R: Standardized effect size of relative importance values for...
ses.maglm
R Documentation
Standardized effect size of relative importance values for mamglm
Description
Standardized effect size of relative importance values for model averaging mutlivariate GLM.
Usage
ses.maglm(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
Vector of independent variables.
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).
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
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
maglm,
mamglm,
ses.mamglm
Examples
#load species composition and environmental data
data(capcay)
adj.sr <- capcay$adj.sr
#use a subset of data in this example to reduce run time
env_sp <- capcay$env_sp[,1:5]
#to execute calculations on a single core:
ses.maglm(data=env_sp, y="adj.sr", par=FALSE,family="gaussian", runs=4)
## Not run:
#to execute parallel calculations:
sfInit(parallel=TRUE, cpus=4)
sfExportAll()
ses.maglm(data=env_sp, y="adj.sr", par=TRUE,family="gaussian", runs=4)
## End(Not run)