hormLong: An R package for longitudinal data analysis in wildlife endocrinology studies

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  Centre for Integrative Ecology, School of Life and Environmental Sciences, Deakin University, Geelong, Victoria, Australia
DOI
10.7287/peerj.preprints.1546v1
Published
Accepted
Subject Areas
Biodiversity, Computational Biology, Diabetes and Endocrinology
Keywords
Area under the curve, baseline, peak detection, non-invasive hormone monitoring, ovulation, steroid, stress, faecal glucocorticoid metabolites
Copyright
© 2015 Fanson et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
Cite this article
Fanson B, Fanson KV. 2015. hormLong: An R package for longitudinal data analysis in wildlife endocrinology studies. PeerJ PrePrints 3:e1546v1

Abstract

The growing number of wildlife endocrinology studies have greatly enhanced our understanding of comparative endocrinology, and have also generated extensive longitudinal data for a vast number of species. However, the extensive graphical analysis required for these longitudinal datasets can be time consuming because there is often a need to create tens, if not hundreds, of graphs. Furthermore, routine methods for summarising hormone profiles, such as the iterative baseline approach and area under the curve (AUC), can be tedious and non-reproducible, especially for large number of individuals. We developed an R package, hormLong, which provides the basic functions to perform graphical and numerical analyses routinely used by wildlife endocrinologists. To encourage its use, hormLong has been developed such that no familiarity with R is necessary. Here, we provide a brief overview of the functions currently available and demonstrate their utility with previously published Asian elephant data. We hope that this package will promote reproducibility and encourage standardization of wildlife hormone data analysis.

Author Comment

This is a preprint submission to PeerJ.