ecospat.cv.glm {ecospat} | R Documentation |
K-fold and leave-one-out cross validation for GLM.
ecospat.cv.glm (glm.obj, K=10, cv.lim=10, jack.knife = FALSE, verbose = FALSE)
glm.obj |
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. |
verbose |
Boolean indicating whether to print progress output during calculation. Default is FALSE. |
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.
Returns a dataframe with the observations (obs) and the corresponding predictions by cross-validation or jacknife.
Christophe Randin christophe.randin@unibas.ch and Antoine Guisan antoine.guisan@unil.ch
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.
data('ecospat.testData')
# data for Soldanella alpina
data.Solalp<- ecospat.testData[c("Soldanella_alpina","ddeg","mind","srad","slp","topo")]
# gbm model for Soldanella alpina
library(rms)
glm.Solalp <- glm(Soldanella_alpina ~ pol(ddeg,2) + pol(mind,2) + pol(srad,2) + pol(slp,2)
+ pol(topo,2), data = data.Solalp, family = binomial)
# cross-validated predictions
glm.pred <- ecospat.cv.glm (glm.obj=glm.Solalp , K=10, cv.lim=10, jack.knife=FALSE)