ecospat (version 3.5.1)

ecospat.cv.me: Maxent Cross Validation

Description

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

Usage

ecospat.cv.me(data.cv.me, name.sp, names.pred, K=10, cv.lim=10, 
              jack.knife=FALSE, verbose=FALSE)

Value

Returns a dataframe with the observations (obs) and the corresponding predictions by cross-validation or jacknife.

Arguments

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.

Author

Christophe Randin and Antoine Guisan

Details

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.

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

# \donttest{
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

# path to maxent.jar file
path<- paste0(system.file(package="dismo"), "/java/maxent.jar")

if (file.exists(path) & 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)
  }
# }