mmiWMRR is a multimodel inference approach evaluating the relative
importance of predictors used in
A model of class
0 or higher integers possible (limit depends on sample size).
Remove smooth wavelets? If
Logical value indicating whether to print results to console.
It performs automatically
generated model selection and creates a model
selection table according to the approach of multi-model inference
(Burnham & Anderson, 2002). The analysis is carried out for scale-specific
regressions (i.e. where
scaleWMRR can be used). AIC is
used to obtain model
selection weights and to rank the models.
Furthermore, this function requires that all predictor variables
mmiWMRR returns a list containing the following elements
A matrix containing slopes, degrees of freedom, likelihood, AIC, delta, and weight values for the set of candidate models. The models are ranked by AIC.
An integer corresponding to scale
Burnham, K.P. & Anderson, D.R. (2002) Model selection and multimodel inference. Springer, New York.
Carl G, Doktor D, Schweiger O, Kuehn I (2016) Assessing relative variable importance across different spatial scales: a two-dimensional wavelet analysis. Journal of Biogeography 43: 2502-2512.
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