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.
R 📦 to fit Dynamic Bayesian Generalised Additive Models for time series analysis and forecasting
Use species’ phylogenetic or functional relationships to inform and partially pool hierarchical, nonlinear smooth functions in Generalized Additive Models with mgcv
Use mgcv’s convenient list data format for setting up nonlinear functions that change smoothly over increasing temporal lags
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