Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests.

By David Posada, T. Buckley. Systematic Biology, 2004

3,936 citations recorded by the source corpus.

Subfields: Genetics, Nature and Landscape Conservation, Paleontology

View Paper: https://www.semanticscholar.org/paper/f99ae00d1244aea3623471a24ee94f1f408398ab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Genetics

1. Koichiro Tamura
2. Sudhir Kumar
3. Glen Stecher
4. David Posada
5. Olivier Gascuel
6. Alexei J. Drummond
7. Matthew E. Neilson
8. Carol A. Stepien

In Nature and Landscape Conservation

1. I. Sanmartín
2. Gonzalo Giribet
3. Liezl-Marié van der Westhuizen
4. Eva Wallander
5. R. E. Clopton
6. G. Sileshi
7. Prashant P. Sharma
8. C. Bock

In Paleontology

1. David Posada
2. Gonzalo Giribet
3. Sarah L. Boyer
4. I. Sanmartín
5. Prashant P. Sharma
6. R. Voss
7. S. Jansa
8. Alexei J. Drummond

Citations in the corpus

• ProtTest 3: fast selection of best-fit models of protein evolution (2011, neutral): “…fixed BIONJ, BIONJ, ML or user defined; (iv) four information criteria: AIC, BIC, AICc and DT (see Sullivan and Joyce 2005); (v) reconstruction of model-averaged phylogenetic trees (Posada and Buckley, 2004); (vi) fault tolerance with checkpointing; and (vii) automatic logging of the user activity.”
• Phylogenetic analysis of morphology in Prunus reveals extensive homoplasy (2006, methodology): “because it has been shown to be superior to the likelihood ratio test ( Posada and Buckley 2004 ).”
• Genetic Improvement of U.S. Soybean in Maturity Groups II, III, and IV (2014, background): “The AIC rewards models with a better coefficient of determination (R2) fit, but penalizes models that require more parameters to achieve the better fit (Sakamoto et al., 1986; Posada and Buckley, 2004).”
• Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model (2016, methodology): “BIC combines the factors of likelihood and number of free parameters to be estimated.”
• Azadinium spinosum gen. et sp. nov. (Dinophyceae) identified as a primary producer of azaspiracid toxins (2009, methodology): “Selected substitution model parameters obtained with Modeltest version 3.7 (Posada & Crandall, 1998; Posada & Buckley, 2004) for each of the four marker regions utilized in this study.”
• MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. (2011, methodology): “The goodness-of-fit of each model to the data is measured by the Bayesian information criterion (BIC, Schwarz 1978) and corrected Akaike information criterion (AICc, Hurvich and Tsai 1989) (see also Posada and Buckley 2004).”
• Systematics of Fraxinus (Oleaceae) and evolution of dioecy (2008, methodology): “Following the recommendations of recent works (Pol 2004; Posada and Buckley 2004), the evolutionary models chosen by the Akaike information criterion, the GTR + G model for ITS1 and ITS2 and the k80 + I model for the nearly invariable 5.”
• A phylogenetic supertree of oscine passerine birds (Aves: Passeri) (2006, neutral): “2001; Posada & Buckley 2004).”
• Welcome back New Zealand: regional biogeography and Gondwanan origin of three endemic genera of mite harvestmen (Arachnida, Opiliones, Cyphophthalmi) (2009, methodology): “For the static alignment of each data set, as well as for the entire concatenated data set, we determined the best-fit model using Modeltest 3.7 (Posada & Crandall, 1998) under the Akaike information criterion (AIC), as recommended by Posada & Buckley (2004).”
• Brand loyalty: Exploring self-brand connection and brand experience (2018, methodology): “Owing to the BIC value being smaller and indicating the best model fit (Posada and Buckley, 2004; Van de Schoot et al., 2012), brand experience was included in the model as a second-order reflective construct with the four underlying dimensions (as proposed by Brakus et al., 2009).”
• Accurate Measurement of the Effects of All Amino-Acid Mutations on Influenza Hemagglutinin (2016, methodology): “Aikake information criterion (AIC) [26] was used compare the maximum likelihood phylogenetic fit of several models to an alignment of seasonal human H1N1 and classical swine H1N1 HAs.”
• Phylogenetic inferences in Prunus (Rosaceae) using chloroplast ndhF and nuclear ribosomal ITS sequences (2008, neutral): “3.7 (Posada & Crandall, 1998; Posada & Buckley, 2004).”
• Zangia, a new genus of Boletaceae supported by molecular and morphological evidence (2011, methodology): “The parameter model was selected by the Akaike information criterion (AIC) as the best-fit likelihood model with Modeltest 3.7 (Posada and Buckley 2004).”