mglmn (version 0.1.0)

ses.maglm: 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.

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

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.

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., 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.

See Also

maglm, mamglm, ses.mamglm

Examples

# NOT RUN {
library(mvabund)
#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)
# }
# NOT RUN {
# }