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# Author: Babak Naimi, naimi.b@gmail.com
# Date (last update): July 2017
# Version 1.1
# Licence GPL v3
#-------------
methodInfo <- list(name=c('brt','BRT','gbm','GBM'),
packages='gbm',
modelTypes = c('pa','pb','ab','n'),
fitParams = list(formula='standard.formula',data='sdmDataFrame'),
fitSettings = list(distribution='bernoulli',
n.trees=1000,
interaction.depth=1,
n.minobsinnode = 10,
shrinkage = 0.001,
bag.fraction = 0.5,
train.fraction = 1.0,
cv.folds=0,
keep.data = TRUE,
verbose = "CV",
class.stratify.cv=NULL),
fitFunction = 'gbm',
settingRules = function(x='sdmVariables',f='fitSettings') {
#if (!is.null(userSetting)) fitSettings <- .assign(fitSettings,userSettings)
#else if (x@distribution == 'ab') fitSettings[['distribution']] <- "poisson"
#else if (x@distribution == 'n') fitSettings[['distribution']] <- "multinomial"
if (x@distribution %in% c('poisson','multinomial')) {
f[['distribution']] <- x@distribution
}
if (x@number.of.records[1] < 55) {
f[['n.minobsinnode']] <- max(floor(x@number.of.records[1] / 5),3)
if (x@number.of.records[1] < 30) f[['bag.fraction']] <- 0.9
}
list(fitSettings=f)
},
tuneParams = NULL,
predictParams=list(object='model',newdata='sdmDataFrame'),
predictSettings=list(n.trees=1000,type='response'),
predictFunction='predict.gbm',
#------ metadata (optional):
title='Boosted Regression Trees',
creator='Babak Naimi',
authors=c('Greg Ridgeway'), # authors of the main method
email='naimi.b@gmail.com',
url='http://r-gis.net',
citation=list(bibentry('Article',title = "A working guide to boosted regression trees",
author = as.person("J. Elith [aut], J. R. Leathwick [aut], T. Hastie [aut]"),
year = "2008",
journal = "Journal of Animal Ecology",
number="77",
pages="802-813",
publisher="Wiley Online Library")
),
description='Fits Boosting regression trees (BRT), called also generalized boosting regression model (GBM). Boosting is the process of iteratively adding basis functions in a greedy fashion so that each additional basis function further reduces the selected loss function [see the help for gbm function in gbm package]'
)
#------------
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