Description Usage Arguments Value References See Also Examples

Model averaging for multivariate GLM based on information theory.

1 |

`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). |

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. |

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
#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$env_assem[, 1:5]
freq.abs <- mvabund(log(capcay$abund + 1))
#to fit a gaussian regression model to frequency data:
mamglm(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)
mamglm(data = env_assem, y = "pre.abs", family = "binomial")
``` |

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