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Last data update: 2014.03.03

R: Standardized effect size of relative importance values for...

ses.mamglm

R Documentation

Standardized effect size of relative importance values for mamglm

Description

Standardized effect size of relative importance values for model averaging GLM.

Usage

ses.mamglm(data, y, family, scale = TRUE,  AIC.restricted = TRUE, par=FALSE, runs = 999)

Arguments

`data`	Data frame, typically of environmental variables. Rows for sites and colmuns for environmental variables.
`y`	Name of 'mvabund' object (character)
`scale`	Whether to scale independent variables (default = TRUE)
`family`	the 'family' object used.
`AIC.restricted`	Wheter to use AICc (TRUE) or AIC (FALSE) (default = TRUE).
`par`	Wheter to use parallel computing (default = FALSE)
`runs`	Number of randomizations.

Details

The currently implemented null model shuffles the set of environmental variables across sites, while maintains species composition. Note that the function would take considerable time to execute.

Value

A data frame of resluts for each term

`res.obs`	Observed importance of terms
`res.rand.mean`	Mean importance of terms in null communites
`res.rand.sd`	Standard deviation of importance of terms in null communites
`SES`	Standardized effect size of importance of terms (= (res.obs - res.rand.mean) / res.rand.sd)
`res.obs.rank`	Rank of observed importance of terms vs. null communites
`runs`	Number of randomizations

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$env_assem[,1:5]
pre.abs0 <- capcay$abund
pre.abs0[pre.abs0>0] = 1
pre.abs <- mvabund(pre.abs0)

#to execute calculations on a single core:
ses.mamglm(data=env_assem,y="pre.abs", par=FALSE, family="binomial",AIC.restricted=FALSE,runs=4)


## Not run: 
#to execute parallel calculations:
sfInit(parallel=TRUE, cpus=4)
sfExportAll()
ses.mamglm(data=env_assem,y="pre.abs", par=TRUE, family="binomial",AIC.restricted=FALSE,runs=4)

## End(Not run)