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Last data update: 2014.03.03

R: Model averaging for multivariate generalized linear models

mamglm

R Documentation

Model averaging for multivariate generalized linear models

Description

Model averaging for multivariate GLM based on information theory.

Usage

mamglm(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`	Name of 'mvabund' object (character)
`family`	the 'family' object used.
`scale`	Whether to scale independent variables (default = TRUE)
`AIC.restricted`	Wheter 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

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

Wang, Y., Naumann, U., Wright, S.T. & Warton, D.I. (2012) mvabund- an R package for model-based analysis of multivariate abundance data. Methods in Ecology and Evolution, 3, 471-474.

Warton, D.I., Wright, S.T. & Wang, Y. (2012) Distance-based multivariate analyses confound location and dispersion effects. Methods in Ecology and Evolution, 3, 89-101.

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

Examples

#load species composition and environmental data
data(capcay)
#use a subset of data in this example to reduce run time
env_assem <- capcay$env_assem[,1:5]
freq.abs <- mvabund(log(capcay$abund+1))

#to fit a gaussian regression model to frequency data:
mamglm(data=env_assem,y="freq.abs",family="gaussian")

#to fit a binomial regression model to presence/absence data"
pre.abs0 <- capcay$abund
pre.abs0[pre.abs0>0] = 1
pre.abs <- mvabund(pre.abs0)

mamglm(data=env_assem,y="pre.abs",family="binomial")