MyMaxLike: Model module: MyMaxLike
In zoonproject/modules: Species Distribution Modelling Code Modules for the ZOON Package

Description Usage Arguments Details Version Date submitted Data type Author(s) References See Also

Model module to fit a simple MyMaxLike model

1	MyMaxLike(.df)

.df

Internal parameter, do not use in the workflow function. .df is data frame that combines the occurrence data and covariate data. .df is passed automatically in workflow from the process module(s) to the model module(s) and should not be passed by the user.

The mymaxlike method aims to infer the parameters of a presence-absence logistic regression model using only presence-background data, in a manner somewhat similar to MaxEnt. The practical utility of this model has been questioned (see references), since it makes very strong assumptions about the underlying model which are unlikely to be met in practice. A more feature-rich version of the model is implemented in the mymaxlike::mymaxlike package, but that implementation only accepts raster/point data as input. This module implements a simple version of the model using the logistic link, in a format that interacts with the rest of the zoon system.

1.0

2015-11-13

presence/background

ZOON Developers, zoonproject@gmail.com

J.A. Royle, R.B. Chandler, C. Yackulic, and J.D. Nichols (2012). Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods in Ecology and Evolution, 3, 545-554.

Hastie, T. & Fithian, W. (2013) Inference from presence-only data; the ongoing controversy. Ecography, 36, 864-867.

Phillips, S.J. & Elith, J. (2013) On estimating probability of presence from use-availability or presence-background data. Ecology, 94, 1409-1419.

Merow, C. & Silander, J.A. (2014) A comparison of MyMaxlike and Maxent for modelling species distributions. Methods in Ecology and Evolution, 5, 215-225.

mymaxlike::mymaxlike

Other model: BiomodModel, Domain, GBM, LogisticRegression, MachineLearn, MaxEnt, MaxLike, MaxNet, NullModel, OptGRaF, QuickGRaF, RandomForest, StochasticLogisticRegression, mgcv