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. 2023 Apr 25;9(1):vead026. doi: 10.1093/ve/vead026

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© The Author(s) 2023. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 2. — ΔAIC profiles for connected component (red/non-blue) and Markov clustering (MCL, blue) methods under a range of Tamura–Nei (TN93) distance thresholds. More negative ΔAIC values indicate less information loss when incorporating additional predictor variables into a Poisson regression of new nodes among clusters (Chato, Kalish and Poon 2020). Each point represents one of 420 parameter combinations, specifically the distance threshold ( $Inline graphic$ ) and the expansion (k) and inflation (r) parameters of the MCL method. Solid lines correspond to cubic smoothing splines fit to each set of points.

Inline graphic — ΔAIC profiles for connected component (red/non-blue) and Markov clustering (MCL, blue) methods under a range of Tamura–Nei (TN93) distance thresholds. More negative ΔAIC values indicate less information loss when incorporating additional predictor variables into a Poisson regression of new nodes among clusters (Chato, Kalish and Poon 2020). Each point represents one of 420 parameter combinations, specifically the distance threshold ( $Inline graphic$ ) and the expansion (k) and inflation (r) parameters of the MCL method. Solid lines correspond to cubic smoothing splines fit to each set of points.