On the use of double quantile regression and visual assessment to estimate performance constraints

Quantile regression (QR) is a widely used tool for estimating performance limits in behavioral data. In Vazquez-Cardona et al. (2023, Behavioral Ecology), we applied mixed QR to estimate motor constraints in Adelaide’s warbler song, and measured performance as deviations from these estimated limits. A recent critique (Cardoso, 2024, Behavioral Ecology) raised two concerns about that analysis: (1) QR-based deviation scores arbitrarily emphasize one of the two variables in the trade-off, and (2) uneven sampling may produce the illusion of constraint, compromising the validity of visual assessments and QR. Here, we evaluate these claims by applying double quantile regression (DQR)—Cardoso’s proposed remedy—and by testing whether rarefaction alters the observed constraints. We introduce a novel method to characterize DQR results using bisecting lines and show that, for some distributions, DQR improves fit to the constrained edge. However, DQR performs poorly under some conditions and can fa..., , # Data and code for On the use of double quantile regression and visual assessment to estimate performance constraints

Author Information

Name: David Logue
ORCID: 0000-0003-3020-7101
Institution: University of Lethbridge
Address: 4401 University Drive / Lethbridge, AB T1K 3M4 / Canada
Email: [[e-mail]](mailto:[e-mail])

Name: Juleyska Vazquez-Cardona
Institution: University of Lethbridge
Address: 4401 University Drive / Lethbridge, AB T1K 3M4 / Canada
Email: [[e-mail]](mailto:[e-mail])

Name: Tyler Bonnell
Institution: University of Calgary
Address: 2500 University Dr NW / Calgary, AB T2N 1N4 / Canada
Email: [[e-mail]](mailto:[e-mail])

* Date of data collection:
Adelaide's warbler note data are from recordings during the periods 2012-03-03 - 2012-06-19 and 2017-04-13 - 2017-05-17

Other data were synthesized

* Geographic location of data collection:
Adelaide's warbler note data are from t...,