Hierarchical Bayesian strategy for modeling correlated compositional data with observed zero counts

Huston, Carolyn; Schwarz, Carl

doi:10.1007/s10651-012-0189-0

Cited by 4 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, see Heisey et al. , Huston and Schwarz for ecological examples). Using a multivariate spatial model allows us to account for correlation among detection, occupancy, and abundance in data models and borrows strength across space.…”

Section: Methodsmentioning

confidence: 99%

“…First, similar to other authors (Dorazio 2014, Fithian et al 2015, Giraud et al 2016 we consider how parameters are shared when fitting models for multiple data sets. Second, we provide a unique contribution by explicitly accounting for the spatial correlation of observations and parameters by using a Multivariate Conditional Autoregressive (MVCAR) spatial model (Banerjee et al 2004, see Heisey et al 2010, Huston and Schwarz 2012 for ecological examples). Using a multivariate spatial model allows us to account for correlation among detection, occupancy, and abundance in data models and borrows strength across space.…”

Section: Modeling Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Pacifici

Reich

Miller

et al. 2017

Ecology

224

278

View full text Add to dashboard Cite

Abstract. The last decade has seen a dramatic increase in the use of species distribution models (SDMs) to characterize patterns of species' occurrence and abundance. Efforts to parameterize SDMs often create a tension between the quality and quantity of data available to fit models. Estimation methods that integrate both standardized and non-standardized data types offer a potential solution to the tradeoff between data quality and quantity. Recently several authors have developed approaches for jointly modeling two sources of data (one of high quality and one of lesser quality). We extend their work by allowing for explicit spatial autocorrelation in occurrence and detection error using a Multivariate Conditional Autoregressive (MVCAR) model and develop three models that share information in a less direct manner resulting in more robust performance when the auxiliary data is of lesser quality. We describe these three new approaches ("Shared," "Correlation," "Covariates") for combining data sources and show their use in a case study of the Brown-headed Nuthatch in the Southeastern U.S. and through simulations. All three of the approaches which used the second data source improved out-of-sample predictions relative to a single data source ("Single"). When information in the second data source is of high quality, the Shared model performs the best, but the Correlation and Covariates model also perform well. When the information quality in the second data source is of lesser quality, the Correlation and Covariates model performed better suggesting they are robust alternatives when little is known about auxiliary data collected opportunistically or through citizen scientists. Methods that allow for both data types to be used will maximize the useful information available for estimating species distributions.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Modeling Frameworkmentioning

confidence: 99%

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Pacifici

Reich

Miller

et al. 2017

Ecology

224

278

View full text Add to dashboard Cite

show abstract

“…Major textbooks do not treat them in any depth (e.g., Pawlowsky-Glahn et al, 2015; Van den Boogaart & Tolosana-Delgado, 2013). Most of the published articles on Bayesian methods for compositional data analysis employ the Dirichlet distribution rather than the log-ratio approach (e.g., Van der Merwe, 2019), or are restricted to analyzing counts rather than continuous variables (e.g., Huston & Schwarz, 2012; Napier et al, 2015). One understandable reason for neglecting this topic is that the traditional log-ratio approach utilizes normal-theory regression and therefore is amenable to well-known hierarchical Bayesian modeling techniques.…”

Section: Discussionmentioning

confidence: 99%

Compositional data analysis tutorial.

Smithson¹,

Broomell²

2024

Psychological Methods

View full text Add to dashboard Cite

This article presents techniques for dealing with a form of dependency in data arising when numerical data sum to a constant for individual cases, that is, "compositional" or "ipsative" data. Examples are percentages that sum to 100, and hours in a day that sum to 24. Ipsative scales fell out of fashion in psychology during the 1960s and 1970s due to a lack of methods for analyzing them. However, ipsative scales have merits, and compositional data commonly occur in psychological research. Moreover, as we demonstrate, sometimes converting data to a compositional form yields insights not otherwise accessible. Fortunately, there are sound methods for analyzing compositional data. We seek to enable researchers to analyze compositional data by presenting appropriate techniques and illustrating their application to real data. First, we elaborate the technical details of compositional data and discuss both established and new approaches to their analysis. We then present applications of these methods to real social science datasets (data and code using R are available in a supplementary document). We conclude with a discussion of the state of the art in compositional data analysis and remaining unsolved problems. A brief guide to available software resources is provided in the first section of the supplementary document.

show abstract

Compositional data analysis of smoke emissions from debris piles with low-density polyethylene

Weise

Jung

Palarea‐Albaladejo

et al. 2020

Journal of the Air & Waste Management Association

View full text Add to dashboard Cite

Hierarchical Bayesian strategy for modeling correlated compositional data with observed zero counts

Cited by 4 publications

References 28 publications

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Integrating multiple data sources in species distribution modeling: a framework for data fusion

Compositional data analysis tutorial.

Compositional data analysis of smoke emissions from debris piles with low-density polyethylene

Contact Info

Product

Resources

About