GAMs

Differing non‐linear, lagged effects of temperature and precipitation on an insect herbivore and its host plant

1. Multivariate climate change is expected to impact insect densities and plant growth in complex, and potentially different, ways. Tea (Camellia sinensis) is a unique crop system where the increase in quality from chemical defences induced by …

Fitting a DLNM is simple without the {dlnm} package

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. Since I’ve been working so much with GAMs for this project, I decided to read sections of Simon Wood’s book, Generalized additive models: an introduction with R more thoroughly.

DLNMs: hypothesis tests and p-values

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. A major goal of my postdoc project is to determine whether drought has an effect on plant vital rates (growth, survival, reproduction, recruitment).

Speeding up DLNMs with bam()

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. DLNMs themselves may not be that computationally expensive, but when combined with random effects and other smoothers, and a large-ish dataset, I’ve noticed gam() being painfully slow.

DLNM marginal basis functions

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. Choosing marginal function to construct a crossbasis According to Gasparrini et al.

Distributed lag non-linear models

Welcome / Disclaimer This is the beginning of a series of blog posts where I publicly stumble my way through figuring out some confusing, complicated, and, frankly, cutting-edge modeling and statistics.

DLNMs: building and visualizing crossbasis functions

This is part of series about distributed lag non-linear models. Please read the first post for an introduction and a disclaimer. The dlnm package The dlnm package offers two ways of fitting crossbasis functions: an “internal” and an “external” method.