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

Ryan P. Franckowiak, Michael Panasci, Karl J. Jarvis, Ian S. Acuña-Rodriguez, Erin L. Landguth, Marie Josée Fortin, Helene H. Wagner

Research output: Contribution to journalArticlepeer-review

Abstract

In 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.

Original languageEnglish
Article numbere0175194
JournalPLoS ONE
Volume12
Issue number4
DOIs
StatePublished - Apr 2017

Fingerprint

Dive into the research topics of 'Model selection with multiple regression on distance matrices leads to incorrect inferences'. Together they form a unique fingerprint.

Cite this