Maxent over glmnet

Description

Maxent species distribution modeling using glmnet for model fitting

Details

Create Maxent models for species distributions from presence and background data, using the glmnet package to do the model fitting. By default, feature sets and regularization are the same as the Maxent java application.

Author(s)

Steven Phillips <phillips@research.att.com>

References

Phillips & Dudik Fithian & Hastie Glmnet

Occurrence records and background data for the brown-throated three-toed sloth, Bradypus variegatus

Description

A dataset containing environmental data at 116 Bradypus variegatus occurrence points and 1000 background points in South and Central America. Occurrence data are from Anderson and Handley (2001); see Phillips et al. (2006) for descriptions of the predictor variables.

Usage

bradypus

Format

An object of class data.frame with 1116 rows and 15 columns.

References

Anderson, R. P. and Handley, Jr., C. O. (2001). A new species of three-toed sloth (Mammalia: Xenarthra) from Panama, with a review of the genus Bradypus. Proceedings of the Biological Society of Washington 114, 1-33.

Phillips, S. J. et al. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231-259

Maxent feature classes

Description

Create and evaluate Maxent's feature classes

Usage

hinge(x, nknots = 50)
thresholds(x, nknots=50)
categorical(x)

Arguments

Value

These functions are typically called by model.matrix rather than directly by a user. hinge, threshold and categorical return a matrix with a column for each feature of the specified type. hinge creates 2*nknots-2 hinge features, half with min=min(x) and half with max=max(x), and knots evenly spaced between min(x) and max(x). A hinge feature h(min,knot) or h(knot,max) is 0 if the predictor is below the first argument, 1 if the predictor is above the second argument, and linearly interpolated inbetween. A threshold feature is 1 if the predictor is above the knot, 0 otherwise. A categorical feature is 1 if the predictor matches the category and 0 otherwise.

Author(s)

Steven Phillips

Examples

library(maxnet)
data(bradypus)
hinge(bradypus$tmp6190_ann,nknots=10)
categorical(bradypus$ecoreg)

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

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:

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")

Response plots for maxnet models

Description

Create response plots for each predictor in a maxnet model

Usage

## S3 method for class 'maxnet'
plot(x, vars = names(x$samplemeans), common.scale = T, 
   type = c("link", "exponential", "cloglog", "logistic"), ylab = NULL, ...)

response.plot(mod, v, type, mm=mod$samplemeans, min=mod$varmin[v], max=mod$varmax[v], 
   levels=unlist(mod$levels[v]), plot=T, xlab=v, ylab=tools::toTitleCase(type), ...)

Arguments

Value

If plot is false, return a vector of y values, one for each factor or 100 ranging from min - 0.1*(max-min) to max + 0.1*(max-min).

Author(s)

Steven Phillips