ENMeval (version 2.0.5)

parse_lambdas: Parse Maxent lambdas information

Description

NOTICE: This function was borrowed from the rmaxent package written by John Baumgartner (https://github.com/johnbaums/rmaxent/). (dependencies on Github-only packages are not allowed for CRAN).

Parse Maxent .lambdas files to extract the types, weights, minima and maxima of features, as well as the fitted model's entropy and other values required for predicting to new data.

Usage

parse_lambdas(lambdas)

Value

A list (of class `lambdas`) with five elements: * `lambdas`: a `data.frame` describing the features used in a Maxent model, including their weights (lambdas), maxima, minima, and type; * `linearPredictorNormalizer`: a constant that ensures the linear predictor (the sum of clamped features multiplied by their respective feature weights) is always negative (for numerical stability); * `densityNormalizer`: a scaling constant that ensures Maxent's raw output sums to 1 over background points; * `numBackgroundPoints`: the number of background points used in model training; and * `entropy`: the entropy of the fitted model.

Arguments

lambdas

Either a `MaxEnt` fitted model object (fitted with the `maxent` function in the `dismo` package), or a file path to a Maxent .lambdas file.

References

* Wilson, P. W. (2009) [_Guidelines for computing MaxEnt model output values from a lambdas file_](http://gis.humboldt.edu/OLM/Courses/GSP_570/Learning%20Modules/10%20BlueSpray_Maxent_Uncertinaty/MaxEnt%20lambda%20files.pdf). * _Maxent software for species habitat modeling, version 3.3.3k_ help file (software freely available [here](https://www.cs.princeton.edu/~schapire/maxent/)).

Examples

if (FALSE) {
# Below we use the predicts::MaxEnt example to fit a model:
library(predicts)
occs <- read.csv(file.path(system.file(package="predicts"),
"/ex/bradypus.csv"))[,2:3]
predictors <- rast(file.path(system.file(package='predicts'), '/ex/bio.tif'))
me <- MaxEnt(predictors, occs)
# ... and then parse the lambdas information:
lam <- parse_lambdas(me)
lam
str(lam, 1)
}