gbm.fixed: gbm fixed

In dismo: Species Distribution Modeling

gbm fixed

Description

Calculates a gradient boosting (gbm) object with a fixed number of trees. The optimal number of trees can be identified using gbm.step or some other procedure. Mostly used as a utility function, e.g., when being called by gbm.simplify. It takes as input a dataset and arguments selecting x and y variables, learning rate and tree complexity.

Usage

gbm.fixed(data, gbm.x, gbm.y, tree.complexity = 1, site.weights = rep(1, nrow(data)),
 verbose = TRUE, learning.rate = 0.001, n.trees = 2000, bag.fraction = 0.5, 
 family = "bernoulli", keep.data = FALSE, var.monotone = rep(0, length(gbm.x)))

Arguments

data	data.frame
gbm.x	indices of the predictors in the input dataframe
gbm.y	index of the response in the input dataframe
tree.complexity	the tree depth - sometimes referred to as interaction depth
site.weights	by default set equal
verbose	to control reporting
learning.rate	controls speed of the gradient descent
n.trees	default number of trees
bag.fraction	varies random sample size for each new tree
family	can be any of "bernoulli", "poisson", "gaussian", or "laplace"
keep.data	Logical. If TRUE, original data is kept
var.monotone	constrain to positive (1) or negative monontone (-1)

Value

object of class gbm

Author(s)

John R. Leathwick and Jane Elith

References

Elith, J., J.R. Leathwick and T. Hastie, 2009. A working guide to boosted regression trees. Journal of Animal Ecology 77: 802-81

dismo documentation built on May 31, 2023, 6:15 p.m.