Spatial Hyperspheric Models for Compositional Data

Abstract

Compositional observations are an increasingly prevalent data source in spatial statistics. Analysis of such data is typically done on log-ratio transformations or via Dirichlet regression. However, these approaches often make unnecessarily strong assumptions (e.g., strictly positive components, exclusively negative correlations). An alternative approach uses square-root transformed compositions and directional distributions. Such distributions naturally allow for zero-valued components and positive correlations, yet they may include support outside the non-negative orthant and are not generative for compositional data. To overcome this challenge, we truncate the elliptically symmetric angular Gaussian (ESAG) distribution to the non-negative orthant. Additionally, we propose a spatial hyperspheric regression model that contains fixed and random multivariate spatial effects. The proposed model also contains a term that can be used to propagate uncertainty that may arise from precursory stochastic models (i.e., machine learning classification). We used our model in a simulation study and for a spatial analysis of classified bioacoustic signals of the Dryobates pubescens (downy woodpecker).