tidysdm (version 1.0.0)

maxnet_fit: Wrapper to fit maxnet models with formulae

Description

This function is a wrapper around maxnet::maxnet, which takes a formula with data as well exposing parameters for normalisation in a manner compatible with parsnip. Users are unlikely to use this function directly. For the parsnip model specification for MaxEnt, see maxent().

Usage

maxnet_fit(
  formula,
  data,
  regmult = 1,
  classes = "default",
  regfun = maxnet::maxnet.default.regularization,
  addsamplestobackground = TRUE,
  ...
)

Value

Maxnet returns an object of class maxnet, which is a list consisting of a glmnet model with the following elements added:

betas

nonzero coefficients of the fitted model

alpha

constant offset making the exponential model sum to one over the background data

entropy

entropy of the exponential model

penalty.factor

the regularization constants used for each feature

featuremins

minimum of each feature, to be used for clamping

featuremaxs

maximum of each feature, to be used for clamping

varmin

minimum of each predictor, to be used for clamping

varmax

maximum of each predictor, to be used for clamping

samplemeans

mean of each predictor over samples (majority for factors)

levels

levels of each predictor that is a factor

Arguments

formula

a formula defining the outcome and the predictors

data

a data.frame with the outcomes and predictors

regmult

numeric, a constant to adjust regularization

classes

character, continuous feature classes desired, either "default" or any subset of "lqpht" (for example, "lh")

regfun

function, computes regularization constant for each feature

addsamplestobackground

logical, if TRUE then add to the background any presence sample that is not already there

...

currently not used.

Details

The response needs to be a factor with the class representing presences as the reference level of the factor (as expected by other classification models). A good guide to how options of a Maxent model work can be found in https://onlinelibrary.wiley.com/doi/full/10.1111/j.1600-0587.2013.07872.x

Examples

# we repeat the example in the `maxnet` package
data("bradypus", package = "maxnet")
bradypus_tb <- tibble::as_tibble(bradypus) %>%
  dplyr::mutate(presence = relevel(
    factor(
      dplyr::case_match(presence, 1 ~ "presence", 0 ~ "absence")
    ),
    ref = "presence"
  ))
mod <- maxnet_fit(presence ~ ., data = bradypus_tb, classes = "lq")
plot(mod, "tmp6190_ann")