ecospat.cv.rf: RandomForest Cross Validation

In ecospat: Spatial Ecology Miscellaneous Methods

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 [e-mail] and Antoine Guisan [e-mail]

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 documentation built on Oct. 18, 2023, 1:19 a.m.