ENMevaluation class — ENMevaluation-class • ENMeval

An S4 class that contains the ENMevaluate results.

Usage

# S4 method for ENMevaluation
show(object)

Arguments

object: ENMevaluation object

Details

The following are brief descriptions of the columns in the results table, which prints when accessing `e@results` or `results(e)` if `e` is the ENMevaluation object. Those columns that represent evaluations of validation data (__.val.__) end in either "avg" (average of the metric across the models trained on withheld data during cross-validation) or "sd" (standard deviation of the metric across these models).
* fc = feature class
* rm = regularization multiplier
* tune.args = combination of arguments that define the complexity settings used for tuning (i.e., fc and rm for Maxent)
* auc.train = AUC calculated on the full dataset
* cbi.train = Continuous Boyce Index calculated on the full dataset
* auc.val = average/sd AUC calculated on the validation datasets (the data withheld during cross-validation)
* auc.diff = average/sd difference between auc.train and auc.val
* or.mtp = average/sd omission rate with threshold as the minimum suitability value across occurrence records
* or.10p = average/sd omission rate with threshold as the minimum suitability value across occurrence records after removing the lowest 10 cbi.val = average/sd Continuous Boyce Index calculated on the validation datasets (the data withheld during cross-validation)
* AICc = AIC corrected for small sample sizes
* delta.AICc = highest AICc value across all models minus this model's AICc value, where lower values mean higher performance and 0 is the highest performing model
* w.AIC = AIC weights, calculated by exp( -0.5 * delta.AIC), where higher values mean higher performance
* ncoef = number of non-zero beta values (model coefficients)

Slots

algorithm: character: algorithm used
tune.settings: data frame: settings that were tuned
partition.method: character: partition method used
partition.settings: list: partition settings used (i.e., value of *k* or aggregation factor)
other.settings: list: other modeling settings used (i.e., decisions about clamping, AUC diff calculation)
doClamp: logical: whether or not clamping was used
clamp.directions: list: the clamping directions specified
results: data frame: evaluation summary statistics
results.partitions: data frame: evaluation k-fold statistics
models: list: model objects
variable.importance: list: variable importance data frames (when available)
predictions: RasterStack: model predictions
taxon.name: character: the name of the focal taxon (optional)
occs: data frame: occurrence coordinates and predictor variable values used for model training
occs.testing: data frame: when provided, the coordinates of the fully-withheld testing records
occs.grp: vector: partition groups for occurrence points
bg: data frame: background coordinates and predictor variable values used for model training
bg.grp: vector: partition groups for background points
overlap: list: matrices of pairwise niche overlap statistics
rmm: list: the rangeModelMetadata objects for each model

References

For references on performance metrics, see the following:

In general for ENMeval:

Muscarella, R., Galante, P. J., Soley-Guardia, M., Boria, R. A., Kass, J. M., Uriarte, M., & Anderson, R. P. (2014). ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5: 1198-1205. https://doi.org/10.1111/2041-210X.12261

AUC

Fielding, A. H., & Bell, J. F. (1997). A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation, 24: 38-49. https://doi.org/10.1017/S0376892997000088

Jiménez‐Valverde, A. (2012). Insights into the area under the receiver operating characteristic curve (AUC) as a discrimination measure in species distribution modelling. Global Ecology and Biogeography, 21: 498-507. https://doi.org/10.1111/j.1466-8238.2011.00683.x

AUC diff

Warren, D. L., Glor, R. E., Turelli, M. & Funk, D. (2008) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution, 62: 2868-2883. https://doi.org/10.1111/j.1558-5646.2008.00482.x

Radosavljevic, A., & Anderson, R. P. (2014). Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41(4), 629-643. https://doi.org/10.1111/jbi.12227

Omission rates

Continuous Boyce Index

Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C., & Guisan, A. (2006). Evaluating the ability of habitat suitability models to predict species presences. Ecological Modelling, 199: 142-152. https://doi.org/10.1016/j.ecolmodel.2006.05.017

Author

Jamie M. Kass, jamie.m.kass@gmail.com, Bob Muscarella, bob.muscarella@gmail.com