ses.mamglm: Standardized effect size of relative importance values for...

In mglmn: Model Averaging for Multivariate GLM with Null Models

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)

mglmn documentation built on July 29, 2020, 5:06 p.m.