "tidybayes"))

INSTALLING AND CHECKING STAN
When working in R, there are two primary interfaces we
can use to fit models with Stan ({rstan} and
CmdStan). It is highly recommended that you use the
Cmdstan backend, with the {cmdstanr}
interface, rather than using {rstan}. But either interface
will work. What is more important is that you have an up to date version
of Stan. For all {mvgam} and
{brms} functionalities to work properly, please ensure you
have at least version 2.33 of Stan installed. The
CRAN and GitHub development versions of {rstan} and
CmdStan are currently several versions ahead of this, and
all of these versions are stable.
Compiling a Stan program requires a modern C++ compiler and the GNU
Make build utility (a.k.a. gmake). The correct versions of these tools
to use will vary by operating system, but unfortunately most standard
Windows and MacOS X machines do not come with them installed by default.
The first step to installing Stan is to update your C++
toolchain so that you can compile models correctly. The
{cmdstanr} package usually makes this easy to do. First
install the R package {cmdstanr} by running
the following command in a fresh R environment:
install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
If you dont have CmdStan installed then
{cmdstanr} can install it for you, assuming you have a
suitable C++ toolchain. To double check that your toolchain is set up
properly you can call the check_cmdstan_toolchain()
function:
library(cmdstanr)
check_cmdstan_toolchain(fix = TRUE)
This may give you a message that you need to update
rtools to match your current version of R, so
please follow the instructions in the printed error message if that is
the case. Once the toolchain is set up properly, {cmdstanr}
will require a working installation of CmdStan,
the shell interface to Stan. CmdStan can be
installed by calling the install_cmdstan()
function:
install_cmdstan(cores = 2)
The exact version you have installed can be checked using
cmdstanr::cmdstan_version(). You should now be able to
follow the remaining instructions on the Getting
Started with {cmdstanr} page to ensure that
Stan models can successfully compile on your machine.
However a quicker way to check this would be to run this script:
library(mvgam)
simdat <- sim_mvgam()
mod <- mvgam(
  y ~ s(season, bs = 'cc', k = 5) +
    s(time, series, bs = 'fs', k = 8),
  data = simdat$data_train
)
But issues can sometimes occur when: 1. you
dont have write access to the folders that CmdStan uses to
create model executables 2. you
are using a university- or company-imposed syncing system such as One
Drive, leading to confusion about where your make file and compilers are
located 3. you
are using a university- or company-imposed firewall that is aggressively
deleting the temporary executable files that CmdStan
creates when compiling
If you run into any of these issues, it is best to consult with your
IT department for support. There
are detailed instructions by the Stan team on how to ensure you have the
correct C++ toolchain to compile models, so please refer to those
when consulting your IT department.



PROGRAM
09:00 - 12:00 (Berlin time): live coding exercises and review of the
lecture materials
3 additional hours: self-guided readings and viewing of lecture
recordings


Monday (day 1)
Lecture 1 (html
| pdf)

 Lecture 2 (html
| pdf)

 Live code examples (Random
effects) 
 Tutorial 1 (html)
* Introduction to time series and time series visualization * Some
traditional time series models and their assumptions * GLMs and GAMs for
ecological modelling * Temporal random effects and temporal residual
correlation structures


Tuesday (day 2)
Lecture 3 (html
| pdf)

 Live code examples (Interactions
| Time-varying
effects) 
 Tutorial 2 (html)
* Dynamic GLMs and Dynamic GAMs * Autoregressive dynamic processes *
Gaussian Processes * Dynamic coefficient models


Wednesday (day 3)
Lecture 4 (html
| pdf)

 Live code example (Distributed
lags 
 Tutorial 3 (html)
* Bayesian posterior predictive checks * Forecasting from dynamic models
* Point-based forecast evaluation * Probabilistic forecast
evaluation


Thursday (day 4)
Lecture 5 (html
| pdf)

 Live code examples (Functionals
| Time-varying
seasonality) 
 Tutorial 4 (html)
* Multivariate ecological time series * Vector autoregressive processes
* Dynamic factor models * Multivariate forecast evaluation


Friday (day 5)

Group-based practical examples / case studies
Review, feedback and open discussion

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