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

Simon Bonner; Ariane Mutzel; Han Na Kim; David Westneat; Jonathan Wright; Matthew Schofield

doi:10.18637/JSS.V100.I10

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

Simon Bonner, Ariane Mutzel, Han Na Kim, David Westneat, Jonathan Wright, Matthew Schofield

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Journal	Journal of Statistical Software
Volume	100
Issue number	10
DOIs	https://doi.org/10.18637/JSS.V100.I10
State	Published - 2021

Bibliographical note

Publisher Copyright:
© 2021, American Statistical Association. All rights reserved.

Funding

DFW, AM, and SB were supported by the US National Science Foundation grant IOS1257718. SB was further supported by NSERC Grant 493024-2016, and JW was partly supported by the Research Council of Norway through its Centers of Excellence funding scheme, project number 223257. We would like to thank Duncan Brown and (the then) CCW for access to the pied flycatcher field site at Abergwyngregyn NNR, as well as Gabrielle Archard, Christian Both, Matt Davey, Kim Denny, Ioan Fazey, Camilla Hinde, Adam Morrey, Roberta Spears, Jane Stott, Richard Yarnell and Yoram and Shlomith Yom-Tov for assistance in the field.

Funders	Funder number
National Science Foundation Arctic Social Science Program	IOS1257718
Natural Sciences and Engineering Research Council of Canada	493024-2016
Norges Forskningsråd	223257

Keywords

Bayesian inference
Diversity patterns
Generalized linear models
Hierarchical models
Markov chain Monte Carlo
Structured residual variance
Variance patterns

ASJC Scopus subject areas

Software
Statistics and Probability
Statistics, Probability and Uncertainty

Access to Document

10.18637/JSS.V100.I10

Cite this

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abstract = "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.",

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AU - Wright, Jonathan

AU - Schofield, Matthew

PY - 2021

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AB - 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.

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

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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