gbm.step.sd {gbm.auto}

Function to assess optimal no of boosting trees using k-fold cross validation

Description

SD fork of dismo's gbm.step to add evaluation metrics like d.squared and rmse. J. Leathwick and J. Elith - 19th September 2005, version 2.9. Function to assess optimal no of boosting trees using k-fold cross validation. Implements the cross-validation procedure described on page 215 of Hastie T, Tibshirani R, Friedman JH (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction Springer-Verlag, New York.

Usage

gbm.step.sd(
  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

The input dataframe.

gbm.x

The predictors.

gbm.y

The response.

offset

Allows an offset to be specified.

fold.vector

Allows a fold vector to be read in for CV 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 p/a 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

Plot hold-out deviance curve.

plot.folds

Plot the individual folds as well.

verbose

Control amount of screen reporting.

silent

To allow running with no output for simplifying model).

keep.fold.models

Keep the fold models from cross valiation.

keep.fold.vector

Allows the vector defining fold membership to be kept.

keep.fold.fit

Allows the predicted values for observations from CV to be kept.

...

Allows for any additional plotting parameters.

Details

Divides the data into 10 subsets, with stratification by prevalence if required for pa data 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, calculates the average holdout residual deviance and its standard error then identifies the optimal number of trees as that at which the holdout deviance is minimised and fits a model with this number of trees, returning it as a gbm model along with additional information from the cv selection process.

requires gbm library from Cran requires roc and calibration scripts of J Elith requires calc.deviance script of J Elith/J Leathwick

Value

GBM models using gbm as the engine.