ENMevaluation-class | R Documentation |

An S4 class that contains the ENMevaluate results.

## S4 method for signature 'ENMevaluation' show(object)

`object` |
ENMevaluation object |

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)

`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

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

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

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