modEvA (version 3.33)

modEvA-package: Model Evaluation and Analysis

Description

The modEvA package can analyse species distribution models and evaluate their performance. It includes functions for performing variation partitioning; calculating several measures of model discrimination, classification, explanatory power, and calibration; optimizing prediction thresholds based on a number of criteria; performing multivariate environmental similarity surface (MESS) analysis; and displaying various analytical plots.

Author

Barbosa A.M., Brown J.A., Jimenez-Valverde A., Real R.

A. Marcia Barbosa <[e-mail]>

Details

Package:	modEvA
Type:	Package
Version:	3.33
Date:	2025-03-19
License:	GPL-3

References

Barbosa A.M., Real R., Munoz A.R. & Brown J.A. (2013) New measures for assessing model equilibrium and prediction mismatch in species distribution models. Diversity and Distributions 19: 1333-1338 (DOI: 10.1111/ddi.12100)

See Also

PresenceAbsence, ROCR, verification, Metrics

Examples

# load sample models:
data(rotif.mods)

# choose a particular model to play with:
mod <- rotif.mods$models[[1]]

# plot this model:
plotGLM(model = mod)

# compute the Root Mean Square Error of the model:
RMSE(model = mod)

# extract variable importance from the model:
varImp(model = mod)

# calculate the area under the ROC curve for the model:
AUC(model = mod)

# calculate some threshold-based measures for this model:
threshMeasures(model = mod, thresh = 0.5)
threshMeasures(model = mod, thresh = "preval")

# calculate optimal thresholds based on several criteria:
optiThresh(model = mod, measures = c("CCR", "Sensitivity", "kappa", "TSS"),
ylim = c(0, 1), pch = 20, cex = 0.5)

# calculate the optimal threshold balancing two evaluation measures:
optiPair(model = mod, measures = c("Sensitivity", "Specificity"))

# calculate the Boyce index, explained deviance, Hosmer-Lemeshow goodness-of-fit,
# Miller's calibration stats, and (pseudo) R-squared values for the model:
Boyce(model = mod)
Dsquared(model = mod)
HLfit(model = mod, bin.method = "quantiles")
MillerCalib(model = mod)
RsqGLM(model = mod)

# calculate a bunch of evaluation measures for a set of models:
multModEv(models = rotif.mods$models[1:4], thresh = "preval",
bin.method = "quantiles")