ecospat.cv.glm {ecospat}

GLM Cross Validation

Description

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

Usage

ecospat.cv.glm (glm.obj, K=10, cv.lim=10, jack.knife = FALSE, verbose = FALSE)

Arguments

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.

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.

Author(s)

Christophe Randin [e-mail] and Antoine Guisan [e-mail]

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

if(require("rms",quietly=TRUE)){
  data('ecospat.testData')

  # data for Soldanella alpina
  data.Solalp<- ecospat.testData[c("Soldanella_alpina","ddeg","mind","srad","slp","topo")] 

  # glm model for Soldanella alpina

  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)
}