Maxent-like Generalized Linear Models (GLM)

This function fits a Generalized Linear Model (GLM) to binary presence-background data. It allows for the specification of custom weights, with a default in which presences have a weight of 1 and background 100.

Usage

glm_mx(formula, family = binomial(link = "cloglog"), data,
       weights = NULL, ...)

Arguments

formula

A formula specifying the model to be fitted, in the format used by glm.

family

A description of the error distribution and link function to be used in the model. Defaults to binomial(link = "cloglog"), which is commonly used for presence-background data.

data

A data.frame containing the variables in the model. Must include a column named pr_bg that indicates whether a record is a presence (1) or background (0), and at least another column with an independent variable (predictor).

weights

Optional. A numeric vector of weights for each observation. If not provided, default weights of 1 for presences and 100 for background are used.

...

Additional arguments to be passed to glm.

Value

A fitted glm object. The model object includes the minimum and maximum values of the non-factor variables in the dataset, stored as model$varmin and model$varmax.

Details

For more details about glms using presence and background emulating what Maxent does, see Fithian and Hastie (2013) doi:10.1214/13-AOAS667.