A farewell to the sum of Akaike weights: The benefits of alternative metrics for variable importance estimations in model selection

By M. Galipaud, Mark A. F. Gillingham, F. Dechaume‐Moncharmont. 2017

81 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/916315089482a30eabe27d85beb4c3625b207ae2

How researchers see this paper

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

In Ecological Modeling

1. David Hodgson
2. Maria Eugenia Correa-Cano
3. Beth S Robinson
4. Richard Inger
5. Lynda Donaldson
6. David N. Fisher
7. J. Evans
8. Cecily E. D. Goodwin

In Ecology, Evolution, Behavior and Systematics

1. Richard Inger
2. David Hodgson
3. Maria Eugenia Correa-Cano
4. Beth S Robinson
5. Lynda Donaldson
6. David N. Fisher
7. J. Evans
8. Cecily E. D. Goodwin

In Nature and Landscape Conservation

1. Richard Inger
2. David Hodgson
3. Maria Eugenia Correa-Cano
4. Beth S Robinson
5. Lynda Donaldson
6. David N. Fisher
7. J. Evans
8. Cecily E. D. Goodwin

Citations in the corpus

• A brief introduction to mixed effects modelling and multi-model inference in ecology (2018, background): “…that this approach is flawed because Akaike weights are interpreted as relative model probabilities, and give no information about the importance of individual predictors in a model (Cade 2015), and fail to distinguish between variables with weak or strong effects (Galipaud et al 2014; 2017).”