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modEvA (version 3.9.3)

Model Evaluation and Analysis

Description

Analyses species distribution models and evaluates their performance. It includes functions for variation partitioning, extracting variable importance, computing several metrics of model discrimination and calibration performance, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots. Initially described in Barbosa et al. (2013) .

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Version

Install

install.packages('modEvA')

Monthly Downloads

1,428

Version

3.9.3

License

GPL-3

Maintainer

A. Barbosa

Last Published

April 14th, 2023

Functions in modEvA (3.9.3)

RsqGLM

R-squared measures for GLMs
MillerCalib

Miller's calibration satistics for logistic regression models
applyThreshold

Apply threshold(s) to model predictions
Boyce

Boyce Index
Dsquared

Explained deviance
OA

Overlap Analysis
RMSE

Root mean square error
AUC

Area Under the Curve
MESS

Multivariate Environmental Similarity Surfaces based on a data frame
HLfit

Hosmer-Lemeshow goodness of fit
evaluate

Evaluate a model based on the elements of a confusion matrix.
getThreshold

Prediction threshold for a given criterion
arrangePlots

Arrange plots
evenness

Evenness in a binary vector.
confusionMatrix

Confusion matrix
inputMunch

Munch inputs into 'obs' and 'pred' vectors
getModEqn

Get model equation
lollipop

Lollipop chart
confusionLabel

Label predictions according to their confusion matrix category
getBins

Get bins of continuous values.
plotGLM

Plot a generalized linear model
predDensity

Plot the density of predicted values for presences and absences.
modEvA-package

Model Evaluation and Analysis
predPlot

Plot predicted values for presences and absences, optionally classified according to a prediction threshold.
mod2obspred

Extract observed and predicted values from a model object.
optiThresh

Optimize threshold for model evaluation.
modEvAmethods

Methods implemented in modEvA functions
multModEv

Multiple model evaluation
optiPair

Optimize the classification threshold for a pair of related model evaluation measures.
varImp

Variable importance.
range01

Shrink or stretch a vector to make it range between 0 and 1
threshMeasures

Threshold-based measures of model evaluation
prevalence

Prevalence
varPart

Variation partitioning
ptsrast2obspred

Observed and predicted values from presence points and a raster map.
standard01

Standardize to 0-1 (or vice-versa)
rotif.mods

Rotifer distribution models