| kuenm_feval | R Documentation | 
kuenm_feval evaluates final Maxent models in terms of statistical significance (partial ROC) and omission rates with a user-defined threshold (E).
kuenm_feval(path, occ.joint, occ.ind, replicates, out.eval, threshold = 5,
            rand.percent = 50, iterations = 500, parallel.proc = FALSE)
| path | (character) directory in which folders containing final models were created. | 
| occ.joint | (character) the csv file with training and testing occurrences combined, or the file containing occurrences used to create final models; columns must be: species, longitude, latitude. | 
| occ.ind | (character) the name of the csv file with independent
occurrences for model evaluation; these occurrences were not used when
creating final models; columns as in  | 
| replicates | (logical) whether or not final models were created performing replicates. | 
| out.eval | (character) name of the folder where evaluation results will be written. | 
| threshold | (numeric) the percentage of omission error allowed (E), default = 5. | 
| rand.percent | (numeric) the percentage of data to be used for the bootstrapping process when calculating partial ROCs; default = 50. | 
| iterations | (numeric) the number of times that the bootstrap is going to be repeated; default = 500. | 
| parallel.proc | (logical) if TRUE, pROC calculations will be performed
in parallel using the available cores of the computer. This will demand more
RAM and almost full use of the CPU; hence, its use is more recommended in
high-performance computers. Using this option will speed up the analyses
only if models are large RasterLayers or if  | 
This function is used after the creation of final models.
A list with two data.frame objects containing results from the evaluation process, and a folder, in the working directory, containing a csv file with the results from final model evaluation.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.