gbm.fixed
From dismo v1.14
by Robert Hijmans
gbm fixed
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.
 Keywords
 spatial
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
References
Elith, J., J.R. Leathwick and T. Hastie, 2009. A working guide to boosted regression trees. Journal of Animal Ecology 77: 80281
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