gbm.fixed: gbm fixed
In dismo: Species Distribution Modeling

Description Usage Arguments Value Author(s) References

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.

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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)))

`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)