Maxent Cross Validation
K-fold and leave-one-out cross validation for Maxent.
ecospat.cv.me(data.cv.me, name.sp, names.pred, K=10, cv.lim=10,
jack.knife=FALSE, verbose=FALSE)data.cv.me |
A dataframe object containing the calibration data set of a Maxent object to validate with the same names for response and predictor variables. |
name.sp |
Name of the species / response variable. |
names.pred |
Names of the predicting 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. |
This function takes a calibrated Maxent 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")]
# maxent modelling and cross-validated predictions
# copy maxent.jar file in the right folder
path.from<-system.file("extdata", "maxent.txt", package="ecospat")
path.to <- paste0(system.file(package="dismo"), "/java/maxent.txt")
path.to.renamed <- paste0(system.file(package="dismo"), "/java/maxent.jar")
file.copy(path.from,path.to,overwrite = TRUE)
file.rename(path.to, path.to.renamed)
if (file.exists(path.to.renamed) & require(rJava)) {
me.pred <- ecospat.cv.me(data.Solalp, names(data.Solalp)[1],
names(data.Solalp)[-1], K = 10, cv.lim = 10, jack.knife = FALSE)
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