gbm.step
gbm step
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.
- Keywords
- spatial
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.
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
# NOT RUN {
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)
# }