R/testroc.b.R
In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research
# This file is a generated template, your changes will not be overwritten
TestROCClass <- if (requireNamespace('jmvcore'))
R6::R6Class(
"TestROCClass",
inherit = TestROCBase,
private = list(
.init = function() {
# if (is.null(self$options$classVar) ||
# is.null(self$options$dependentVars)) {
# self$results$resultsTable$setVisible(visible = FALSE)
# }
},
.run = function() {
if (is.null(self$options$classVar) ||
is.null(self$options$dependentVars)) {
self$results$instructions$setContent(
"<html>
<head>
</head>
<body>
<div class='instructions'>
<p><b>This analysis is still in development: The format of results may change in future releases. Please report any errors or requests <a href='https://github.com/lucasjfriesen/jamoviPsychoPDA/issues' target = '_blank'>here</a></b></p>
<p>Welcome to PsychoPDA's Test ROC analysis To get started:</p>
<ol>
<li>Place the responses in the 'Dependent Variable' slot<br /><br /></li>
<li>Place the classification in the 'Class Variable' slot<br /><br /></li>
<li>[<em>Optional</em>] Place a grouping variable in the 'Grouping Variable' slot<br /><br /></li>
</ol>
<p>If you encounter any errors, or have questions, please see the <a href='https://lucasjfriesen.github.io/jamoviPsychoPDA_docs/measureDiagnostics_testROC_gettingStarted.html' target = '_blank'>documentation.</a></p>
<p>If this software is used in conducting published research, please do provide a citation using the information at the bottom of the analysis.</p>
</div>
</body>
</html>"
)
return()
} else {
self$results$instructions$setVisible(visible = FALSE)
procedureNotes <- paste0(
"<html>
<body>
<p>Procedure Notes</p>
<hr>",
"<p> The TestROC optimal cutpoint analysis has been completed using the following specifications: ",
"<p> </p>",
"<p> Measure Variable(s): ",
paste(unlist(self$options$dependentVars), collapse = ", "),
"</p>",
"<p> Class Variable: ",
self$options$classVar,
"</p>"
)
if (self$options$positiveClass == "") {
procedureNotes <- paste0(
procedureNotes,
"<p> Positive Class: ", self$data[,self$options$classVar][1], "</p>")
} else {
procedureNotes <- paste0(procedureNotes,
"<p> Positive Class: ",
self$options$positiveClass,
"</p>")
}
# Was there subgrouping?
if (!is.null(self$options$subGroup)) {
procedureNotes <- paste0(procedureNotes,
"<p> Sub-Group Variable: ",
self$options$subGroup,
"</p>")
}
procedureNotes <- paste0(
procedureNotes,
"<p> </p>",
"<p> Method: ",
self$options$method,
"</p>",
"<p> All Observed Cutpoints: ",
self$options$allObserved,
"</p>",
"<p> Metric: ",
self$options$metric,
"</p>",
"<p> Direction (relative to cutpoint): ",
self$options$direction,
"</p>",
"<p> Tie Breakers: ",
self$options$break_ties,
"</p>",
"<p> Metric Tolerance: ",
self$options$tol_metric,
"</p>",
"<p> </p>"
)
# If bootstrapping happened
if (self$options$boot_runs != 0) {
procedureNotes <- paste0(procedureNotes,
"<p> Bootstrap Runs: ",
self$options$boot_runs,
"</p>")
}
# Close the notes
procedureNotes <- paste0(
procedureNotes,
"<hr />
<p>For more information on how calculations are performed and interpretting results, please see the <a href='https://lucasjfriesen.github.io/jamoviPsychoPDA_docs/measureDiagnostics_testROC_gettingStarted.html' target = '_blank'>documentation</a></p>
</body>
</html>"
)
self$results$procedureNotes$setContent(procedureNotes)
}
# Var handling ----
data = self$data
if (self$options$method == "oc_manual") {
method = "cutpointr::oc_manual"
if (self$options$specifyCutScore == "") {
stop("Please specify a cut score for using this method.")
} else {
score = as.numeric(self$options$specifyCutScore)
}
} else {
method = paste0("cutpointr::", self$options$method)
score = NULL
}
if (method %in% c(
"cutpointr::maximize_metric",
"cutpointr::minimize_metric",
"cutpointr::maximize_loess_metric",
"cutpointr::minimize_loess_metric",
"cutpointr::maximize_spline_metric",
"cutpointr::minimize_spline_metric"
)) {
tol_metric = self$options$tol_metric
} else {
tol_metric = NULL
}
method = eval(parse(text = method))
metric = paste0("cutpointr::", self$options$metric)
metric = eval(parse(text = metric))
direction = self$options$direction
boot_runs = self$options$boot_runs
# use_midpoints = self$options$use_midpoint
break_ties = self$options$break_ties
break_ties = eval(parse(text = break_ties))
boot_runs = self$options$boot_runs
plotDataList <- data.frame(
var = list(),
cutpoint = list(),
sensitivity = list(),
specificity = list(),
ppv = list(),
npv = list(),
AUC = list(),
youden = list(),
stringsAsFactors = FALSE)
# Data ----
vars <- self$options$dependentVars
if (!is.null(self$options$subGroup)) {
subGroup = data[, self$options$subGroup]
classVar = data[, self$options$classVar]
uniqueGroups <- unique(subGroup)
vars <-
apply(expand.grid(vars, uniqueGroups), 1, paste, collapse = "_")
} else {
subGroup = NULL
}
aucList = list()
for (var in vars) {
if (!var %in% self$results$resultsTable$itemKeys) {
self$results$sensSpecTable$addItem(key = var)
self$results$resultsTable$addItem(key = var)
if (self$options$combinePlots == FALSE) {
self$results$plotROC$addItem(key = var)
}
}
if (is.null(subGroup)) {
dependentVar = as.numeric(data[, var])
classVar = data[, self$options$classVar]
} else {
dependentVar = as.numeric(data[subGroup == strsplit(var, split = "_")[[1]][2],
names(data) == strsplit(var, split = "_")[[1]][1]])
classVar = data[subGroup == strsplit(var, split = "_")[[1]][2],
self$options$classVar]
}
if (self$options$positiveClass == ""){
# self$results$debug$setContent(classVar[1])
pos_class <- classVar[1]
} else {
pos_class <- self$options$positiveClass
}
# Caclulations ----
results = cutpointr::cutpointr(
x = dependentVar,
class = classVar,
subgroup = NULL,
method = method,
cutpoint = score,
metric = metric,
direction = direction,
pos_class = pos_class,
# use_midpoints = use_midpoints,
tol_metric = tol_metric,
boot_runs = boot_runs,
break_ties = break_ties,
na.rm = TRUE
)
# self$results$debug$setContent(results)
if (!self$options$allObserved) {
resultsToDisplay <- sort(unlist(results$optimal_cutpoint))
} else {
resultsToDisplay <- sort(unlist(unique(dependentVar)))
}
# Confusion matrix ----
confusionMatrix <-
confusionMatrixForTable <- results$roc_curve[[1]]
if (!self$options$allObserved) {
confusionMatrixForTable = confusionMatrixForTable[confusionMatrixForTable$x.sorted %in% resultsToDisplay,]
}
if (self$options$sensSpecTable) {
self$results$sensSpecTable$setVisible(TRUE)
sensSpecRes <-
print.sensSpecTable(
Title = paste0(
"Scale: ",
var,
" | Score: ",
confusionMatrixForTable$x.sorted
),
TP = confusionMatrixForTable$tp,
FP = confusionMatrixForTable$fp,
TN = confusionMatrixForTable$tn,
FN = confusionMatrixForTable$fn
)
sensTable <- self$results$sensSpecTable$get(key = var)
sensTable$setContent(sensSpecRes)
sensTable$setVisible(TRUE)
}
# Results columns ----
sensList <-
(
cutpointr::sensitivity(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
) * 100
)
specList <-
(
cutpointr::specificity(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
) * 100
)
ppvList <- (
cutpointr::ppv(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
) * 100
)
npvList <- (
cutpointr::npv(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
) * 100
)
youdenList <-
cutpointr::youden(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
)
metricList <-
metric(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
)
resultsToReturn <- data.frame(
#scaleName = rep(var, times = length(sensList)),
cutpoint = as.character(confusionMatrix$x.sorted),
sensitivity = formatter(sensList),
specificity = formatter(specList),
ppv = formatter(ppvList),
npv = formatter(npvList),
AUC = results$AUC,
youden = youdenList,
metricValue = unname(metricList),
stringsAsFactors = FALSE # FUCK
)
aucList[[var]] = results$AUC
# State Savers ----
# Results table ----
resultState <- self$results$resultsTable$state
if (!is.null(resultState)) {
# ... populate the table from the state
} else {
# ... create the table and the state
table <- self$results$resultsTable$get(key = var)
for (row in resultsToDisplay) {
table$setTitle(paste0("Scale: ", var))
table$addRow(rowKey = row, value = resultsToReturn[resultsToReturn$cutpoint == row,])
}
self$results$resultsTable$setState(resultState)
}
# Plotting Data ----
if (self$options$plotROC == TRUE &
self$options$combinePlots == FALSE) {
image <- self$results$plotROC$get(key = var)
image$setTitle(paste0("ROC Curve: ", var))
image$setState(
data.frame(
var = var,
cutpoint = confusionMatrix$x.sorted,
sensitivity = sensList,
specificity = specList,
ppv = ppvList,
npv = npvList,
AUC = results$AUC,
youden = youdenList,
stringsAsFactors = FALSE # FUCK
)
)
} else {
plotDataList <- rbind(
plotDataList,
data.frame(
var = var,
cutpoint = confusionMatrix$x.sorted,
sensitivity = sensList,
specificity = specList,
ppv = ppvList,
npv = npvList,
AUC = results$AUC,
youden = youdenList,
stringsAsFactors = FALSE
)
)}}
if (self$options$plotROC == TRUE &
self$options$combinePlots == TRUE){
self$results$plotROC$addItem(key = 1)
image <- self$results$plotROC$get(key = 1)
image$setTitle(paste0("ROC Curve: Combined"))
image$setState(plotDataList)
#
}
# DeLong Test ----
# self$results$debug$setContent(classVar)
if (self$options$delongTest == TRUE) {
if (length(self$options$dependentVars) < 2){
stop("Please specify at least two dependent variables to use DeLong's test.")
}
if (!is.null(self$options$subGroup)) {
stop(
"DeLong's test does not currently support the grouping variable. If you would like to contribute/provide guidance, please use the contact information provided in the documentation."
)
} else {
delongResults <- deLong.test(
data = data.frame(lapply(data[, vars], as.numeric)),
classVar = as.character(classVar),
ref = NULL,
pos_class = pos_class,
conf.level = 0.95
)
}
self$results$delongTest$setVisible(visible = TRUE)
self$results$delongTest$setContent(paste0(capture.output(print.DeLong(
delongResults
))))
}
},
.plotROC = function(image, ggtheme, theme, ...) {
plotData <- data.frame(image$state)
if (self$options$combinePlots == TRUE) {
plot <-
ggplot2::ggplot(plotData,
ggplot2::aes(
x = 1 - specificity,
y = sensitivity,
colour = var
))
} else {
plot <-
ggplot2::ggplot(plotData, ggplot2::aes(x = 1 - specificity, y = sensitivity))
}
# self$results$debug$setContent(plotData)
plot <- plot +
ggplot2::geom_point() +
ggplot2::geom_line() +
ggplot2::xlab("1 - Specificity") +
ggplot2::ylab("Sensitivity") +
ggtheme + ggplot2::theme(plot.margin = ggplot2::margin(5.5, 5.5, 5.5, 5.5))
if (self$options$smoothing) {
if (self$options$displaySE) {
plot = plot +
ggplot2::geom_smooth(se = TRUE)
} else {
plot = plot +
ggplot2::geom_smooth(se = FALSE)
}
}
print(plot)
TRUE
}
)
)
sbalci/ClinicoPathJamoviModule documentation built on Feb. 25, 2025, 6:34 a.m.
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# This file is a generated template, your changes will not be overwritten
TestROCClass <- if (requireNamespace('jmvcore'))
R6::R6Class(
"TestROCClass",
inherit = TestROCBase,
private = list(
.init = function() {
# if (is.null(self$options$classVar) ||
# is.null(self$options$dependentVars)) {
# self$results$resultsTable$setVisible(visible = FALSE)
# }
},
.run = function() {
if (is.null(self$options$classVar) ||
is.null(self$options$dependentVars)) {
self$results$instructions$setContent(
"<html>
<head>
</head>
<body>
<div class='instructions'>
<p><b>This analysis is still in development: The format of results may change in future releases. Please report any errors or requests <a href='https://github.com/lucasjfriesen/jamoviPsychoPDA/issues' target = '_blank'>here</a></b></p>
<p>Welcome to PsychoPDA's Test ROC analysis To get started:</p>
<ol>
<li>Place the responses in the 'Dependent Variable' slot<br /><br /></li>
<li>Place the classification in the 'Class Variable' slot<br /><br /></li>
<li>[<em>Optional</em>] Place a grouping variable in the 'Grouping Variable' slot<br /><br /></li>
</ol>
<p>If you encounter any errors, or have questions, please see the <a href='https://lucasjfriesen.github.io/jamoviPsychoPDA_docs/measureDiagnostics_testROC_gettingStarted.html' target = '_blank'>documentation.</a></p>
<p>If this software is used in conducting published research, please do provide a citation using the information at the bottom of the analysis.</p>
</div>
</body>
</html>"
)
return()
} else {
self$results$instructions$setVisible(visible = FALSE)
procedureNotes <- paste0(
"<html>
<body>
<p>Procedure Notes</p>
<hr>",
"<p> The TestROC optimal cutpoint analysis has been completed using the following specifications: ",
"<p> </p>",
"<p> Measure Variable(s): ",
paste(unlist(self$options$dependentVars), collapse = ", "),
"</p>",
"<p> Class Variable: ",
self$options$classVar,
"</p>"
)
if (self$options$positiveClass == "") {
procedureNotes <- paste0(
procedureNotes,
"<p> Positive Class: ", self$data[,self$options$classVar][1], "</p>")
} else {
procedureNotes <- paste0(procedureNotes,
"<p> Positive Class: ",
self$options$positiveClass,
"</p>")
}
# Was there subgrouping?
if (!is.null(self$options$subGroup)) {
procedureNotes <- paste0(procedureNotes,
"<p> Sub-Group Variable: ",
self$options$subGroup,
"</p>")
}
procedureNotes <- paste0(
procedureNotes,
"<p> </p>",
"<p> Method: ",
self$options$method,
"</p>",
"<p> All Observed Cutpoints: ",
self$options$allObserved,
"</p>",
"<p> Metric: ",
self$options$metric,
"</p>",
"<p> Direction (relative to cutpoint): ",
self$options$direction,
"</p>",
"<p> Tie Breakers: ",
self$options$break_ties,
"</p>",
"<p> Metric Tolerance: ",
self$options$tol_metric,
"</p>",
"<p> </p>"
)
# If bootstrapping happened
if (self$options$boot_runs != 0) {
procedureNotes <- paste0(procedureNotes,
"<p> Bootstrap Runs: ",
self$options$boot_runs,
"</p>")
}
# Close the notes
procedureNotes <- paste0(
procedureNotes,
"<hr />
<p>For more information on how calculations are performed and interpretting results, please see the <a href='https://lucasjfriesen.github.io/jamoviPsychoPDA_docs/measureDiagnostics_testROC_gettingStarted.html' target = '_blank'>documentation</a></p>
</body>
</html>"
)
self$results$procedureNotes$setContent(procedureNotes)
}
# Var handling ----
data = self$data
if (self$options$method == "oc_manual") {
method = "cutpointr::oc_manual"
if (self$options$specifyCutScore == "") {
stop("Please specify a cut score for using this method.")
} else {
score = as.numeric(self$options$specifyCutScore)
}
} else {
method = paste0("cutpointr::", self$options$method)
score = NULL
}
if (method %in% c(
"cutpointr::maximize_metric",
"cutpointr::minimize_metric",
"cutpointr::maximize_loess_metric",
"cutpointr::minimize_loess_metric",
"cutpointr::maximize_spline_metric",
"cutpointr::minimize_spline_metric"
)) {
tol_metric = self$options$tol_metric
} else {
tol_metric = NULL
}
method = eval(parse(text = method))
metric = paste0("cutpointr::", self$options$metric)
metric = eval(parse(text = metric))
direction = self$options$direction
boot_runs = self$options$boot_runs
# use_midpoints = self$options$use_midpoint
break_ties = self$options$break_ties
break_ties = eval(parse(text = break_ties))
boot_runs = self$options$boot_runs
plotDataList <- data.frame(
var = list(),
cutpoint = list(),
sensitivity = list(),
specificity = list(),
ppv = list(),
npv = list(),
AUC = list(),
youden = list(),
stringsAsFactors = FALSE)
# Data ----
vars <- self$options$dependentVars
if (!is.null(self$options$subGroup)) {
subGroup = data[, self$options$subGroup]
classVar = data[, self$options$classVar]
uniqueGroups <- unique(subGroup)
vars <-
apply(expand.grid(vars, uniqueGroups), 1, paste, collapse = "_")
} else {
subGroup = NULL
}
aucList = list()
for (var in vars) {
if (!var %in% self$results$resultsTable$itemKeys) {
self$results$sensSpecTable$addItem(key = var)
self$results$resultsTable$addItem(key = var)
if (self$options$combinePlots == FALSE) {
self$results$plotROC$addItem(key = var)
}
}
if (is.null(subGroup)) {
dependentVar = as.numeric(data[, var])
classVar = data[, self$options$classVar]
} else {
dependentVar = as.numeric(data[subGroup == strsplit(var, split = "_")[[1]][2],
names(data) == strsplit(var, split = "_")[[1]][1]])
classVar = data[subGroup == strsplit(var, split = "_")[[1]][2],
self$options$classVar]
}
if (self$options$positiveClass == ""){
# self$results$debug$setContent(classVar[1])
pos_class <- classVar[1]
} else {
pos_class <- self$options$positiveClass
}
# Caclulations ----
results = cutpointr::cutpointr(
x = dependentVar,
class = classVar,
subgroup = NULL,
method = method,
cutpoint = score,
metric = metric,
direction = direction,
pos_class = pos_class,
# use_midpoints = use_midpoints,
tol_metric = tol_metric,
boot_runs = boot_runs,
break_ties = break_ties,
na.rm = TRUE
)
# self$results$debug$setContent(results)
if (!self$options$allObserved) {
resultsToDisplay <- sort(unlist(results$optimal_cutpoint))
} else {
resultsToDisplay <- sort(unlist(unique(dependentVar)))
}
# Confusion matrix ----
confusionMatrix <-
confusionMatrixForTable <- results$roc_curve[[1]]
if (!self$options$allObserved) {
confusionMatrixForTable = confusionMatrixForTable[confusionMatrixForTable$x.sorted %in% resultsToDisplay,]
}
if (self$options$sensSpecTable) {
self$results$sensSpecTable$setVisible(TRUE)
sensSpecRes <-
print.sensSpecTable(
Title = paste0(
"Scale: ",
var,
" | Score: ",
confusionMatrixForTable$x.sorted
),
TP = confusionMatrixForTable$tp,
FP = confusionMatrixForTable$fp,
TN = confusionMatrixForTable$tn,
FN = confusionMatrixForTable$fn
)
sensTable <- self$results$sensSpecTable$get(key = var)
sensTable$setContent(sensSpecRes)
sensTable$setVisible(TRUE)
}
# Results columns ----
sensList <-
(
cutpointr::sensitivity(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
) * 100
)
specList <-
(
cutpointr::specificity(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
) * 100
)
ppvList <- (
cutpointr::ppv(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
) * 100
)
npvList <- (
cutpointr::npv(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
) * 100
)
youdenList <-
cutpointr::youden(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
)
metricList <-
metric(
tp = confusionMatrix$tp,
fp = confusionMatrix$fp,
tn = confusionMatrix$tn,
fn = confusionMatrix$fn
)
resultsToReturn <- data.frame(
#scaleName = rep(var, times = length(sensList)),
cutpoint = as.character(confusionMatrix$x.sorted),
sensitivity = formatter(sensList),
specificity = formatter(specList),
ppv = formatter(ppvList),
npv = formatter(npvList),
AUC = results$AUC,
youden = youdenList,
metricValue = unname(metricList),
stringsAsFactors = FALSE # FUCK
)
aucList[[var]] = results$AUC
# State Savers ----
# Results table ----
resultState <- self$results$resultsTable$state
if (!is.null(resultState)) {
# ... populate the table from the state
} else {
# ... create the table and the state
table <- self$results$resultsTable$get(key = var)
for (row in resultsToDisplay) {
table$setTitle(paste0("Scale: ", var))
table$addRow(rowKey = row, value = resultsToReturn[resultsToReturn$cutpoint == row,])
}
self$results$resultsTable$setState(resultState)
}
# Plotting Data ----
if (self$options$plotROC == TRUE &
self$options$combinePlots == FALSE) {
image <- self$results$plotROC$get(key = var)
image$setTitle(paste0("ROC Curve: ", var))
image$setState(
data.frame(
var = var,
cutpoint = confusionMatrix$x.sorted,
sensitivity = sensList,
specificity = specList,
ppv = ppvList,
npv = npvList,
AUC = results$AUC,
youden = youdenList,
stringsAsFactors = FALSE # FUCK
)
)
} else {
plotDataList <- rbind(
plotDataList,
data.frame(
var = var,
cutpoint = confusionMatrix$x.sorted,
sensitivity = sensList,
specificity = specList,
ppv = ppvList,
npv = npvList,
AUC = results$AUC,
youden = youdenList,
stringsAsFactors = FALSE
)
)}}
if (self$options$plotROC == TRUE &
self$options$combinePlots == TRUE){
self$results$plotROC$addItem(key = 1)
image <- self$results$plotROC$get(key = 1)
image$setTitle(paste0("ROC Curve: Combined"))
image$setState(plotDataList)
#
}
# DeLong Test ----
# self$results$debug$setContent(classVar)
if (self$options$delongTest == TRUE) {
if (length(self$options$dependentVars) < 2){
stop("Please specify at least two dependent variables to use DeLong's test.")
}
if (!is.null(self$options$subGroup)) {
stop(
"DeLong's test does not currently support the grouping variable. If you would like to contribute/provide guidance, please use the contact information provided in the documentation."
)
} else {
delongResults <- deLong.test(
data = data.frame(lapply(data[, vars], as.numeric)),
classVar = as.character(classVar),
ref = NULL,
pos_class = pos_class,
conf.level = 0.95
)
}
self$results$delongTest$setVisible(visible = TRUE)
self$results$delongTest$setContent(paste0(capture.output(print.DeLong(
delongResults
))))
}
},
.plotROC = function(image, ggtheme, theme, ...) {
plotData <- data.frame(image$state)
if (self$options$combinePlots == TRUE) {
plot <-
ggplot2::ggplot(plotData,
ggplot2::aes(
x = 1 - specificity,
y = sensitivity,
colour = var
))
} else {
plot <-
ggplot2::ggplot(plotData, ggplot2::aes(x = 1 - specificity, y = sensitivity))
}
# self$results$debug$setContent(plotData)
plot <- plot +
ggplot2::geom_point() +
ggplot2::geom_line() +
ggplot2::xlab("1 - Specificity") +
ggplot2::ylab("Sensitivity") +
ggtheme + ggplot2::theme(plot.margin = ggplot2::margin(5.5, 5.5, 5.5, 5.5))
if (self$options$smoothing) {
if (self$options$displaySE) {
plot = plot +
ggplot2::geom_smooth(se = TRUE)
} else {
plot = plot +
ggplot2::geom_smooth(se = FALSE)
}
}
print(plot)
TRUE
}
)
)
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