There is currently no consensus on the most biologically meaningful way to calculate discrete-individual repeatability of stress response curves. In the current study, we compared three metrics of discrete-individual repeatability that incorporate the whole stress response curve: profile repeatability, Kullback-Leibler (KL) divergence, and hypothalamic-pituitary-adrenal (HPA) flexibility. As part of this work, we present a new R package for computing profile repeatability, "profrep." Using three datasets (one synthetic and two corticosterone datasets from live birds), our objectives were (1) to compare how these metrics correlate with one another and (2) to determine how representative repeatability scores of fewer replicates were to the "consensus" score (i.e., the score of the full dataset). We found that (1) these three discrete-individual repeatability metrics do not consistently correlate with one another; (2) KL divergence and HPA flexibility are poor at distinguishing individuals from each other (i.e., they give similar scores for each individual regardless of perceived repeatability); and (3) profile repeatability tends to overestimate repeatability when fewer replicates are available, and the consensus score is low. Despite this drawback of profile repeatability, we suggest that it may be the most well-suited metric for assessing discrete-individual repeatability.
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http://dx.doi.org/10.1093/iob/obaf005 | DOI Listing |
Integr Org Biol
February 2025
Department of Biology, Tufts University, 200 College Ave, Medford, MA 02155, USA.
There is currently no consensus on the most biologically meaningful way to calculate discrete-individual repeatability of stress response curves. In the current study, we compared three metrics of discrete-individual repeatability that incorporate the whole stress response curve: profile repeatability, Kullback-Leibler (KL) divergence, and hypothalamic-pituitary-adrenal (HPA) flexibility. As part of this work, we present a new R package for computing profile repeatability, "profrep.
View Article and Find Full Text PDFEntropy (Basel)
August 2022
CEVIPOF-Centre for Political Research, Sciences Po and CNRS, 1, Place Saint Thomas d'Aquin, 75007 Paris, France.
I review and extend the set of unifying principles that allow comparing all models of opinion dynamics within one single frame. Within the Global Unifying Frame (GUF), any specific update rule chosen to study opinion dynamics for discrete individual choices is recast into a probabilistic update formula. The associated dynamics is deployed using a general probabilistic sequential process, which is iterated via the repeated reshuffling of agents between successive rounds of local updates.
View Article and Find Full Text PDFJ Vis
January 2007
Faculty of Life Sciences, University of Manchester, Manchester, UK.
Sequence learning is common to all motor systems and is an essential aspect of human behavior necessary for the acquisition of motor skill. Many previous studies have demonstrated the ability to observe, store, and repeat sequences in a variety of modalities resulting in reduced reaction time. Recently, it has been found that subjects can make predictive smooth eye movements to a sequence of discrete horizontal target motions (C.
View Article and Find Full Text PDFMath Biosci
July 2000
Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor 48109-2117, USA.
Deterministic differential equation models indicate that partnership concurrency and non-homogeneous mixing patterns play an important role in the spread of sexually transmitted infections. Stochastic discrete-individual simulation studies arrive at similar conclusions, but from a very different modeling perspective. This paper presents a stochastic discrete-individual infection model that helps to unify these two approaches to infection modeling.
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