ENMevaluation-class {ENMeval}

ENMevaluation class

Description

An S4 class that contains the ENMevaluate results.

Usage

## S4 method for signature '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

Author(s)

Jamie M. Kass, [e-mail], Bob Muscarella, [e-mail]

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. doi: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. doi: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. doi: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. doi: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. doi:10.1111/jbi.12227

Omission rates

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

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. doi:10.1016/j.ecolmodel.2006.05.017