2015/09/06 by Jonathan S. Lefcheck, Lefcheck, Jonathan S. · 1 voice · 56 citations
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · Environmental Science · #Ecology and Vegetation Dynamics Studies #FOS: Biological sciences #Plant and animal studies #Populations and Evolution (q-bio.PE) #Quantitative Methods (q-bio.QM) #Species Distribution and Climate Change #q-bio.PE #q-bio.QM
paper · pdf · doi:10.48550/arxiv.1509.01845
20 pages, 2 figures
arxiv created 2015/09/06 · openalex publication_date 2015/09/06 · arxiv published 2015/09/06 · arxiv updated 2015/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Ecologists and evolutionary biologists are relying on an increasingly sophisticated set of statistical tools to describe complex natural systems. One such tool that has gained increasing traction in the life sciences is structural equation modeling (SEM), a variant of path analysis that resolves complex multivariate relationships among a suite of interrelated variables. SEM has historically relied on covariances among variables, rather than the values of the data points themselves. While this approach permits a wide variety of model forms, it limits the incorporation of detailed specifications. Here, I present a fully-documented, open-source R package piecewiseSEM that builds on the base R syntax for all current generalized linear, least-square, and mixed effects models. I also provide two worked examples: one involving a hierarchical dataset with non-normally distributed variables, and a second involving phylogenetically-independent contrasts. My goal is to provide a user-friendly and tractable implementation of SEM that also reflects the ecological and methodological processes generating data.