Maxent over glmnet
Maxent species distribution modeling using glmnet for model fitting
maxnet(p, data, f = maxnet.formula(p, data), regmult = 1,
regfun = maxnet.default.regularization, ...)
maxnet.default.regularization(p, m)
## S3 method for class 'maxnet'
predict(object, newdata, clamp=T, type=c("link","exponential","cloglog","logistic"), ...)
## S3 method for class 'formula'
maxnet(p, data, classes="default")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 |
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. |
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".
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 |
Steven Phillips
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")
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