ecospat.cv.gbm

0th

Percentile

GBM Cross Validation

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)
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.

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.

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.

Aliases
  • ecospat.cv.gbm
Examples
# NOT RUN {
ecospat.cv.example() #generates data
gbm <- ecospat.cv.gbm (gbm.obj= get("gbm.Achillea_atrata", envir=ecospat.env), 
ecospat.testData, K=10, cv.lim=10, jack.knife=FALSE)
# }
Documentation reproduced from package ecospat, version 3.0, License: GPL

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