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Train a model using one of the following methods: Artificial Neural Networks, Boosted Regression Trees, Maxent, Maxnet or Random Forest.
train(method, data, folds = NULL, verbose = TRUE, ...)character or character vector. Method used to train the model, possible values are "ANN", "BRT", "Maxent", "Maxnet" or "RF", see details.
list. Output of the function randomFolds or folds
object created with other packages, see details, default is NULL.
logical, if TRUE shows a progress bar during cross
validation, default is TRUE.
Arguments passed to the relative method, see details.
An '>SDMmodel or '>SDMmodelCV
or a list of model objects.
object.
For the ANN method possible arguments are (for more details see
nnet):
size: integer. Number of the units in the hidden layer.
decay numeric. Weight decay, default is 0.
rang numeric. Initial random weights, default is 0.7.
maxit integer. Maximum number of iterations, default is 100.
For the BRT method possible arguments are (for more details see
gbm):
distribution: character. Name of the distribution to use, default is "bernoulli".
n.trees: integer. Maximum number of tree to grow, default is 100.
interaction.depth: integer. Maximum depth of each tree, default is 1.
shrinkage: numeric. The shrinkage parameter, default is 0.1.
bag.fraction: numeric. Random fraction of data used in the tree expansion, default is 0.5.
For the RF method the model is trained as classification. Possible
arguments are (for more details see
randomForest):
mtry: integer. Number of variable randomly sampled at each split,
default is floor(sqrt(number of variables)).
ntree: integer. Number of tree to grow, default is 500.
nodesize: integer. Minimum size of terminal nodes.
Maxent models are trained using the arguments
"removeduplicates=false" and "addsamplestobackground=false".
Use the function thinData to remove duplicates and the function
addSamplesToBg to add presence locations to background
locations. For the Maxent method, possible arguments are:
reg: numeric. The value of the regularization multiplier, default is 1.
fc: character. The value of the feature classes, possible values are combinations of "l", "q", "p", "h" and "t", default is "lqph".
iter: numeric. Number of iterations used by the MaxEnt algorithm, default is 500.
For the Maxnet method, possible arguments are (for more details see
maxnet):
reg: numeric. The value of the regularization intensity, default is 1.
fc: character. The value of the feature classes, possible values are combinations of "l", "q", "p", "h" and "t", default is "lqph".
The folds argument accepts also objects created with other packages: ENMeval or blockCV. In this case the function converts internally the folds into a format valid for SDMtune.
When multiple methods are given as method argument, the function
returns a named list of model object, with the name corresponding to the
used method, see examples.
Venables, W. N. & Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth Edition. Springer, New York. ISBN 0-387-95457-0.
Brandon Greenwell, Bradley Boehmke, Jay Cunningham and GBM Developers (2019). gbm: Generalized Boosted Regression Models. https://CRAN.R-project.org/package=gbm.
A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3), 18--22.
Hijmans, Robert J., Steven Phillips, John Leathwick, and Jane Elith. 2017. dismo: Species Distribution Modeling. https://cran.r-project.org/package=dismo.
Steven Phillips (2017). maxnet: Fitting 'Maxent' Species Distribution Models with 'glmnet'. https://CRAN.R-project.org/package=maxnet.
Muscarella, R., Galante, P.J., Soley-Guardia, M., Boria, R.A., Kass, J., Uriarte, M. and R.P. Anderson (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for ecological niche models. Methods in Ecology and Evolution.
Roozbeh Valavi, Jane Elith, Jos<U+00E9> Lahoz-Monfort and Gurutzeta Guillera-Arroita (2018). blockCV: Spatial and environmental blocking for k-fold cross-validation. https://github.com/rvalavi/blockCV.