ses.mamglm {mglmn} | 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) |
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 |
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., C. J. Burwell, C. L. Lambkin, M. Katabuchi, A. McDougall, R. J. Raven, and V. J. Neldner. (2015) The role of human disturbance in island biogeography of arthropods and plants: an information theoretic approach. Journal of Biogeography 42:1406-1417.
Examples
library(mvabund)
#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)