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Tools for univariate and multivariate generalized linear models with model averaging and null model technique (Nakamura et al. 2015).

The package provides functions to estimate the relative importance of predictor variables in univariate and multivariate generalized linear models. The relative importance predictor variables is calculated by summing the Akaike weights of all models in which that predictor variable is included (Burnham & Anderson, 2002). The sum of the Akaike weights indicates the importance of a variable in explaining variation in a given dataset, relative to other predictor variables included in the analysis. The significance of each predictor variable can be assessed by the null model approach.

The key functions in this package are the following.


From within R (>= 3.0.0), you can install: * the latest version of mglmn from CRAN with r install.packages(“mglmn”)


Burnham, K.P. & Anderson, D.R. (2002) Model selection and multi-model inference: a practical information-theoretic approach. Springer Verlag, New York.

Nakamura A., Burwell C.J., Lambkin C.L., Katabuchi M., McDougall A., Raven R.J. and Neldner V.J. (2015), The role of human disturbance in island biogeography of arthropods and plants: an information theoretic approach, Journal of Biogeography, DOI: 10.1111/jbi.12520

mattocci27/mglmn documentation built on Aug. 23, 2018, 3:01 p.m.