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, keep.data = FALSE, plot.main = TRUE,
plot.folds = FALSE, verbose = TRUE, silent = FALSE, keep.fold.models = FALSE,
keep.fold.vector = FALSE, keep.fold.fit = FALSE, ...)
Arguments
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
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
keep.data
Logical. keep raw data in final model
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