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modEvA (version 3.5)

Model Evaluation and Analysis

Description

Analyses species distribution models and evaluates their performance. It includes functions for performing variation partitioning, calculating several measures of model discrimination and calibration, optimizing prediction thresholds based on a number of criteria, performing multivariate environmental similarity surface (MESS) analysis, and displaying various analytical plots.

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Version

Install

install.packages('modEvA')

Monthly Downloads

1,808

Version

3.5

License

GPL-3

Maintainer

A. Barbosa

Last Published

June 1st, 2022

Functions in modEvA (3.5)

HLfit

Hosmer-Lemeshow goodness of fit
MESS

Multivariate Environmental Similarity Surfaces based on a data frame
MillerCalib

Miller's calibration satistics for logistic regression models
OA

Overlap Analysis
AUC

Area Under the Curve
confusionLabel

Label predictions according to their confusion matrix category
evaluate

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

Boyce Index
getModEqn

Get model equation
RsqGLM

R-squared measures for GLMs
varPart

Variation partitioning
modEvAmethods

Methods implemented in modEvA functions
modEvA-package

Model Evaluation and Analysis
range01

Shrink or stretch a vector to make it range between 0 and 1
rotif.mods

Rotifer distribution models
applyThreshold

Apply a threshold to model predictions
getThreshold

Prediction threshold for a given criterion
arrangePlots

Arrange plots
getBins

Get bins of continuous values.
evenness

Evenness in a binary vector.
multModEv

Multiple model evaluation
optiPair

Optimize the classification threshold for a pair of related model evaluation measures.
standard01

Standardize to 0-1 (or vice-versa)
prevalence

Prevalence
predDensity

Plot the density of predicted values for presences and absences.
predPlot

Plot predicted values for presences and absences, classified according to a prediction threshold.
ptsrast2obspred

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

Threshold-based measures of model evaluation
plotGLM

Plot a generalized linear model
optiThresh

Optimize threshold for model evaluation.
Dsquared

Proportion of deviance explained by a GLM
inputMunch

Munch inputs into 'obs' and 'pred' vectors
mod2obspred

Extract observed and predicted values from a model object.