maxnet (version 0.1.1)

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, ...)
maxnet.default.regularization(p, m)
"predict"(object, newdata, clamp=T, type=c("link","exponential","cloglog","logistic"), ...)
"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.

Value

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

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".

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")