dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble

dalmatian: A Package for Fitting Double Hierarchical Linear Models in R via JAGS and nimble

: Bayesian inference, diversity patterns, hierarchical models, generalized linear models, Markov chain Monte Carlo, structured residual variance, variance patterns

Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many
biological studies seek to understand non-deterministic patterns in the variance or dispersion of a phenotypic or ecological response variable. We describe a new R package,
dalmatian, that provides methods for fitting double hierarchical generalized linear models
incorporating fixed and random predictors of both the mean and variance. Models are
fit via Markov chain Monte Carlo sampling implemented in either JAGS or nimble and
the package provides simple functions for monitoring the sampler and summarizing the
results. We illustrate these functions through an application to data on food delivery by
breeding pied flycatchers (Ficedula hypoleuca). Our intent is that this package makes it
easier for practitioners to implement these models without having to learn the intricacies
of Markov chain Monte Carlo methods.

Simon Bonner

https://www.jstatsoft.org/article/view/v100i10

University of Western
Ontario

November 2021

Fajar Bagus W

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English

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