miceCM {micer}R Documentation

miceCM

Description

Calculate map image classification efficacy (MICE) and other metrics using confusion matrix

Usage

miceCM(
  cm,
  mappings = levels(as.factor(row.names(cm))),
  multiclass = TRUE,
  positiveIndex = 1
)

Arguments

cm

confusion matrix as table object where rows define predictions and columns define reference labels.

mappings

names of classes (if not provided, factor levels are used).

multiclass

TRUE or FALSE. If TRUE, treats classification as multiclass. If FALSE, treats classification as binary. Default is TRUE.

positiveIndex

index for positive case for binary classification. Ignored for multiclass classification. Default is 1 or first factor level.

Details

For multiclass classification, returns a list object with the following items: $Mappings = class names; $confusionMatrix = confusion matrix where columns represent the reference data and rows represent the classification result; $referenceCounts = count of samples in each reference class; $predictionCounts = count of predictions in each class; $overallAccuracy = overall accuracy; $MICE = map image classification efficacy; $usersAccuracies = class-level user's accuracies (1 - commission error); $CTBICEs = classification-total-based image classification efficacies (adjusted user's accuracies); $producersAccuracies = class-level producer's accuracies (1 - omission error); $RTBICEs = reference-total-based image classification efficacies (adjusted producer's accuracies); $F1Scores = class-level harmonic mean of user's and producer's accuracies; $F1Efficacies = F1-score efficacies; $macroPA = class-aggregated, macro-averaged producer's accuracy; $macroRTBICE = class-aggregated, macro-averaged reference-total-based image classification efficacy; $macroUA = class-aggregated, macro-averaged user's accuracy; $macroCTBICE = class-aggregated, macro-averaged classification-total-based image classification efficacy; $macroF1 = class-aggregated, macro-averaged F1-score; $macroF1Efficacy = class-aggregated, macro-averaged F1 efficacy;

For binary classification, returns a list object with the following items: $Mappings = class names; $confusionMatrix = confusion matrix where columns represent the reference data and rows represent the classification result; $referenceCounts = count of samples in each reference class; $predictionCounts = count of predictions in each class; $postiveCase = name or mapping for the positive case; $overallAccuracy = overall accuracy; $MICE = map image classification efficacy; $Precision = precision (1 - commission error relative to positive case); $precisionEfficacy = precision efficacy; $NPV = negative predictive value (1 - commission error relative to negative case); $npvEfficacy = negative predictive value efficacy; $Recall = recall (1 - omission error relative to positive case); $recallEfficacy = recall efficacy; $specificity = specificity (1 - omission error relative to negative case); $specificityEfficacy = specificity efficacy; $f1Score = harmonic mean of precision and recall; $f1Efficacy = F1-score efficacy;

Value

multiclass or binary assessment metrics in a list object. See details for description of generated metrics.

Examples

#Multiclass example
data(mcData)
cmMC <- table(mcData$pred, mcData$ref)
miceCM(cmMC,
mappings=c("Barren", "Forest", "Impervious", "Low Vegetation", "Mixed Dev", "Water"),
multiclass=TRUE)

#Binary example
data(biData)
cmB <- table(biData$pred, biData$ref)
miceMCResult <- miceCM(cmB,
mappings=c("Mined", "Not Mined"),
multiclass=FALSE,
positiveIndex=1)
print(miceMCResult)

[Package micer version 0.2.0 Index]