maxnet {maxnet}

Maxent over 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")