Running the analysis

Fit the models - Step 7/14

Now we check the result in the training area by projecting the models.
To save time we fist downscale the bioclimatic variables in the training area to 5Km:

> vars_training_5km<-stack(aggregate(vars_training, 5, progress="text"))

then project:

> myproj<-BIOMOD_Projection(modeling.output=mymodel,
+      new.env= vars_training_5km,
+      proj.name="current",
+      selected.models= "all",
+      do.stack=T)

-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Do Models Projections -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

    > Building clamping mask

    > Projecting krameri.1_PA1_RUN1_GLM ...
    > Projecting krameri.1_PA1_RUN1_GAM ...
    > Projecting krameri.1_PA1_RUN1_GBM ...
    > Projecting krameri.1_PA1_RUN1_MAXENT ...
    > Projecting krameri.1_PA1_RUN2_GLM ...
    > Projecting krameri.1_PA1_RUN2_GAM ...
    > Projecting krameri.1_PA1_RUN2_GBM ...
    > Projecting krameri.1_PA1_RUN2_MAXENT ...
    > Projecting krameri.1_PA1_RUN3_GLM ...
    > Projecting krameri.1_PA1_RUN3_GAM ...
    > Projecting krameri.1_PA1_RUN3_GBM ...
    > Projecting krameri.1_PA1_RUN3_MAXENT ...
-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Done -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=