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ecospat.cv.rf

RandomForest Cross Validation


Description

K-fold and leave-one-out cross validation for randomForest.

Usage

ecospat.cv.rf (rf.obj, data.cv, K=10, cv.lim=10, jack.knife=FALSE, verbose = FALSE)

Arguments

rf.obj

Any calibrated randomForest object with a binomial error distribution.

data.cv

A dataframe object containing the calibration data set with the same names for response and predictor variables.

K

Number of folds. 10 is recommended; 5 for small data sets.

cv.lim

Minimum number of presences required to perform the K-fold cross-validation.

jack.knife

If TRUE, then the leave-one-out / jacknife cross-validation is performed instead of the 10-fold cross-validation.

verbose

Boolean indicating whether to print progress output during calculation. Default is FALSE.

Details

This function takes a calibrated randomForest object with a binomial error distribution and returns predictions from a stratified 10-fold cross-validation or a leave-one-out / jack-knived cross-validation. Stratified means that the original prevalence of the presences and absences in the full dataset is conserved in each fold.

Value

Returns a dataframe with the observations (obs) and the corresponding predictions by cross-validation or jacknife.

Author(s)

Christophe Randin christophe.randin@wsl.ch and Antoine Guisan antoine.guisan@unil.ch

References

Randin, C.F., T. Dirnbock, S. Dullinger, N.E. Zimmermann, M. Zappa and A. Guisan. 2006. Are niche-based species distribution models transferable in space? Journal of Biogeography, 33, 1689-1703.

Pearman, P.B., C.F. Randin, O. Broennimann, P. Vittoz, W.O. van der Knaap, R. Engler, G. Le Lay, N.E. Zimmermann and A. Guisan. 2008. Prediction of plant species distributions across six millennia. Ecology Letters, 11, 357-369.

Examples

data('ecospat.testData')

# data for Soldanella alpina
data.Solalp<- ecospat.testData[c("Soldanella_alpina","ddeg","mind","srad","slp","topo")] 

library(randomForest)
rf.Solalp <- randomForest(x = data.Solalp[,-1], y = as.factor(data.Solalp[,1]))
rf.pred <- ecospat.cv.rf(rf.Solalp, data.Solalp, K = 10, cv.lim = 10, 
                         jack.knife = FALSE, verbose = FALSE)

ecospat

Spatial Ecology Miscellaneous Methods

v3.2
GPL
Authors
Olivier Broennimann [cre, aut, ctb], Valeria Di Cola [aut, ctb], Blaise Petitpierre [ctb], Frank Breiner [ctb], Daniel Scherrer [ctb], Manuela D`Amen [ctb], Christophe Randin [ctb], Robin Engler [ctb], Wim Hordijk [ctb], Heidi Mod [ctb], Julien Pottier [ctb], Mirko Di Febbraro [ctb], Loic Pellissier [ctb], Dorothea Pio [ctb], Ruben Garcia Mateo [ctb], Anne Dubuis [ctb], Luigi Maiorano [ctb], Achilleas Psomas [ctb], Charlotte Ndiribe [ctb], Nicolas Salamin [ctb], Niklaus Zimmermann [ctb], Antoine Guisan [aut]
Initial release
2021-02-17

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