# Model Evaluation and Analysis

## Documentation for package ‘modEvA’ version 3.9.3

- DESCRIPTION file.
- Package NEWS.

## Help Pages

modEvA-package | Model Evaluation and Analysis
applyThreshold | Apply threshold(s) to model predictions
arrangePlots | Arrange plots
AUC | Area Under the Curve
Boyce | Boyce Index
confusionLabel | Label predictions according to their confusion matrix category
confusionMatrix | Confusion matrix
Dsquared | Explained deviance
evaluate | Evaluate a model based on the elements of a confusion matrix.
evenness | Evenness in a binary vector.
getBins | Get bins of continuous values.
getModEqn | Get model equation
getThreshold | Prediction threshold for a given criterion
HLfit | Hosmer-Lemeshow goodness of fit
inputMunch | Munch inputs into 'obs' and 'pred' vectors
lollipop | Lollipop chart
MESS | Multivariate Environmental Similarity Surfaces based on a data frame
MillerCalib | Miller's calibration satistics for logistic regression models
mod2obspred | Extract observed and predicted values from a model object.
modEvA | Model Evaluation and Analysis
modEvAmethods | Methods implemented in modEvA functions
multModEv | Multiple model evaluation
OA | Overlap Analysis
optiPair | Optimize the classification threshold for a pair of related model evaluation measures.
optiThresh | Optimize threshold for model evaluation.
plotGLM | Plot a generalized linear model
predDensity | Plot the density of predicted values for presences and absences.
predPlot | Plot predicted values for presences and absences, optionally classified according to a prediction threshold.
prevalence | Prevalence
ptsrast2obspred | Observed and predicted values from presence points and a raster map.
range01 | Shrink or stretch a vector to make it range between 0 and 1
RMSE | Root mean square error
rotif.mods | Rotifer distribution models
RsqGLM | R-squared measures for GLMs
standard01 | Standardize to 0-1 (or vice-versa)
threshMeasures | Threshold-based measures of model evaluation
varImp | Variable importance.
varPart | Variation partitioning