maxnet

0th

Percentile

Maxent over glmnet

Maxent species distribution modeling using glmnet for model fitting

Keywords
MaxEnt, glmnet
Usage
maxnet(p, data, f = maxnet.formula(p, data), regmult = 1, 
   regfun = maxnet.default.regularization, ...)
maxnet.default.regularization(p, m)

# S3 method for maxnet predict(object, newdata, clamp=T, type=c("link","exponential","cloglog","logistic"), ...) # S3 method for formula maxnet(p, data, classes="default")

Arguments
p
a vector of 1 (for presence) or 0 (for background).
data
a matrix or data frame of predictor variables.
f
a formula to determine the features to be used.
regmult
a constant to adjust regularization.
regfun
a function to compute regularization constant for each feature.
object
an object of class "maxnet", i.e., a fitted model.
newdata
values of predictor variables to predict to.
m
a matrix of feature values.
clamp
if true, predictors and features are restricted to the range seen during model training.
type
type of response required.
classes
continuous feature classes desired, either "default" or any subset of "lqpht" (for example, "lh").
not used.
Details

Using lp for the linear predictor and entropy for the entropy of the exponential model over the background data, the values plotted on the y-axis are: lp if type is "link". exp(lp) if type is "exponential". 1-exp(-exp(entropy+lp)) if type is "cloglog". 1/(1+exp(-entropy-lp)) if type is "logistic".

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

Aliases
  • maxnet
  • predict.maxnet
  • maxnet.formula
  • maxnet.default.regularization
Examples
library(maxnet)
data(bradypus)
p <- bradypus$presence
data <- bradypus[,-1]
mod <- maxnet(p, data)
plot(mod, type="cloglog")
mod <- maxnet(p, data, maxnet.formula(p, data, classes="lq"))
plot(mod, "tmp6190_ann")
Documentation reproduced from package maxnet, version 0.1.2, License: MIT + file LICENSE

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