gbm.step: gbm step

In dismo: Species Distribution Modeling

gbm step

Description

Function to assess the optimal number of boosting trees using k-fold cross validation. This is an implementation of the cross-validation procedure described on page 215 of Hastie et al (2001).

The data is divided into 10 subsets, with stratification by prevalence if required for presence/absence data. The function then fits a gbm model of increasing complexity along the sequence from n.trees to n.trees + (n.steps * step.size), calculating the residual deviance at each step along the way. After each fold processed, the function calculates the average holdout residual deviance and its standard error and then identifies the optimal number of trees as that at which the holdout deviance is minimised. It fits a model with this number of trees, returning it as a gbm model along with additional information from the cross-validation selection process.

Usage

gbm.step(data, gbm.x, gbm.y, offset = NULL, fold.vector = NULL, tree.complexity = 1,
 learning.rate = 0.01, bag.fraction = 0.75, site.weights = rep(1, nrow(data)), 
 var.monotone = rep(0, length(gbm.x)), n.folds = 10, prev.stratify = TRUE, 
 family = "bernoulli", n.trees = 50, step.size = n.trees, max.trees = 10000,
 tolerance.method = "auto", tolerance = 0.001, plot.main = TRUE, plot.folds = FALSE,
 verbose = TRUE, silent = FALSE, keep.fold.models = FALSE, keep.fold.vector = FALSE, 
 keep.fold.fit = FALSE, ...)

Arguments

data	input data.frame
gbm.x	indices or names of predictor variables in data
gbm.y	index or name of response variable in data
offset	offset
fold.vector	a fold vector to be read in for cross validation with offsets
tree.complexity	sets the complexity of individual trees
learning.rate	sets the weight applied to inidivudal trees
bag.fraction	sets the proportion of observations used in selecting variables
site.weights	allows varying weighting for sites
var.monotone	restricts responses to individual predictors to monotone
n.folds	number of folds
prev.stratify	prevalence stratify the folds - only for presence/absence data
family	family - bernoulli (=binomial), poisson, laplace or gaussian
n.trees	number of initial trees to fit
step.size	numbers of trees to add at each cycle
max.trees	max number of trees to fit before stopping
tolerance.method	method to use in deciding to stop - "fixed" or "auto"
tolerance	tolerance value to use - if method == fixed is absolute, if auto is multiplier * total mean deviance
plot.main	Logical. plot hold-out deviance curve
plot.folds	Logical. plot the individual folds as well
verbose	Logical. control amount of screen reporting
silent	Logical. to allow running with no output for simplifying model)
keep.fold.models	Logical. keep the fold models from cross valiation
keep.fold.vector	Logical. allows the vector defining fold membership to be kept
keep.fold.fit	Logical. allows the predicted values for observations from cross-validation to be kept
...	Logical. allows for any additional plotting parameters

Value

object of S3 class gbm

Note

This and other boosted regression trees (BRT) functions in the dismo package do not work if you use only one predictor. There is an easy work around: make a dummy variable with a constant value and then fit a model with two predictors, the one of interest and the dummy variable, which will be ignored by the model fitting as it has no useful information.

Author(s)

John R. Leathwick and Jane Elith

References

Hastie, T., R. Tibshirani, and J.H. Friedman, 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York Elith, J., J.R. Leathwick and T. Hastie, 2009. A working guide to boosted regression trees. Journal of Animal Ecology 77: 802-81

Examples

data(Anguilla_train)
# reduce data set to speed things up a bit
Anguilla_train = Anguilla_train[1:200,]
angaus.tc5.lr01 <- gbm.step(data=Anguilla_train, gbm.x = 3:14, gbm.y = 2, family = "bernoulli",
       tree.complexity = 5, learning.rate = 0.01, bag.fraction = 0.5)

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