maxnet: Maxent over glmnet

In maxnet: Fitting 'Maxent' Species Distribution Models with 'glmnet'

Description

Maxent species distribution modeling using glmnet for model fitting

Usage

maxnet(p, data, f = maxnet.formula(p, data), regmult = 1, 
   regfun = maxnet.default.regularization, addsamplestobackground=T, ...)
maxnet.default.regularization(p, m)
maxnet.formula(p, data, classes="default")

## S3 method for class 'maxnet'
predict(object, newdata, clamp=T, type=c("link","exponential","cloglog","logistic"), ...)

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.
addsamplestobackground	if T, add to the background any presence sample that is not already there.
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

Author(s)

Steven Phillips

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")

Example output

maxnet documentation built on July 9, 2021, 5:08 p.m.