skill_ROC: ROC Curve

In MazamaScience/PWFSLSmoke: Utilities for Working with Air Quality Monitoring Data

ROC Curve

Description

This function calculates an ROC dataframe of TPR, FPR, and Cost for a range of thresholds as well as the area under the ROC curve.

Usage

skill_ROC(predicted, observed, t1Range = NULL, t2 = NULL, n = 101)

Arguments

predicted	vector of predicted values (or a ws_monitor object with a single location)
observed	vector of observed values (or a ws_monitor object with a single location)
t1Range	lo and high values used to generate test thresholds for classifying predicted data
t2	used to classify observed data
n	number of test thresholds in ROC curve

Value

List containing an roc matrix and the auc area under the ROC curve.

References

Receiver Operating Characteristic

See Also

skill_confusionMatrix

skill_ROCPlot

Examples

## Not run: 
# Fail gracefully if any resources are not available
try({

# Napa Fires -- October, 2017
ca <- airnow_loadAnnual(2017) %>%
  monitor_subset(tlim = c(20171001,20171101), stateCodes = 'CA')
Vallejo <- monitor_subset(ca, monitorIDs = '060950004_01')
Napa <- monitor_subset(ca, monitorIDs = '060550003_01')
t2 <- AQI$breaks_24[4] # 'Unhealthy'
rocList <- skill_ROC(Vallejo, Napa, t1Range = c(0,100), t2 = t2)
roc <- rocList$roc
auc <- rocList$auc
plot(roc$TPR ~ roc$FPR, type = 'S')
title(paste0('Area Under Curve = ', format(auc,digits = 3)))

}, silent = FALSE)

## End(Not run)

MazamaScience/PWFSLSmoke documentation built on July 3, 2023, 11:03 a.m.