ecospat.cv.gbm {ecospat}

GBM Cross Validation

Description

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

Usage

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

Arguments

gbm.obj

A calibrated GBM object with a binomial error distribution. Attention: users have to tune model input parameters according to their study!

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 GBM 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

library(gbm)
data('ecospat.testData')

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

# gbm model for Soldanella alpina
gbm.Solalp <- gbm::gbm(Soldanella_alpina ~ ., data = data.Solalp,
                  distribution = "bernoulli", cv.folds = 10, n.cores=2)

# cross-validated predictions
gbm.pred <- ecospat.cv.gbm (gbm.obj= gbm.Solalp,data.Solalp, 
                            K=10, cv.lim=10, jack.knife=FALSE)