maxnet_fit {tidysdm} | R Documentation |
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()
.
maxnet_fit(
formula,
data,
regmult = 1,
classes = "default",
regfun = maxnet::maxnet.default.regularization,
addsamplestobackground = TRUE,
...
)
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. |
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
Maxnet returns an object of class maxnet
, which is a list
consisting of a glmnet model with the following elements added:
nonzero coefficients of the fitted model
constant offset making the exponential model sum to one over the background data
entropy of the exponential model
the regularization constants used for each feature
minimum of each feature, to be used for clamping
maximum of each feature, to be used for clamping
minimum of each predictor, to be used for clamping
maximum of each predictor, to be used for clamping
mean of each predictor over samples (majority for factors)
levels of each predictor that is a factor
# 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")