mvgam

MultiVariate (Dynamic) Generalized Addivite Models

The goal of mvgam is to use a Bayesian framework to estimate parameters of Dynamic Generalized Additive Models (DGAMs) for time series with dynamic trend components. The package provides an interface to fit Bayesian DGAMs using Stan as the backend. The formula syntax is based on that of the package mgcv to provide a familiar GAM modelling interface. The motivation for the package and some of its primary objectives are described in detail by Clark & Wells 2022 (published in Methods in Ecology and Evolution). An introduction to the package and some worked examples are shown in this seminar: Ecological Forecasting with Dynamic Generalized Additive Models.

Installation

Install the stable version from CRAN using: install.packages('mvgam'), or install the development version from GitHub using: devtools::install_github("nicholasjclark/mvgam"). Note that to actually condition models with MCMC sampling, the Stan software must be installed (along with either rstan and/or cmdstanr). Only rstan is listed as a dependency of mvgam to ensure that installation is less difficult. If users wish to fit the models using mvgam, please refer to installation links for Stan with rstan here, or for Stan with cmdstandr here. You will need a fairly recent version of Stan (preferably 2.29 or above) to ensure all the model syntax is recognized. We highly recommend you use Cmdstan through the cmdstanr interface as the backend. This is because Cmdstan is easier to install, is more up to date with new features, and uses less memory than Rstan. See this documentation from the Cmdstan team for more information.

Introductory seminar

Cheatsheet

Getting started

mvgam was originally designed to analyse and forecast non-negative integer-valued data (counts). These data are traditionally challenging to analyse with existing time-series analysis packages. But further development of mvgam has resulted in support for a growing number of observation families that extend to other types of data. Currently, the package can handle data for the following families:

• gaussian() for real-valued data
• student_t() for heavy-tailed real-valued data
• lognormal() for non-negative real-valued data
• Gamma() for non-negative real-valued data
• betar() for proportional data on (0,1)
• bernoulli() for binary data
• poisson() for count data
• nb() for overdispersed count data
• binomial() for count data with known number of trials
• beta_binomial() for overdispersed count data with known number of trials
• nmix() for count data with imperfect detection (unknown number of trials)

See ??mvgam_families for more information. Below is a simple example for simulating and modelling proportional data with Beta observations over a set of seasonal series with independent Gaussian Process dynamic trends:

data <- sim_mvgam(family = betar(),
                  T = 80,
                  trend_model = GP(),
                  trend_rel = 0.5, 
                  seasonality = 'shared')

Plot the series to see how they evolve over time

plot_mvgam_series(data = data$data_train, series = 'all')

Simulating and analysing multivariate time series with Dynamic Generalized Additive Models

Fit a DGAM to these series that uses a hierarchical cyclic seasonal smooth term to capture variation in seasonality among series. The model also includes series-specific latent Gaussian Processes with squared exponential covariance functions to capture temporal dynamics

mod <- mvgam(y ~ s(season, bs = 'cc', k = 7) +
               s(season, by = series, m = 1, k = 5),
             trend_model = GP(),
             data = data$data_train,
             newdata = data$data_test,
             family = betar())

Plot the estimated posterior hindcast and forecast distributions for each series

layout(matrix(1:4, nrow = 2, byrow = TRUE))
for(i in 1:3){
  plot(mod, type = 'forecast', series = i)
}

Forecasting multivariate time series with Dynamic Generalized Additive Models

Various S3 functions can be used to inspect parameter estimates, plot smooth functions and residuals, and evaluate models through posterior predictive checks or forecast comparisons. Please see the package documentation for more detailed examples.

Vignettes

You can set build_vignettes = TRUE when installing with either devtools::install_github or remotes::install_github, but be aware this will slow down the installation drastically. Instead, you can always access the vignette htmls online at https://nicholasjclark.github.io/mvgam/articles/

Other resources

A number of case studies have been compiled to highlight how GAMs and DGAMs can be useful for working with time series data:

• Ecological Forecasting with Dynamic Generalized Additive Models
• State-Space Vector Autoregressions in mvgam
• How to interpret and report nonlinear effects from Generalized Additive Models
• Phylogenetic smoothing using mgcv
• Distributed lags (and hierarchical distributed lags) using mgcv and mvgam
• Incorporating time-varying seasonality in forecast models

The package can also be used to generate all necessary data structures, initial value functions and modelling code necessary to fit DGAMs using Stan. This can be helpful if users wish to make changes to the model to better suit their own bespoke research / analysis goals.

Interested in contributing?

I’m actively seeking PhD students and other researchers to work in the areas of ecological forecasting, multivariate model evaluation and development of mvgam. Please reach out if you are interested (n.clark’at’uq.edu.au)