Model averaging for GLM based on information theory.
Usage
maglm(data, y, family, scale = TRUE, AIC.restricted = FALSE)
Arguments
data
Data frame, typically of environmental variables. Rows for sites and colmuns for environmental variables.
y
Vector of independent variables.
family
the 'family' object used.
scale
Whether to scale independent variables (default = TRUE)
AIC.restricted
Whether to use AICc (TRUE) or AIC (FALSE) (default = TRUE).
Value
A list of results
res.table
data frame with "AIC", AIC of the model, "log.L", log-likelihood of the model, "delta.aic", AIC difference to the best model, "wAIC", weighted AIC to the model, "n.vars", number of variables in the model, and each term.
importance
vector of relative importance value of each term, caluclated as as um of the weighted AIC over all of the model in whith the term aperars.
family
the 'family' object used.
Author(s)
Masatoshi Katabuchi <mattocci27@gmail.com>
References
Dobson, A. J. (1990) An Introduction to Generalized Linear Models. London: Chapman and Hall.
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
See Also
mamglm,
ses.maglm,
ses.mamglm
Examples
#load sspecies composition and environmental data
data(capcay)
adj.sr <- capcay$adj.sr
env_sp <- capcay$env_sp
#to fit a regression model:
maglm(data=env_sp, y="adj.sr", family="gaussian",AIC.restricted=TRUE)