glmnet_mx: Maxent-like glmnet models

In kuenm2: Detailed Development of Ecological Niche Models

Maxent-like glmnet models

Description

This function fits Maxent-like models using the glmnet package, designed for presence-background data.

Usage

glmnet_mx(p, data, f, regmult = 1.0, regfun = maxnet.default.regularization,
          addsamplestobackground = TRUE, weights = NULL, ...)

Arguments

p	A vector of binary presence-background labels, where 1 indicates presence and 0 indicates background.
data	A data.frame containing the predictor variables for the model. This must include the same number of rows as the length of p.
f	A formula specifying the model to be fitted, in the format used by model.matrix.
regmult	(numeric) Regularization multiplier, default is 1.0.
regfun	A function that calculates regularization penalties. Default is maxnet.default.regularization.
addsamplestobackground	(logical) Whether to add presence points not in the background to the background data. Default is TRUE.
weights	(numeric) A numeric vector of weights for each observation. Default is NULL, which sets weights to 1 for presence points and 100 for background points.
...	Additional arguments to pass to glmnet.

Details

This function is modified from the package maxnet and fits a Maxent-like model using regularization to avoid over-fitting. Regularization weights are computed using a provided function (which can be changed) and can be multiplied by a regularization multiplier (regmult). The function also includes an option to calculate AIC.

Value

A fitted Maxent-like model object of class glmnet_mx, which includes model coefficients, AIC (if requested), and other elements such as feature mins and maxes, sample means, and entropy.

kuenm2 documentation built on April 21, 2026, 1:07 a.m.