GAMbler

This is a growing collection of interesting and (hopefully) useful tips for ecological modeling, with (perhaps too much) emphasis on Generalized Additive Models.

Written by Nicholas Clark

Incorporating time-varying seasonality in forecast models

Many time series show repeated seasonal patterns, and fitting models that can capture this seasonality is a major focus of time series forecasting algorithms. There are a lot of useful, established methods to deal with this (i.e. SARIMA, Harmonic regression), but sometimes the seasonal patterns change over time. Fewer time series and forecasting models can handle this feature. This post introduces some strategies for capturing time-varying seasonality and time-varying periodicity in Dynamic Generalized Additive Models, using the mvgam package in R.

First release of mvgam(v1.1.0) to CRAN

The mvgam package has been officially released to CRAN. This package fits Bayesian Dynamic Generalized Additive Models to sets of time series. Users can build dynamic nonlinear State-Space models that can incorporate semiparametric effects in observation and process components, using a wide range of observation families. Estimation is performed using Markov Chain Monte Carlo with Hamiltonian Monte Carlo in the software Stan.

How to interpret and report nonlinear effects from Generalized Additive Models

Generalized additive models (GAMs) are incredibly flexible tools that fit penalized regression splines to data. But interpreting nonlinear effects from GAMs is not as easy as interpreting linear models. In this post I provide 3 simple steps to help you understand and interpret nonlinear effects from GAMs using the mgcv R package.

Phylogenetic smoothing using mgcv

Use species’ phylogenetic or functional relationships to inform and partially pool hierarchical, nonlinear smooth functions in Generalized Additive Models with mgcv

By Nicholas Clark in rstats mgcv

February 24, 2024

Temporal autocorrelation in GAMs and the mvgam package

Temporal autocorrelation is a dominant feature of time series. We often want to use Generalized Additive Models (GAMs) to fit smoothing splines to time series data, but incorporating autocorrelation in these models can be difficult. Enter the mvgam package