# gbm.fixed

##### 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: 802-81

*Documentation reproduced from package dismo, version 1.3-3, License: GPL (>= 3)*