mamgllvm: Model averaging for multivariate generalized linear latent...

In mattocci27/mglmn: Model Averaging for Multivariate GLMM with Null Models

Description

Model averaging for multivariate GLLVM based on information theory.

Usage

mamgllvm(data, y, family, scale = TRUE, AIC.restricted = FALSE)

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	Whether to use AICc (TRUE) or AIC (FALSE) (default = TRUE).

Value

A list of results

res.table	data frame with "AIC", AIC of the model, "log.L", log-likelihood of the model, "delta.aic", AIC difference to the best model, "wAIC", weighted AIC to the model, "n.vars", number of variables in the model, and each term.
importance	vector of relative importance value of each term, caluclated as as um of the weighted AIC over all of the model in whith the term aperars.
family	the 'family' object used.

References

Burnham, K.P. & Anderson, D.R. (2002) Model selection and multi-model inference: a practical information-theoretic approach. Springer Verlag, New York.

Niku, J., Warton, D. I., Hui, F. K. C., and Taskinen, S. (2017). Generalized linear latent variable models for multivariate count and biomass data in ecology. Journal of Agricultural, Biological, and Environmental Statistics, 22:498-522.

Niku, J., Brooks, W., Herliansyah, R., Hui, F. K. C., Taskinen, S., and Warton, D. I. (2018). Efficient estimation of generalized linear latent variable models. PLoS One, 14(5):1-20.

Warton, D. I., Guillaume Blanchet, F., O'Hara, R. B., Ovaskainen, O., Taskinen, S., Walker, S. C. and Hui, F. K. C. (2015). So many variables: Joint modeling in community ecology. Trends in Ecology & Evolution, 30:766-779.

See Also

maglm, ses.maglm, ses.mamglm

Examples

#load species composition and environmental data
library(mvabund)
data(capcay)
#use a subset of data in this example to reduce run time
env_assem <- capcay$env_assem[, 1:2]
freq.abs <- mvabund(log(capcay$abund + 1))

#to fit a gaussian regression model to frequency data:
mamgllvm(data = env_assem, y = "freq.abs", family = "gaussian")

#to fit a binomial regression model to presence/absence data"
pre.abs0 <- capcay$abund
pre.abs0[pre.abs0 > 0] = 1
pre.abs <- mvabund(pre.abs0)

mamgllvm(data = env_assem, y = "pre.abs", family = "binomial")

mattocci27/mglmn documentation built on Aug. 3, 2020, 8:57 p.m.