[Biomod-commits] predictive accuracy issue

[Popko]

Dear colleagues,

Looking at my Evaluation.results I noticed that predictive accuracy of 
Calibration (called Cross.validation in output table) is lower than the 
predictive accuracy using Evaluation (called "indepdt.data" in output 
table). This is true for all 63 species I've analyzed, and independent of 
method (ROC, kappa, TSS).
Q: How is this possible?

This is the model I ran:
Models(GLM=T, TypeGLM="quad", Test="AIC", GBM=F, No.trees=2000, GAM=F, 
Spline=3, CTA=F, CV.tree=50, ANN=F, CV.ann=2, SRE=F, Perc025=T, Perc05=F, 
MDA=F, MARS=F, RF=F, NbRunEval=3, DataSplit=80, Yweights=NULL, Roc=T, 
Optimized.Threshold.Roc=T, Kappa=T, TSS=T, KeepPredIndependent=T, 
VarImport=5, NbRepPA=2, strategy="circles", coor=CoorXY, distance=2, 
nb.absences=1000)

Data consisted of presence/absence data with ca. 2500 cases per species. 
Some 70 environmental variables were entered in the model.

Eagerly awaiting your responses,

Popko Wiersma
SOVON Dutch Centre for Field Ornithology