Any calibrated GLM object with a binomial error distribution.
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 GLM 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.
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
## Not run:
glm <- ecospat.cv.glm (glm.obj = get ("glm.Agrostis_capillaris", envir=ecospat.env),
K=10, cv.lim=10, jack.knife=FALSE)
## End(Not run)