micer (version 0.2.1)

miceCI: miceCI

Description

Calculate confidence intervals (CIs) for MICE and associated metrics using bootstrap sampling and the percentile method.

Usage

miceCI(
  reps = 200,
  lowPercentile,
  highPercentile,
  reference,
  prediction,
  mappings = levels(as.factor(reference)),
  multiclass = TRUE,
  positiveIndex = 1
)

Value

dataframe object of metric name and estimated mean value, median value, and lower and upper CIs.

Arguments

reps

number of bootstrap replicates to use. Default is 200.

lowPercentile

lower percentile for confidence interval. Default is 0.025 for a 95% CI.

highPercentile

upper percentile for confidence interval. Default is 0.975 for a 95% CI.

reference

column of reference labels as factor data type.

prediction

column of predicted labels as factor data type.

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

Confidence intervals are estimated for overall accuracy, MICE, and all class-aggregated, macro-averaged metrics produced by mice() or miceCM(). Returns metric name, mean metric value, median metric value, lower confidence interval bounds (low.ci), and upper confidence interval bounds (upper.ci) as a dataframe object.

Examples

#Multiclass example
data(mcData)
ciResultsMC <- miceCI(rep=100,
mcData$ref,
mcData$pred,
lowPercentile=0.025,
highPercentile=0.975,
mappings=c("Barren", "Forest", "Impervious", "Low Vegetation", "Mixed Dev", "Water"),
multiclass=TRUE)

print(ciResultsMC)

#Binary example
data(biData)
ciResultsBi <- miceCI(rep=100,
biData$ref,
biData$pred,
lowPercentile=0.025,
highPercentile=0.975,
mappings = c("Mined", "Not Mined"),
multiclass=FALSE,
positiveIndex=1)

print(ciResultsBi)