Model selection with multiple regression on distance matrices leads to incorrect inferences

dc.creatorFranckowiak, Ryan P.
dc.creatorPanasci, Michael (TTU)
dc.creatorJarvis, Karl J.
dc.creatorAcuña-Rodriguez, Ian S.
dc.creatorLandguth, Erin L.
dc.creatorFortin, Marie-Josée
dc.creatorWagner, Helene H.
dc.date.accessioned2022-08-31T23:43:34Z
dc.date.available2022-08-31T23:43:34Z
dc.date.issued2017
dc.description© 2017 Franckowiak et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.description.abstractIn landscape genetics, model selection procedures based on Information Theoretic and Bayesian principles have been used with multiple regression on distance matrices (MRM) to test the relationship between multiple vectors of pairwise genetic, geographic, and environmental distance. Using Monte Carlo simulations, we examined the ability of model selection criteria based on Akaike’s information criterion (AIC), its small-sample correction (AICc), and the Bayesian information criterion (BIC) to reliably rank candidate models when applied with MRM while varying the sample size. The results showed a serious problem: all three criteria exhibit a systematic bias toward selecting unnecessarily complex models containing spurious random variables and erroneously suggest a high level of support for the incorrectly ranked best model. These problems effectively increased with increasing sample size. The failure of AIC, AICc, and BIC was likely driven by the inflated sample size and different sum-of-squares partitioned by MRM, and the resulting effect on delta values. Based on these findings, we strongly discourage the continued application of AIC, AICc, and BIC for model selection with MRM.en_US
dc.identifier.citationFranckowiak RP, Panasci M, Jarvis KJ, Acuña-Rodriguez IS, Landguth EL, Fortin M-J, et al. (2017) Model selection with multiple regression on distance matrices leads to incorrect inferences. PLoS ONE 12(4): e0175194. https://doi.org/10.1371/journal.pone.0175194en_US
dc.identifier.urihttps://doi.org/10.1371/journal.pone.0175194
dc.identifier.urihttps://hdl.handle.net/2346/90126
dc.language.isoengen_US
dc.subjectSimulation and Modelingen_US
dc.subjectGeneticsen_US
dc.subjectGene Flowen_US
dc.subjectRandom Variablesen_US
dc.subjectMonte Carlo Methoden_US
dc.subjectSpatial Autocorrelationen_US
dc.subjectRegression Analysisen_US
dc.subjectStatistical Dataen_US
dc.titleModel selection with multiple regression on distance matrices leads to incorrect inferencesen_US
dc.typeArticleen_US

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
panasci_article.pdf
Size:
1.8 MB
Format:
Adobe Portable Document Format
Description:
Main article with TTU Libraries cover page

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.57 KB
Format:
Item-specific license agreed upon to submission
Description:

Collections