R/psychopdaroc.h.R
In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research
Defines functions
psychopdaroc
Documented in
psychopdaroc
# This file is automatically generated, you probably don't want to edit this
psychopdarocOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"psychopdarocOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
dependentVars = NULL,
classVar = NULL,
positiveClass = NULL,
subGroup = NULL,
method = "maximize_metric",
metric = "youden",
direction = ">=",
specifyCutScore = "",
tol_metric = 0.05,
break_ties = "mean",
allObserved = FALSE,
boot_runs = 0,
usePriorPrev = FALSE,
priorPrev = 0.5,
costratioFP = 1,
sensSpecTable = FALSE,
showThresholdTable = FALSE,
maxThresholds = 20,
delongTest = FALSE,
plotROC = TRUE,
combinePlots = TRUE,
cleanPlot = FALSE,
showOptimalPoint = TRUE,
displaySE = FALSE,
smoothing = FALSE,
showConfidenceBands = FALSE,
legendPosition = "right",
directLabel = FALSE,
interactiveROC = FALSE,
showCriterionPlot = FALSE,
showPrevalencePlot = FALSE,
showDotPlot = FALSE,
precisionRecallCurve = FALSE,
partialAUC = FALSE,
partialAUCfrom = 0.8,
partialAUCto = 1,
rocSmoothingMethod = "none",
bootstrapCI = FALSE,
bootstrapReps = 2000,
quantileCIs = FALSE,
quantiles = "0.1,0.25,0.5,0.75,0.9",
compareClassifiers = FALSE,
calculateIDI = FALSE,
calculateNRI = FALSE,
refVar = NULL,
nriThresholds = "",
idiNriBootRuns = 1000, ...) {
super$initialize(
package="ClinicoPath",
name="psychopdaroc",
requiresData=TRUE,
...)
private$..dependentVars <- jmvcore::OptionVariables$new(
"dependentVars",
dependentVars,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..classVar <- jmvcore::OptionVariable$new(
"classVar",
classVar,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..positiveClass <- jmvcore::OptionLevel$new(
"positiveClass",
positiveClass,
variable="(classVar)")
private$..subGroup <- jmvcore::OptionVariable$new(
"subGroup",
subGroup,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..method <- jmvcore::OptionList$new(
"method",
method,
options=list(
"maximize_metric",
"minimize_metric",
"maximize_loess_metric",
"minimize_loess_metric",
"maximize_spline_metric",
"minimize_spline_metric",
"maximize_boot_metric",
"minimize_boot_metric",
"oc_youden_kernel",
"oc_youden_normal",
"oc_manual",
"oc_cost_ratio",
"oc_equal_sens_spec",
"oc_closest_01"),
default="maximize_metric")
private$..metric <- jmvcore::OptionList$new(
"metric",
metric,
options=list(
"youden",
"sum_sens_spec",
"accuracy",
"sum_ppv_npv",
"prod_sens_spec",
"prod_ppv_npv",
"cohens_kappa",
"abs_d_sens_spec",
"abs_d_ppv_npv",
"F1_score",
"odds_ratio",
"risk_ratio",
"misclassification_cost",
"total_utility",
"roc01",
"p_chisquared"),
default="youden")
private$..direction <- jmvcore::OptionList$new(
"direction",
direction,
options=list(
">=",
"<="),
default=">=")
private$..specifyCutScore <- jmvcore::OptionString$new(
"specifyCutScore",
specifyCutScore,
default="")
private$..tol_metric <- jmvcore::OptionNumber$new(
"tol_metric",
tol_metric,
default=0.05,
min=0,
max=1)
private$..break_ties <- jmvcore::OptionList$new(
"break_ties",
break_ties,
options=list(
"mean",
"median",
"c"),
default="mean")
private$..allObserved <- jmvcore::OptionBool$new(
"allObserved",
allObserved,
default=FALSE)
private$..boot_runs <- jmvcore::OptionNumber$new(
"boot_runs",
boot_runs,
default=0,
min=0,
max=10000)
private$..usePriorPrev <- jmvcore::OptionBool$new(
"usePriorPrev",
usePriorPrev,
default=FALSE)
private$..priorPrev <- jmvcore::OptionNumber$new(
"priorPrev",
priorPrev,
default=0.5,
min=0.001,
max=0.999)
private$..costratioFP <- jmvcore::OptionNumber$new(
"costratioFP",
costratioFP,
default=1,
min=0.01,
max=100)
private$..sensSpecTable <- jmvcore::OptionBool$new(
"sensSpecTable",
sensSpecTable,
default=FALSE)
private$..showThresholdTable <- jmvcore::OptionBool$new(
"showThresholdTable",
showThresholdTable,
default=FALSE)
private$..maxThresholds <- jmvcore::OptionNumber$new(
"maxThresholds",
maxThresholds,
default=20,
min=5,
max=100)
private$..delongTest <- jmvcore::OptionBool$new(
"delongTest",
delongTest,
default=FALSE)
private$..plotROC <- jmvcore::OptionBool$new(
"plotROC",
plotROC,
default=TRUE)
private$..combinePlots <- jmvcore::OptionBool$new(
"combinePlots",
combinePlots,
default=TRUE)
private$..cleanPlot <- jmvcore::OptionBool$new(
"cleanPlot",
cleanPlot,
default=FALSE)
private$..showOptimalPoint <- jmvcore::OptionBool$new(
"showOptimalPoint",
showOptimalPoint,
default=TRUE)
private$..displaySE <- jmvcore::OptionBool$new(
"displaySE",
displaySE,
default=FALSE)
private$..smoothing <- jmvcore::OptionBool$new(
"smoothing",
smoothing,
default=FALSE)
private$..showConfidenceBands <- jmvcore::OptionBool$new(
"showConfidenceBands",
showConfidenceBands,
default=FALSE)
private$..legendPosition <- jmvcore::OptionList$new(
"legendPosition",
legendPosition,
options=list(
"none",
"right",
"bottom",
"topleft",
"topright"),
default="right")
private$..directLabel <- jmvcore::OptionBool$new(
"directLabel",
directLabel,
default=FALSE)
private$..interactiveROC <- jmvcore::OptionBool$new(
"interactiveROC",
interactiveROC,
default=FALSE)
private$..showCriterionPlot <- jmvcore::OptionBool$new(
"showCriterionPlot",
showCriterionPlot,
default=FALSE)
private$..showPrevalencePlot <- jmvcore::OptionBool$new(
"showPrevalencePlot",
showPrevalencePlot,
default=FALSE)
private$..showDotPlot <- jmvcore::OptionBool$new(
"showDotPlot",
showDotPlot,
default=FALSE)
private$..precisionRecallCurve <- jmvcore::OptionBool$new(
"precisionRecallCurve",
precisionRecallCurve,
default=FALSE)
private$..partialAUC <- jmvcore::OptionBool$new(
"partialAUC",
partialAUC,
default=FALSE)
private$..partialAUCfrom <- jmvcore::OptionNumber$new(
"partialAUCfrom",
partialAUCfrom,
default=0.8,
min=0,
max=1)
private$..partialAUCto <- jmvcore::OptionNumber$new(
"partialAUCto",
partialAUCto,
default=1,
min=0,
max=1)
private$..rocSmoothingMethod <- jmvcore::OptionList$new(
"rocSmoothingMethod",
rocSmoothingMethod,
options=list(
"none",
"binormal",
"density",
"fitdistr"),
default="none")
private$..bootstrapCI <- jmvcore::OptionBool$new(
"bootstrapCI",
bootstrapCI,
default=FALSE)
private$..bootstrapReps <- jmvcore::OptionNumber$new(
"bootstrapReps",
bootstrapReps,
default=2000,
min=100,
max=10000)
private$..quantileCIs <- jmvcore::OptionBool$new(
"quantileCIs",
quantileCIs,
default=FALSE)
private$..quantiles <- jmvcore::OptionString$new(
"quantiles",
quantiles,
default="0.1,0.25,0.5,0.75,0.9")
private$..compareClassifiers <- jmvcore::OptionBool$new(
"compareClassifiers",
compareClassifiers,
default=FALSE)
private$..calculateIDI <- jmvcore::OptionBool$new(
"calculateIDI",
calculateIDI,
default=FALSE)
private$..calculateNRI <- jmvcore::OptionBool$new(
"calculateNRI",
calculateNRI,
default=FALSE)
private$..refVar <- jmvcore::OptionLevel$new(
"refVar",
refVar,
variable="(dependentVars)")
private$..nriThresholds <- jmvcore::OptionString$new(
"nriThresholds",
nriThresholds,
default="")
private$..idiNriBootRuns <- jmvcore::OptionNumber$new(
"idiNriBootRuns",
idiNriBootRuns,
default=1000,
min=100,
max=10000)
self$.addOption(private$..dependentVars)
self$.addOption(private$..classVar)
self$.addOption(private$..positiveClass)
self$.addOption(private$..subGroup)
self$.addOption(private$..method)
self$.addOption(private$..metric)
self$.addOption(private$..direction)
self$.addOption(private$..specifyCutScore)
self$.addOption(private$..tol_metric)
self$.addOption(private$..break_ties)
self$.addOption(private$..allObserved)
self$.addOption(private$..boot_runs)
self$.addOption(private$..usePriorPrev)
self$.addOption(private$..priorPrev)
self$.addOption(private$..costratioFP)
self$.addOption(private$..sensSpecTable)
self$.addOption(private$..showThresholdTable)
self$.addOption(private$..maxThresholds)
self$.addOption(private$..delongTest)
self$.addOption(private$..plotROC)
self$.addOption(private$..combinePlots)
self$.addOption(private$..cleanPlot)
self$.addOption(private$..showOptimalPoint)
self$.addOption(private$..displaySE)
self$.addOption(private$..smoothing)
self$.addOption(private$..showConfidenceBands)
self$.addOption(private$..legendPosition)
self$.addOption(private$..directLabel)
self$.addOption(private$..interactiveROC)
self$.addOption(private$..showCriterionPlot)
self$.addOption(private$..showPrevalencePlot)
self$.addOption(private$..showDotPlot)
self$.addOption(private$..precisionRecallCurve)
self$.addOption(private$..partialAUC)
self$.addOption(private$..partialAUCfrom)
self$.addOption(private$..partialAUCto)
self$.addOption(private$..rocSmoothingMethod)
self$.addOption(private$..bootstrapCI)
self$.addOption(private$..bootstrapReps)
self$.addOption(private$..quantileCIs)
self$.addOption(private$..quantiles)
self$.addOption(private$..compareClassifiers)
self$.addOption(private$..calculateIDI)
self$.addOption(private$..calculateNRI)
self$.addOption(private$..refVar)
self$.addOption(private$..nriThresholds)
self$.addOption(private$..idiNriBootRuns)
}),
active = list(
dependentVars = function() private$..dependentVars$value,
classVar = function() private$..classVar$value,
positiveClass = function() private$..positiveClass$value,
subGroup = function() private$..subGroup$value,
method = function() private$..method$value,
metric = function() private$..metric$value,
direction = function() private$..direction$value,
specifyCutScore = function() private$..specifyCutScore$value,
tol_metric = function() private$..tol_metric$value,
break_ties = function() private$..break_ties$value,
allObserved = function() private$..allObserved$value,
boot_runs = function() private$..boot_runs$value,
usePriorPrev = function() private$..usePriorPrev$value,
priorPrev = function() private$..priorPrev$value,
costratioFP = function() private$..costratioFP$value,
sensSpecTable = function() private$..sensSpecTable$value,
showThresholdTable = function() private$..showThresholdTable$value,
maxThresholds = function() private$..maxThresholds$value,
delongTest = function() private$..delongTest$value,
plotROC = function() private$..plotROC$value,
combinePlots = function() private$..combinePlots$value,
cleanPlot = function() private$..cleanPlot$value,
showOptimalPoint = function() private$..showOptimalPoint$value,
displaySE = function() private$..displaySE$value,
smoothing = function() private$..smoothing$value,
showConfidenceBands = function() private$..showConfidenceBands$value,
legendPosition = function() private$..legendPosition$value,
directLabel = function() private$..directLabel$value,
interactiveROC = function() private$..interactiveROC$value,
showCriterionPlot = function() private$..showCriterionPlot$value,
showPrevalencePlot = function() private$..showPrevalencePlot$value,
showDotPlot = function() private$..showDotPlot$value,
precisionRecallCurve = function() private$..precisionRecallCurve$value,
partialAUC = function() private$..partialAUC$value,
partialAUCfrom = function() private$..partialAUCfrom$value,
partialAUCto = function() private$..partialAUCto$value,
rocSmoothingMethod = function() private$..rocSmoothingMethod$value,
bootstrapCI = function() private$..bootstrapCI$value,
bootstrapReps = function() private$..bootstrapReps$value,
quantileCIs = function() private$..quantileCIs$value,
quantiles = function() private$..quantiles$value,
compareClassifiers = function() private$..compareClassifiers$value,
calculateIDI = function() private$..calculateIDI$value,
calculateNRI = function() private$..calculateNRI$value,
refVar = function() private$..refVar$value,
nriThresholds = function() private$..nriThresholds$value,
idiNriBootRuns = function() private$..idiNriBootRuns$value),
private = list(
..dependentVars = NA,
..classVar = NA,
..positiveClass = NA,
..subGroup = NA,
..method = NA,
..metric = NA,
..direction = NA,
..specifyCutScore = NA,
..tol_metric = NA,
..break_ties = NA,
..allObserved = NA,
..boot_runs = NA,
..usePriorPrev = NA,
..priorPrev = NA,
..costratioFP = NA,
..sensSpecTable = NA,
..showThresholdTable = NA,
..maxThresholds = NA,
..delongTest = NA,
..plotROC = NA,
..combinePlots = NA,
..cleanPlot = NA,
..showOptimalPoint = NA,
..displaySE = NA,
..smoothing = NA,
..showConfidenceBands = NA,
..legendPosition = NA,
..directLabel = NA,
..interactiveROC = NA,
..showCriterionPlot = NA,
..showPrevalencePlot = NA,
..showDotPlot = NA,
..precisionRecallCurve = NA,
..partialAUC = NA,
..partialAUCfrom = NA,
..partialAUCto = NA,
..rocSmoothingMethod = NA,
..bootstrapCI = NA,
..bootstrapReps = NA,
..quantileCIs = NA,
..quantiles = NA,
..compareClassifiers = NA,
..calculateIDI = NA,
..calculateNRI = NA,
..refVar = NA,
..nriThresholds = NA,
..idiNriBootRuns = NA)
)
psychopdarocResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"psychopdarocResults",
inherit = jmvcore::Group,
active = list(
instructions = function() private$.items[["instructions"]],
procedureNotes = function() private$.items[["procedureNotes"]],
simpleResultsTable = function() private$.items[["simpleResultsTable"]],
resultsTable = function() private$.items[["resultsTable"]],
sensSpecTable = function() private$.items[["sensSpecTable"]],
thresholdTable = function() private$.items[["thresholdTable"]],
aucSummaryTable = function() private$.items[["aucSummaryTable"]],
delongComparisonTable = function() private$.items[["delongComparisonTable"]],
delongTest = function() private$.items[["delongTest"]],
plotROC = function() private$.items[["plotROC"]],
interactivePlot = function() private$.items[["interactivePlot"]],
criterionPlot = function() private$.items[["criterionPlot"]],
prevalencePlot = function() private$.items[["prevalencePlot"]],
dotPlot = function() private$.items[["dotPlot"]],
dotPlotMessage = function() private$.items[["dotPlotMessage"]],
precisionRecallPlot = function() private$.items[["precisionRecallPlot"]],
idiTable = function() private$.items[["idiTable"]],
nriTable = function() private$.items[["nriTable"]],
partialAUCTable = function() private$.items[["partialAUCTable"]],
bootstrapCITable = function() private$.items[["bootstrapCITable"]],
rocComparisonTable = function() private$.items[["rocComparisonTable"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="ROC Analysis",
refs=list(
"cutpointr",
"pROC",
"MASS",
"plotROC"))
self$add(jmvcore::Html$new(
options=options,
name="instructions",
visible=TRUE))
self$add(jmvcore::Html$new(
options=options,
name="procedureNotes",
visible=TRUE))
self$add(jmvcore::Table$new(
options=options,
name="simpleResultsTable",
title="ROC Analysis Summary",
visible=TRUE,
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="auc",
`title`="AUC",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Array$new(
options=options,
name="resultsTable",
title="Optimal Cutpoints and Performance",
visible=TRUE,
refs=list(
"cutpointr"),
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"method",
"metric",
"direction",
"allObserved",
"specifyCutScore",
"boot_runs",
"break_ties",
"tol_metric"),
template=jmvcore::Table$new(
options=options,
rows=0,
columns=list(
list(
`name`="cutpoint",
`title`="Cutpoint",
`type`="number",
`format`="zto"),
list(
`name`="sensitivity",
`title`="Sensitivity",
`type`="number",
`format`="pc"),
list(
`name`="specificity",
`title`="Specificity",
`type`="number",
`format`="pc"),
list(
`name`="ppv",
`title`="PPV",
`type`="number",
`format`="pc"),
list(
`name`="npv",
`title`="NPV",
`type`="number",
`format`="pc"),
list(
`name`="youden",
`title`="Youden's J",
`type`="number",
`format`="zto"),
list(
`name`="AUC",
`title`="AUC",
`type`="number",
`format`="zto"),
list(
`name`="metricValue",
`title`="Metric Score",
`type`="number",
`format`="zto")))))
self$add(jmvcore::Array$new(
options=options,
name="sensSpecTable",
title="Confusion Matrices",
visible="(sensSpecTable)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"method",
"allObserved",
"specifyCutScore"),
template=jmvcore::Html$new(
options=options)))
self$add(jmvcore::Table$new(
options=options,
name="thresholdTable",
title="Detailed Threshold Performance",
visible="(showThresholdTable)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"direction"),
columns=list(
list(
`name`="threshold",
`title`="Cut-off",
`type`="number",
`format`="zto"),
list(
`name`="sensitivity",
`title`="Sensitivity",
`type`="number",
`format`="pc"),
list(
`name`="specificity",
`title`="Specificity",
`type`="number",
`format`="pc"),
list(
`name`="accuracy",
`title`="Accuracy",
`type`="number",
`format`="pc"),
list(
`name`="ppv",
`title`="PPV",
`type`="number",
`format`="pc"),
list(
`name`="npv",
`title`="NPV",
`type`="number",
`format`="pc"),
list(
`name`="plr",
`title`="+LR",
`type`="number",
`format`="zto"),
list(
`name`="nlr",
`title`="-LR",
`type`="number",
`format`="zto"),
list(
`name`="youden",
`title`="Youden's J",
`type`="number",
`format`="zto"))))
self$add(jmvcore::Table$new(
options=options,
name="aucSummaryTable",
title="Area Under the ROC Curve",
visible=TRUE,
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="auc",
`title`="AUC",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="delongComparisonTable",
title="DeLong Test Pairwise Comparisons",
visible="(delongTest)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass"),
columns=list(
list(
`name`="comparison",
`title`="Comparison",
`type`="text"),
list(
`name`="auc_diff",
`title`="AUC Difference",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="z",
`title`="Z-statistic",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Preformatted$new(
options=options,
name="delongTest",
title="DeLong Test Details",
visible="(delongTest)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass"),
refs=list(
"DeLong1988")))
self$add(jmvcore::Array$new(
options=options,
name="plotROC",
title="ROC Curves",
visible="(plotROC)",
template=jmvcore::Image$new(
options=options,
width=550,
height=450,
renderFun=".plotROC",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"direction",
"smoothing",
"showOptimalPoint",
"cleanPlot",
"showConfidenceBands",
"quantileCIs"))))
self$add(jmvcore::Image$new(
options=options,
name="interactivePlot",
title="Interactive ROC Plot",
width=650,
height=500,
renderFun=".plotInteractiveROC",
visible="(interactiveROC)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup"),
refs=list(
"plotROC")))
self$add(jmvcore::Array$new(
options=options,
name="criterionPlot",
title="Sensitivity/Specificity vs. Threshold",
visible="(showCriterionPlot)",
template=jmvcore::Image$new(
options=options,
title="$key",
width=600,
height=400,
renderFun=".plotCriterion",
requiresData=TRUE,
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"method",
"metric",
"direction"))))
self$add(jmvcore::Array$new(
options=options,
name="prevalencePlot",
title="Predictive Values vs. Prevalence",
visible="(showPrevalencePlot)",
template=jmvcore::Image$new(
options=options,
title="$key",
width=600,
height=400,
renderFun=".plotPrevalence",
requiresData=TRUE,
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"usePriorPrev",
"priorPrev"))))
self$add(jmvcore::Array$new(
options=options,
name="dotPlot",
title="Test Values Distribution",
visible="(showDotPlot)",
template=jmvcore::Image$new(
options=options,
title="$key",
width=600,
height=400,
renderFun=".plotDot",
requiresData=TRUE,
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"direction"))))
self$add(jmvcore::Html$new(
options=options,
name="dotPlotMessage",
title="Dot Plot Note",
visible="(showDotPlot && combinePlots)"))
self$add(jmvcore::Array$new(
options=options,
name="precisionRecallPlot",
title="Precision-Recall Curves",
visible="(precisionRecallCurve)",
template=jmvcore::Image$new(
options=options,
width=550,
height=450,
renderFun=".plotPrecisionRecall",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup"))))
self$add(jmvcore::Table$new(
options=options,
name="idiTable",
title="Integrated Discrimination Improvement (IDI)",
visible="(calculateIDI)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"refVar",
"idiNriBootRuns",
"direction"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="refVar",
`title`="Reference",
`type`="text"),
list(
`name`="idi",
`title`="IDI",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue")),
refs=list(
"Pencina2008")))
self$add(jmvcore::Table$new(
options=options,
name="nriTable",
title="Net Reclassification Index (NRI)",
visible="(calculateNRI)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"refVar",
"nriThresholds",
"idiNriBootRuns",
"direction"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="refVar",
`title`="Reference",
`type`="text"),
list(
`name`="nri",
`title`="NRI",
`type`="number",
`format`="zto"),
list(
`name`="event_nri",
`title`="Event NRI",
`type`="number",
`format`="zto"),
list(
`name`="non_event_nri",
`title`="Non-Event NRI",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue")),
refs=list(
"Pencina2008",
"Pencina2011")))
self$add(jmvcore::Table$new(
options=options,
name="partialAUCTable",
title="Partial AUC Results",
visible="(partialAUC)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"partialAUCfrom",
"partialAUCto"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="pAUC",
`title`="Partial AUC",
`type`="number",
`format`="zto"),
list(
`name`="pAUC_normalized",
`title`="Normalized pAUC",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="spec_range",
`title`="Specificity Range",
`type`="text")),
refs=list(
"McClish1989")))
self$add(jmvcore::Table$new(
options=options,
name="bootstrapCITable",
title="Bootstrap Confidence Intervals",
visible="(bootstrapCI)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"bootstrapReps"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="parameter",
`title`="Parameter",
`type`="text"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"))))
self$add(jmvcore::Table$new(
options=options,
name="rocComparisonTable",
title="Classifier Performance Comparison",
visible="(compareClassifiers)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="auc",
`title`="AUC",
`type`="number",
`format`="zto"),
list(
`name`="auprc",
`title`="AUPRC",
`type`="number",
`format`="zto"),
list(
`name`="brier",
`title`="Brier Score",
`type`="number",
`format`="zto"),
list(
`name`="f1_score",
`title`="F1 Score",
`type`="number",
`format`="zto"),
list(
`name`="accuracy",
`title`="Accuracy",
`type`="number",
`format`="pc"),
list(
`name`="balanced_accuracy",
`title`="Balanced Accuracy",
`type`="number",
`format`="pc"))))}))
psychopdarocBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"psychopdarocBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "ClinicoPath",
name = "psychopdaroc",
version = c(0,0,3),
options = options,
results = psychopdarocResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' ROC Analysis
#'
#' Receiver Operating Characteristic (ROC) curve analysis with optimal
#' cutpoint determination.
#'
#' @param data The data as a data frame.
#' @param dependentVars Test variable(s) to be evaluated for classification
#' performance. Multiple variables can be selected for comparison.
#' @param classVar Binary classification variable representing the true class
#' (gold standard). Must have exactly two levels.
#' @param positiveClass Specifies which level of the class variable should be
#' treated as the positive class.
#' @param subGroup Optional grouping variable for stratified analysis. ROC
#' curves will be calculated separately for each group.
#' @param method Method for determining the optimal cutpoint. Different
#' methods optimize different aspects of classifier performance.
#' @param metric Metric to optimize when determining the cutpoint. Only
#' applies to maximize/minimize methods.
#' @param direction Direction of classification relative to the cutpoint. Use
#' '>=' when higher test values indicate the positive class.
#' @param specifyCutScore Specific cutpoint value to use when method is set to
#' 'Manual cutpoint'.
#' @param tol_metric Tolerance for the metric value when multiple cutpoints
#' yield similar performance. Cutpoints within this tolerance are considered
#' equivalent.
#' @param break_ties Method for handling ties when multiple cutpoints achieve
#' the same metric value.
#' @param allObserved Display performance metrics for all observed test values
#' as potential cutpoints, not just the optimal cutpoint.
#' @param boot_runs Number of bootstrap iterations for methods using
#' bootstrapping. Set to 0 to disable bootstrapping.
#' @param usePriorPrev Use a specified prior prevalence instead of the sample
#' prevalence for calculating predictive values.
#' @param priorPrev Population prevalence to use for predictive value
#' calculations. Only used when 'Use Prior Prevalence' is checked.
#' @param costratioFP Relative cost of false positives compared to false
#' negatives. Values > 1 penalize false positives more heavily.
#' @param sensSpecTable Display detailed confusion matrices at optimal
#' cutpoints.
#' @param showThresholdTable Display detailed table with performance metrics
#' at multiple thresholds.
#' @param maxThresholds Maximum number of threshold values to show in the
#' threshold table.
#' @param delongTest Perform DeLong's test for comparing AUCs between multiple
#' test variables. Requires at least two test variables.
#' @param plotROC Display ROC curves for visual assessment of classifier
#' performance.
#' @param combinePlots When multiple test variables are selected, combine all
#' ROC curves in a single plot.
#' @param cleanPlot Create clean ROC curves without annotations, suitable for
#' publications.
#' @param showOptimalPoint Display the optimal cutpoint on the ROC curve.
#' @param displaySE Display standard error bands on ROC curves (when LOESS
#' smoothing is applied).
#' @param smoothing Apply LOESS smoothing to ROC curves for visualization.
#' @param showConfidenceBands Display confidence bands around the ROC curve.
#' @param legendPosition Position of the legend in plots with multiple ROC
#' curves.
#' @param directLabel Label curves directly on the plot instead of using a
#' legend.
#' @param interactiveROC Create an interactive HTML ROC plot (requires plotROC
#' package).
#' @param showCriterionPlot Plot showing how sensitivity and specificity
#' change across different thresholds.
#' @param showPrevalencePlot Plot showing how PPV and NPV change with disease
#' prevalence.
#' @param showDotPlot Dot plot showing the distribution of test values by
#' class.
#' @param precisionRecallCurve Display precision-recall curves alongside ROC
#' curves.
#' @param partialAUC Calculate AUC for a specific region of the ROC curve.
#' @param partialAUCfrom Lower bound of specificity range for partial AUC
#' calculation.
#' @param partialAUCto Upper bound of specificity range for partial AUC
#' calculation.
#' @param rocSmoothingMethod Method for smoothing the ROC curve (requires pROC
#' package).
#' @param bootstrapCI Calculate bootstrap confidence intervals for AUC and
#' optimal cutpoints.
#' @param bootstrapReps Number of bootstrap replications for confidence
#' interval calculation.
#' @param quantileCIs Display confidence intervals at specific quantiles of
#' the test variable.
#' @param quantiles Comma-separated list of quantiles (0-1) at which to
#' display confidence intervals.
#' @param compareClassifiers Perform comprehensive comparison of classifier
#' performance metrics.
#' @param calculateIDI Calculate Integrated Discrimination Improvement for
#' model comparison.
#' @param calculateNRI Calculate Net Reclassification Index for model
#' comparison.
#' @param refVar Reference test variable for IDI and NRI calculations. Other
#' variables will be compared against this reference.
#' @param nriThresholds Comma-separated probability thresholds (0-1) defining
#' risk categories for NRI. Leave empty for continuous NRI.
#' @param idiNriBootRuns Number of bootstrap iterations for IDI and NRI
#' confidence intervals.
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$instructions} \tab \tab \tab \tab \tab a html \cr
#' \code{results$procedureNotes} \tab \tab \tab \tab \tab a html \cr
#' \code{results$simpleResultsTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$resultsTable} \tab \tab \tab \tab \tab an array of tables \cr
#' \code{results$sensSpecTable} \tab \tab \tab \tab \tab an array of htmls \cr
#' \code{results$thresholdTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$aucSummaryTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$delongComparisonTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$delongTest} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$plotROC} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$interactivePlot} \tab \tab \tab \tab \tab an image \cr
#' \code{results$criterionPlot} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$prevalencePlot} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$dotPlot} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$dotPlotMessage} \tab \tab \tab \tab \tab a html \cr
#' \code{results$precisionRecallPlot} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$idiTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$nriTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$partialAUCTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$bootstrapCITable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$rocComparisonTable} \tab \tab \tab \tab \tab a table \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$simpleResultsTable$asDF}
#'
#' \code{as.data.frame(results$simpleResultsTable)}
#'
#' @export
psychopdaroc <- function(
data,
dependentVars,
classVar,
positiveClass,
subGroup,
method = "maximize_metric",
metric = "youden",
direction = ">=",
specifyCutScore = "",
tol_metric = 0.05,
break_ties = "mean",
allObserved = FALSE,
boot_runs = 0,
usePriorPrev = FALSE,
priorPrev = 0.5,
costratioFP = 1,
sensSpecTable = FALSE,
showThresholdTable = FALSE,
maxThresholds = 20,
delongTest = FALSE,
plotROC = TRUE,
combinePlots = TRUE,
cleanPlot = FALSE,
showOptimalPoint = TRUE,
displaySE = FALSE,
smoothing = FALSE,
showConfidenceBands = FALSE,
legendPosition = "right",
directLabel = FALSE,
interactiveROC = FALSE,
showCriterionPlot = FALSE,
showPrevalencePlot = FALSE,
showDotPlot = FALSE,
precisionRecallCurve = FALSE,
partialAUC = FALSE,
partialAUCfrom = 0.8,
partialAUCto = 1,
rocSmoothingMethod = "none",
bootstrapCI = FALSE,
bootstrapReps = 2000,
quantileCIs = FALSE,
quantiles = "0.1,0.25,0.5,0.75,0.9",
compareClassifiers = FALSE,
calculateIDI = FALSE,
calculateNRI = FALSE,
refVar,
nriThresholds = "",
idiNriBootRuns = 1000) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("psychopdaroc requires jmvcore to be installed (restart may be required)")
if ( ! missing(dependentVars)) dependentVars <- jmvcore::resolveQuo(jmvcore::enquo(dependentVars))
if ( ! missing(classVar)) classVar <- jmvcore::resolveQuo(jmvcore::enquo(classVar))
if ( ! missing(subGroup)) subGroup <- jmvcore::resolveQuo(jmvcore::enquo(subGroup))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(dependentVars), dependentVars, NULL),
`if`( ! missing(classVar), classVar, NULL),
`if`( ! missing(subGroup), subGroup, NULL))
for (v in classVar) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in subGroup) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- psychopdarocOptions$new(
dependentVars = dependentVars,
classVar = classVar,
positiveClass = positiveClass,
subGroup = subGroup,
method = method,
metric = metric,
direction = direction,
specifyCutScore = specifyCutScore,
tol_metric = tol_metric,
break_ties = break_ties,
allObserved = allObserved,
boot_runs = boot_runs,
usePriorPrev = usePriorPrev,
priorPrev = priorPrev,
costratioFP = costratioFP,
sensSpecTable = sensSpecTable,
showThresholdTable = showThresholdTable,
maxThresholds = maxThresholds,
delongTest = delongTest,
plotROC = plotROC,
combinePlots = combinePlots,
cleanPlot = cleanPlot,
showOptimalPoint = showOptimalPoint,
displaySE = displaySE,
smoothing = smoothing,
showConfidenceBands = showConfidenceBands,
legendPosition = legendPosition,
directLabel = directLabel,
interactiveROC = interactiveROC,
showCriterionPlot = showCriterionPlot,
showPrevalencePlot = showPrevalencePlot,
showDotPlot = showDotPlot,
precisionRecallCurve = precisionRecallCurve,
partialAUC = partialAUC,
partialAUCfrom = partialAUCfrom,
partialAUCto = partialAUCto,
rocSmoothingMethod = rocSmoothingMethod,
bootstrapCI = bootstrapCI,
bootstrapReps = bootstrapReps,
quantileCIs = quantileCIs,
quantiles = quantiles,
compareClassifiers = compareClassifiers,
calculateIDI = calculateIDI,
calculateNRI = calculateNRI,
refVar = refVar,
nriThresholds = nriThresholds,
idiNriBootRuns = idiNriBootRuns)
analysis <- psychopdarocClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions psychopdaroc
Documented in psychopdaroc
# This file is automatically generated, you probably don't want to edit this
psychopdarocOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"psychopdarocOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
dependentVars = NULL,
classVar = NULL,
positiveClass = NULL,
subGroup = NULL,
method = "maximize_metric",
metric = "youden",
direction = ">=",
specifyCutScore = "",
tol_metric = 0.05,
break_ties = "mean",
allObserved = FALSE,
boot_runs = 0,
usePriorPrev = FALSE,
priorPrev = 0.5,
costratioFP = 1,
sensSpecTable = FALSE,
showThresholdTable = FALSE,
maxThresholds = 20,
delongTest = FALSE,
plotROC = TRUE,
combinePlots = TRUE,
cleanPlot = FALSE,
showOptimalPoint = TRUE,
displaySE = FALSE,
smoothing = FALSE,
showConfidenceBands = FALSE,
legendPosition = "right",
directLabel = FALSE,
interactiveROC = FALSE,
showCriterionPlot = FALSE,
showPrevalencePlot = FALSE,
showDotPlot = FALSE,
precisionRecallCurve = FALSE,
partialAUC = FALSE,
partialAUCfrom = 0.8,
partialAUCto = 1,
rocSmoothingMethod = "none",
bootstrapCI = FALSE,
bootstrapReps = 2000,
quantileCIs = FALSE,
quantiles = "0.1,0.25,0.5,0.75,0.9",
compareClassifiers = FALSE,
calculateIDI = FALSE,
calculateNRI = FALSE,
refVar = NULL,
nriThresholds = "",
idiNriBootRuns = 1000, ...) {
super$initialize(
package="ClinicoPath",
name="psychopdaroc",
requiresData=TRUE,
...)
private$..dependentVars <- jmvcore::OptionVariables$new(
"dependentVars",
dependentVars,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..classVar <- jmvcore::OptionVariable$new(
"classVar",
classVar,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..positiveClass <- jmvcore::OptionLevel$new(
"positiveClass",
positiveClass,
variable="(classVar)")
private$..subGroup <- jmvcore::OptionVariable$new(
"subGroup",
subGroup,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..method <- jmvcore::OptionList$new(
"method",
method,
options=list(
"maximize_metric",
"minimize_metric",
"maximize_loess_metric",
"minimize_loess_metric",
"maximize_spline_metric",
"minimize_spline_metric",
"maximize_boot_metric",
"minimize_boot_metric",
"oc_youden_kernel",
"oc_youden_normal",
"oc_manual",
"oc_cost_ratio",
"oc_equal_sens_spec",
"oc_closest_01"),
default="maximize_metric")
private$..metric <- jmvcore::OptionList$new(
"metric",
metric,
options=list(
"youden",
"sum_sens_spec",
"accuracy",
"sum_ppv_npv",
"prod_sens_spec",
"prod_ppv_npv",
"cohens_kappa",
"abs_d_sens_spec",
"abs_d_ppv_npv",
"F1_score",
"odds_ratio",
"risk_ratio",
"misclassification_cost",
"total_utility",
"roc01",
"p_chisquared"),
default="youden")
private$..direction <- jmvcore::OptionList$new(
"direction",
direction,
options=list(
">=",
"<="),
default=">=")
private$..specifyCutScore <- jmvcore::OptionString$new(
"specifyCutScore",
specifyCutScore,
default="")
private$..tol_metric <- jmvcore::OptionNumber$new(
"tol_metric",
tol_metric,
default=0.05,
min=0,
max=1)
private$..break_ties <- jmvcore::OptionList$new(
"break_ties",
break_ties,
options=list(
"mean",
"median",
"c"),
default="mean")
private$..allObserved <- jmvcore::OptionBool$new(
"allObserved",
allObserved,
default=FALSE)
private$..boot_runs <- jmvcore::OptionNumber$new(
"boot_runs",
boot_runs,
default=0,
min=0,
max=10000)
private$..usePriorPrev <- jmvcore::OptionBool$new(
"usePriorPrev",
usePriorPrev,
default=FALSE)
private$..priorPrev <- jmvcore::OptionNumber$new(
"priorPrev",
priorPrev,
default=0.5,
min=0.001,
max=0.999)
private$..costratioFP <- jmvcore::OptionNumber$new(
"costratioFP",
costratioFP,
default=1,
min=0.01,
max=100)
private$..sensSpecTable <- jmvcore::OptionBool$new(
"sensSpecTable",
sensSpecTable,
default=FALSE)
private$..showThresholdTable <- jmvcore::OptionBool$new(
"showThresholdTable",
showThresholdTable,
default=FALSE)
private$..maxThresholds <- jmvcore::OptionNumber$new(
"maxThresholds",
maxThresholds,
default=20,
min=5,
max=100)
private$..delongTest <- jmvcore::OptionBool$new(
"delongTest",
delongTest,
default=FALSE)
private$..plotROC <- jmvcore::OptionBool$new(
"plotROC",
plotROC,
default=TRUE)
private$..combinePlots <- jmvcore::OptionBool$new(
"combinePlots",
combinePlots,
default=TRUE)
private$..cleanPlot <- jmvcore::OptionBool$new(
"cleanPlot",
cleanPlot,
default=FALSE)
private$..showOptimalPoint <- jmvcore::OptionBool$new(
"showOptimalPoint",
showOptimalPoint,
default=TRUE)
private$..displaySE <- jmvcore::OptionBool$new(
"displaySE",
displaySE,
default=FALSE)
private$..smoothing <- jmvcore::OptionBool$new(
"smoothing",
smoothing,
default=FALSE)
private$..showConfidenceBands <- jmvcore::OptionBool$new(
"showConfidenceBands",
showConfidenceBands,
default=FALSE)
private$..legendPosition <- jmvcore::OptionList$new(
"legendPosition",
legendPosition,
options=list(
"none",
"right",
"bottom",
"topleft",
"topright"),
default="right")
private$..directLabel <- jmvcore::OptionBool$new(
"directLabel",
directLabel,
default=FALSE)
private$..interactiveROC <- jmvcore::OptionBool$new(
"interactiveROC",
interactiveROC,
default=FALSE)
private$..showCriterionPlot <- jmvcore::OptionBool$new(
"showCriterionPlot",
showCriterionPlot,
default=FALSE)
private$..showPrevalencePlot <- jmvcore::OptionBool$new(
"showPrevalencePlot",
showPrevalencePlot,
default=FALSE)
private$..showDotPlot <- jmvcore::OptionBool$new(
"showDotPlot",
showDotPlot,
default=FALSE)
private$..precisionRecallCurve <- jmvcore::OptionBool$new(
"precisionRecallCurve",
precisionRecallCurve,
default=FALSE)
private$..partialAUC <- jmvcore::OptionBool$new(
"partialAUC",
partialAUC,
default=FALSE)
private$..partialAUCfrom <- jmvcore::OptionNumber$new(
"partialAUCfrom",
partialAUCfrom,
default=0.8,
min=0,
max=1)
private$..partialAUCto <- jmvcore::OptionNumber$new(
"partialAUCto",
partialAUCto,
default=1,
min=0,
max=1)
private$..rocSmoothingMethod <- jmvcore::OptionList$new(
"rocSmoothingMethod",
rocSmoothingMethod,
options=list(
"none",
"binormal",
"density",
"fitdistr"),
default="none")
private$..bootstrapCI <- jmvcore::OptionBool$new(
"bootstrapCI",
bootstrapCI,
default=FALSE)
private$..bootstrapReps <- jmvcore::OptionNumber$new(
"bootstrapReps",
bootstrapReps,
default=2000,
min=100,
max=10000)
private$..quantileCIs <- jmvcore::OptionBool$new(
"quantileCIs",
quantileCIs,
default=FALSE)
private$..quantiles <- jmvcore::OptionString$new(
"quantiles",
quantiles,
default="0.1,0.25,0.5,0.75,0.9")
private$..compareClassifiers <- jmvcore::OptionBool$new(
"compareClassifiers",
compareClassifiers,
default=FALSE)
private$..calculateIDI <- jmvcore::OptionBool$new(
"calculateIDI",
calculateIDI,
default=FALSE)
private$..calculateNRI <- jmvcore::OptionBool$new(
"calculateNRI",
calculateNRI,
default=FALSE)
private$..refVar <- jmvcore::OptionLevel$new(
"refVar",
refVar,
variable="(dependentVars)")
private$..nriThresholds <- jmvcore::OptionString$new(
"nriThresholds",
nriThresholds,
default="")
private$..idiNriBootRuns <- jmvcore::OptionNumber$new(
"idiNriBootRuns",
idiNriBootRuns,
default=1000,
min=100,
max=10000)
self$.addOption(private$..dependentVars)
self$.addOption(private$..classVar)
self$.addOption(private$..positiveClass)
self$.addOption(private$..subGroup)
self$.addOption(private$..method)
self$.addOption(private$..metric)
self$.addOption(private$..direction)
self$.addOption(private$..specifyCutScore)
self$.addOption(private$..tol_metric)
self$.addOption(private$..break_ties)
self$.addOption(private$..allObserved)
self$.addOption(private$..boot_runs)
self$.addOption(private$..usePriorPrev)
self$.addOption(private$..priorPrev)
self$.addOption(private$..costratioFP)
self$.addOption(private$..sensSpecTable)
self$.addOption(private$..showThresholdTable)
self$.addOption(private$..maxThresholds)
self$.addOption(private$..delongTest)
self$.addOption(private$..plotROC)
self$.addOption(private$..combinePlots)
self$.addOption(private$..cleanPlot)
self$.addOption(private$..showOptimalPoint)
self$.addOption(private$..displaySE)
self$.addOption(private$..smoothing)
self$.addOption(private$..showConfidenceBands)
self$.addOption(private$..legendPosition)
self$.addOption(private$..directLabel)
self$.addOption(private$..interactiveROC)
self$.addOption(private$..showCriterionPlot)
self$.addOption(private$..showPrevalencePlot)
self$.addOption(private$..showDotPlot)
self$.addOption(private$..precisionRecallCurve)
self$.addOption(private$..partialAUC)
self$.addOption(private$..partialAUCfrom)
self$.addOption(private$..partialAUCto)
self$.addOption(private$..rocSmoothingMethod)
self$.addOption(private$..bootstrapCI)
self$.addOption(private$..bootstrapReps)
self$.addOption(private$..quantileCIs)
self$.addOption(private$..quantiles)
self$.addOption(private$..compareClassifiers)
self$.addOption(private$..calculateIDI)
self$.addOption(private$..calculateNRI)
self$.addOption(private$..refVar)
self$.addOption(private$..nriThresholds)
self$.addOption(private$..idiNriBootRuns)
}),
active = list(
dependentVars = function() private$..dependentVars$value,
classVar = function() private$..classVar$value,
positiveClass = function() private$..positiveClass$value,
subGroup = function() private$..subGroup$value,
method = function() private$..method$value,
metric = function() private$..metric$value,
direction = function() private$..direction$value,
specifyCutScore = function() private$..specifyCutScore$value,
tol_metric = function() private$..tol_metric$value,
break_ties = function() private$..break_ties$value,
allObserved = function() private$..allObserved$value,
boot_runs = function() private$..boot_runs$value,
usePriorPrev = function() private$..usePriorPrev$value,
priorPrev = function() private$..priorPrev$value,
costratioFP = function() private$..costratioFP$value,
sensSpecTable = function() private$..sensSpecTable$value,
showThresholdTable = function() private$..showThresholdTable$value,
maxThresholds = function() private$..maxThresholds$value,
delongTest = function() private$..delongTest$value,
plotROC = function() private$..plotROC$value,
combinePlots = function() private$..combinePlots$value,
cleanPlot = function() private$..cleanPlot$value,
showOptimalPoint = function() private$..showOptimalPoint$value,
displaySE = function() private$..displaySE$value,
smoothing = function() private$..smoothing$value,
showConfidenceBands = function() private$..showConfidenceBands$value,
legendPosition = function() private$..legendPosition$value,
directLabel = function() private$..directLabel$value,
interactiveROC = function() private$..interactiveROC$value,
showCriterionPlot = function() private$..showCriterionPlot$value,
showPrevalencePlot = function() private$..showPrevalencePlot$value,
showDotPlot = function() private$..showDotPlot$value,
precisionRecallCurve = function() private$..precisionRecallCurve$value,
partialAUC = function() private$..partialAUC$value,
partialAUCfrom = function() private$..partialAUCfrom$value,
partialAUCto = function() private$..partialAUCto$value,
rocSmoothingMethod = function() private$..rocSmoothingMethod$value,
bootstrapCI = function() private$..bootstrapCI$value,
bootstrapReps = function() private$..bootstrapReps$value,
quantileCIs = function() private$..quantileCIs$value,
quantiles = function() private$..quantiles$value,
compareClassifiers = function() private$..compareClassifiers$value,
calculateIDI = function() private$..calculateIDI$value,
calculateNRI = function() private$..calculateNRI$value,
refVar = function() private$..refVar$value,
nriThresholds = function() private$..nriThresholds$value,
idiNriBootRuns = function() private$..idiNriBootRuns$value),
private = list(
..dependentVars = NA,
..classVar = NA,
..positiveClass = NA,
..subGroup = NA,
..method = NA,
..metric = NA,
..direction = NA,
..specifyCutScore = NA,
..tol_metric = NA,
..break_ties = NA,
..allObserved = NA,
..boot_runs = NA,
..usePriorPrev = NA,
..priorPrev = NA,
..costratioFP = NA,
..sensSpecTable = NA,
..showThresholdTable = NA,
..maxThresholds = NA,
..delongTest = NA,
..plotROC = NA,
..combinePlots = NA,
..cleanPlot = NA,
..showOptimalPoint = NA,
..displaySE = NA,
..smoothing = NA,
..showConfidenceBands = NA,
..legendPosition = NA,
..directLabel = NA,
..interactiveROC = NA,
..showCriterionPlot = NA,
..showPrevalencePlot = NA,
..showDotPlot = NA,
..precisionRecallCurve = NA,
..partialAUC = NA,
..partialAUCfrom = NA,
..partialAUCto = NA,
..rocSmoothingMethod = NA,
..bootstrapCI = NA,
..bootstrapReps = NA,
..quantileCIs = NA,
..quantiles = NA,
..compareClassifiers = NA,
..calculateIDI = NA,
..calculateNRI = NA,
..refVar = NA,
..nriThresholds = NA,
..idiNriBootRuns = NA)
)
psychopdarocResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"psychopdarocResults",
inherit = jmvcore::Group,
active = list(
instructions = function() private$.items[["instructions"]],
procedureNotes = function() private$.items[["procedureNotes"]],
simpleResultsTable = function() private$.items[["simpleResultsTable"]],
resultsTable = function() private$.items[["resultsTable"]],
sensSpecTable = function() private$.items[["sensSpecTable"]],
thresholdTable = function() private$.items[["thresholdTable"]],
aucSummaryTable = function() private$.items[["aucSummaryTable"]],
delongComparisonTable = function() private$.items[["delongComparisonTable"]],
delongTest = function() private$.items[["delongTest"]],
plotROC = function() private$.items[["plotROC"]],
interactivePlot = function() private$.items[["interactivePlot"]],
criterionPlot = function() private$.items[["criterionPlot"]],
prevalencePlot = function() private$.items[["prevalencePlot"]],
dotPlot = function() private$.items[["dotPlot"]],
dotPlotMessage = function() private$.items[["dotPlotMessage"]],
precisionRecallPlot = function() private$.items[["precisionRecallPlot"]],
idiTable = function() private$.items[["idiTable"]],
nriTable = function() private$.items[["nriTable"]],
partialAUCTable = function() private$.items[["partialAUCTable"]],
bootstrapCITable = function() private$.items[["bootstrapCITable"]],
rocComparisonTable = function() private$.items[["rocComparisonTable"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="ROC Analysis",
refs=list(
"cutpointr",
"pROC",
"MASS",
"plotROC"))
self$add(jmvcore::Html$new(
options=options,
name="instructions",
visible=TRUE))
self$add(jmvcore::Html$new(
options=options,
name="procedureNotes",
visible=TRUE))
self$add(jmvcore::Table$new(
options=options,
name="simpleResultsTable",
title="ROC Analysis Summary",
visible=TRUE,
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="auc",
`title`="AUC",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Array$new(
options=options,
name="resultsTable",
title="Optimal Cutpoints and Performance",
visible=TRUE,
refs=list(
"cutpointr"),
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"method",
"metric",
"direction",
"allObserved",
"specifyCutScore",
"boot_runs",
"break_ties",
"tol_metric"),
template=jmvcore::Table$new(
options=options,
rows=0,
columns=list(
list(
`name`="cutpoint",
`title`="Cutpoint",
`type`="number",
`format`="zto"),
list(
`name`="sensitivity",
`title`="Sensitivity",
`type`="number",
`format`="pc"),
list(
`name`="specificity",
`title`="Specificity",
`type`="number",
`format`="pc"),
list(
`name`="ppv",
`title`="PPV",
`type`="number",
`format`="pc"),
list(
`name`="npv",
`title`="NPV",
`type`="number",
`format`="pc"),
list(
`name`="youden",
`title`="Youden's J",
`type`="number",
`format`="zto"),
list(
`name`="AUC",
`title`="AUC",
`type`="number",
`format`="zto"),
list(
`name`="metricValue",
`title`="Metric Score",
`type`="number",
`format`="zto")))))
self$add(jmvcore::Array$new(
options=options,
name="sensSpecTable",
title="Confusion Matrices",
visible="(sensSpecTable)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"method",
"allObserved",
"specifyCutScore"),
template=jmvcore::Html$new(
options=options)))
self$add(jmvcore::Table$new(
options=options,
name="thresholdTable",
title="Detailed Threshold Performance",
visible="(showThresholdTable)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"direction"),
columns=list(
list(
`name`="threshold",
`title`="Cut-off",
`type`="number",
`format`="zto"),
list(
`name`="sensitivity",
`title`="Sensitivity",
`type`="number",
`format`="pc"),
list(
`name`="specificity",
`title`="Specificity",
`type`="number",
`format`="pc"),
list(
`name`="accuracy",
`title`="Accuracy",
`type`="number",
`format`="pc"),
list(
`name`="ppv",
`title`="PPV",
`type`="number",
`format`="pc"),
list(
`name`="npv",
`title`="NPV",
`type`="number",
`format`="pc"),
list(
`name`="plr",
`title`="+LR",
`type`="number",
`format`="zto"),
list(
`name`="nlr",
`title`="-LR",
`type`="number",
`format`="zto"),
list(
`name`="youden",
`title`="Youden's J",
`type`="number",
`format`="zto"))))
self$add(jmvcore::Table$new(
options=options,
name="aucSummaryTable",
title="Area Under the ROC Curve",
visible=TRUE,
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="auc",
`title`="AUC",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="delongComparisonTable",
title="DeLong Test Pairwise Comparisons",
visible="(delongTest)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass"),
columns=list(
list(
`name`="comparison",
`title`="Comparison",
`type`="text"),
list(
`name`="auc_diff",
`title`="AUC Difference",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="z",
`title`="Z-statistic",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Preformatted$new(
options=options,
name="delongTest",
title="DeLong Test Details",
visible="(delongTest)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass"),
refs=list(
"DeLong1988")))
self$add(jmvcore::Array$new(
options=options,
name="plotROC",
title="ROC Curves",
visible="(plotROC)",
template=jmvcore::Image$new(
options=options,
width=550,
height=450,
renderFun=".plotROC",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"direction",
"smoothing",
"showOptimalPoint",
"cleanPlot",
"showConfidenceBands",
"quantileCIs"))))
self$add(jmvcore::Image$new(
options=options,
name="interactivePlot",
title="Interactive ROC Plot",
width=650,
height=500,
renderFun=".plotInteractiveROC",
visible="(interactiveROC)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup"),
refs=list(
"plotROC")))
self$add(jmvcore::Array$new(
options=options,
name="criterionPlot",
title="Sensitivity/Specificity vs. Threshold",
visible="(showCriterionPlot)",
template=jmvcore::Image$new(
options=options,
title="$key",
width=600,
height=400,
renderFun=".plotCriterion",
requiresData=TRUE,
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"method",
"metric",
"direction"))))
self$add(jmvcore::Array$new(
options=options,
name="prevalencePlot",
title="Predictive Values vs. Prevalence",
visible="(showPrevalencePlot)",
template=jmvcore::Image$new(
options=options,
title="$key",
width=600,
height=400,
renderFun=".plotPrevalence",
requiresData=TRUE,
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"usePriorPrev",
"priorPrev"))))
self$add(jmvcore::Array$new(
options=options,
name="dotPlot",
title="Test Values Distribution",
visible="(showDotPlot)",
template=jmvcore::Image$new(
options=options,
title="$key",
width=600,
height=400,
renderFun=".plotDot",
requiresData=TRUE,
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup",
"direction"))))
self$add(jmvcore::Html$new(
options=options,
name="dotPlotMessage",
title="Dot Plot Note",
visible="(showDotPlot && combinePlots)"))
self$add(jmvcore::Array$new(
options=options,
name="precisionRecallPlot",
title="Precision-Recall Curves",
visible="(precisionRecallCurve)",
template=jmvcore::Image$new(
options=options,
width=550,
height=450,
renderFun=".plotPrecisionRecall",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"subGroup"))))
self$add(jmvcore::Table$new(
options=options,
name="idiTable",
title="Integrated Discrimination Improvement (IDI)",
visible="(calculateIDI)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"refVar",
"idiNriBootRuns",
"direction"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="refVar",
`title`="Reference",
`type`="text"),
list(
`name`="idi",
`title`="IDI",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue")),
refs=list(
"Pencina2008")))
self$add(jmvcore::Table$new(
options=options,
name="nriTable",
title="Net Reclassification Index (NRI)",
visible="(calculateNRI)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"refVar",
"nriThresholds",
"idiNriBootRuns",
"direction"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="refVar",
`title`="Reference",
`type`="text"),
list(
`name`="nri",
`title`="NRI",
`type`="number",
`format`="zto"),
list(
`name`="event_nri",
`title`="Event NRI",
`type`="number",
`format`="zto"),
list(
`name`="non_event_nri",
`title`="Non-Event NRI",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue")),
refs=list(
"Pencina2008",
"Pencina2011")))
self$add(jmvcore::Table$new(
options=options,
name="partialAUCTable",
title="Partial AUC Results",
visible="(partialAUC)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"partialAUCfrom",
"partialAUCto"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="pAUC",
`title`="Partial AUC",
`type`="number",
`format`="zto"),
list(
`name`="pAUC_normalized",
`title`="Normalized pAUC",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"),
list(
`name`="spec_range",
`title`="Specificity Range",
`type`="text")),
refs=list(
"McClish1989")))
self$add(jmvcore::Table$new(
options=options,
name="bootstrapCITable",
title="Bootstrap Confidence Intervals",
visible="(bootstrapCI)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass",
"bootstrapReps"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="parameter",
`title`="Parameter",
`type`="text"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number",
`format`="zto"),
list(
`name`="ci_lower",
`title`="95% CI Lower",
`type`="number",
`format`="zto"),
list(
`name`="ci_upper",
`title`="95% CI Upper",
`type`="number",
`format`="zto"))))
self$add(jmvcore::Table$new(
options=options,
name="rocComparisonTable",
title="Classifier Performance Comparison",
visible="(compareClassifiers)",
clearWith=list(
"dependentVars",
"classVar",
"positiveClass"),
columns=list(
list(
`name`="variable",
`title`="Variable",
`type`="text"),
list(
`name`="auc",
`title`="AUC",
`type`="number",
`format`="zto"),
list(
`name`="auprc",
`title`="AUPRC",
`type`="number",
`format`="zto"),
list(
`name`="brier",
`title`="Brier Score",
`type`="number",
`format`="zto"),
list(
`name`="f1_score",
`title`="F1 Score",
`type`="number",
`format`="zto"),
list(
`name`="accuracy",
`title`="Accuracy",
`type`="number",
`format`="pc"),
list(
`name`="balanced_accuracy",
`title`="Balanced Accuracy",
`type`="number",
`format`="pc"))))}))
psychopdarocBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"psychopdarocBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "ClinicoPath",
name = "psychopdaroc",
version = c(0,0,3),
options = options,
results = psychopdarocResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' ROC Analysis
#'
#' Receiver Operating Characteristic (ROC) curve analysis with optimal
#' cutpoint determination.
#'
#' @param data The data as a data frame.
#' @param dependentVars Test variable(s) to be evaluated for classification
#' performance. Multiple variables can be selected for comparison.
#' @param classVar Binary classification variable representing the true class
#' (gold standard). Must have exactly two levels.
#' @param positiveClass Specifies which level of the class variable should be
#' treated as the positive class.
#' @param subGroup Optional grouping variable for stratified analysis. ROC
#' curves will be calculated separately for each group.
#' @param method Method for determining the optimal cutpoint. Different
#' methods optimize different aspects of classifier performance.
#' @param metric Metric to optimize when determining the cutpoint. Only
#' applies to maximize/minimize methods.
#' @param direction Direction of classification relative to the cutpoint. Use
#' '>=' when higher test values indicate the positive class.
#' @param specifyCutScore Specific cutpoint value to use when method is set to
#' 'Manual cutpoint'.
#' @param tol_metric Tolerance for the metric value when multiple cutpoints
#' yield similar performance. Cutpoints within this tolerance are considered
#' equivalent.
#' @param break_ties Method for handling ties when multiple cutpoints achieve
#' the same metric value.
#' @param allObserved Display performance metrics for all observed test values
#' as potential cutpoints, not just the optimal cutpoint.
#' @param boot_runs Number of bootstrap iterations for methods using
#' bootstrapping. Set to 0 to disable bootstrapping.
#' @param usePriorPrev Use a specified prior prevalence instead of the sample
#' prevalence for calculating predictive values.
#' @param priorPrev Population prevalence to use for predictive value
#' calculations. Only used when 'Use Prior Prevalence' is checked.
#' @param costratioFP Relative cost of false positives compared to false
#' negatives. Values > 1 penalize false positives more heavily.
#' @param sensSpecTable Display detailed confusion matrices at optimal
#' cutpoints.
#' @param showThresholdTable Display detailed table with performance metrics
#' at multiple thresholds.
#' @param maxThresholds Maximum number of threshold values to show in the
#' threshold table.
#' @param delongTest Perform DeLong's test for comparing AUCs between multiple
#' test variables. Requires at least two test variables.
#' @param plotROC Display ROC curves for visual assessment of classifier
#' performance.
#' @param combinePlots When multiple test variables are selected, combine all
#' ROC curves in a single plot.
#' @param cleanPlot Create clean ROC curves without annotations, suitable for
#' publications.
#' @param showOptimalPoint Display the optimal cutpoint on the ROC curve.
#' @param displaySE Display standard error bands on ROC curves (when LOESS
#' smoothing is applied).
#' @param smoothing Apply LOESS smoothing to ROC curves for visualization.
#' @param showConfidenceBands Display confidence bands around the ROC curve.
#' @param legendPosition Position of the legend in plots with multiple ROC
#' curves.
#' @param directLabel Label curves directly on the plot instead of using a
#' legend.
#' @param interactiveROC Create an interactive HTML ROC plot (requires plotROC
#' package).
#' @param showCriterionPlot Plot showing how sensitivity and specificity
#' change across different thresholds.
#' @param showPrevalencePlot Plot showing how PPV and NPV change with disease
#' prevalence.
#' @param showDotPlot Dot plot showing the distribution of test values by
#' class.
#' @param precisionRecallCurve Display precision-recall curves alongside ROC
#' curves.
#' @param partialAUC Calculate AUC for a specific region of the ROC curve.
#' @param partialAUCfrom Lower bound of specificity range for partial AUC
#' calculation.
#' @param partialAUCto Upper bound of specificity range for partial AUC
#' calculation.
#' @param rocSmoothingMethod Method for smoothing the ROC curve (requires pROC
#' package).
#' @param bootstrapCI Calculate bootstrap confidence intervals for AUC and
#' optimal cutpoints.
#' @param bootstrapReps Number of bootstrap replications for confidence
#' interval calculation.
#' @param quantileCIs Display confidence intervals at specific quantiles of
#' the test variable.
#' @param quantiles Comma-separated list of quantiles (0-1) at which to
#' display confidence intervals.
#' @param compareClassifiers Perform comprehensive comparison of classifier
#' performance metrics.
#' @param calculateIDI Calculate Integrated Discrimination Improvement for
#' model comparison.
#' @param calculateNRI Calculate Net Reclassification Index for model
#' comparison.
#' @param refVar Reference test variable for IDI and NRI calculations. Other
#' variables will be compared against this reference.
#' @param nriThresholds Comma-separated probability thresholds (0-1) defining
#' risk categories for NRI. Leave empty for continuous NRI.
#' @param idiNriBootRuns Number of bootstrap iterations for IDI and NRI
#' confidence intervals.
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$instructions} \tab \tab \tab \tab \tab a html \cr
#' \code{results$procedureNotes} \tab \tab \tab \tab \tab a html \cr
#' \code{results$simpleResultsTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$resultsTable} \tab \tab \tab \tab \tab an array of tables \cr
#' \code{results$sensSpecTable} \tab \tab \tab \tab \tab an array of htmls \cr
#' \code{results$thresholdTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$aucSummaryTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$delongComparisonTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$delongTest} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$plotROC} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$interactivePlot} \tab \tab \tab \tab \tab an image \cr
#' \code{results$criterionPlot} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$prevalencePlot} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$dotPlot} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$dotPlotMessage} \tab \tab \tab \tab \tab a html \cr
#' \code{results$precisionRecallPlot} \tab \tab \tab \tab \tab an array of images \cr
#' \code{results$idiTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$nriTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$partialAUCTable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$bootstrapCITable} \tab \tab \tab \tab \tab a table \cr
#' \code{results$rocComparisonTable} \tab \tab \tab \tab \tab a table \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$simpleResultsTable$asDF}
#'
#' \code{as.data.frame(results$simpleResultsTable)}
#'
#' @export
psychopdaroc <- function(
data,
dependentVars,
classVar,
positiveClass,
subGroup,
method = "maximize_metric",
metric = "youden",
direction = ">=",
specifyCutScore = "",
tol_metric = 0.05,
break_ties = "mean",
allObserved = FALSE,
boot_runs = 0,
usePriorPrev = FALSE,
priorPrev = 0.5,
costratioFP = 1,
sensSpecTable = FALSE,
showThresholdTable = FALSE,
maxThresholds = 20,
delongTest = FALSE,
plotROC = TRUE,
combinePlots = TRUE,
cleanPlot = FALSE,
showOptimalPoint = TRUE,
displaySE = FALSE,
smoothing = FALSE,
showConfidenceBands = FALSE,
legendPosition = "right",
directLabel = FALSE,
interactiveROC = FALSE,
showCriterionPlot = FALSE,
showPrevalencePlot = FALSE,
showDotPlot = FALSE,
precisionRecallCurve = FALSE,
partialAUC = FALSE,
partialAUCfrom = 0.8,
partialAUCto = 1,
rocSmoothingMethod = "none",
bootstrapCI = FALSE,
bootstrapReps = 2000,
quantileCIs = FALSE,
quantiles = "0.1,0.25,0.5,0.75,0.9",
compareClassifiers = FALSE,
calculateIDI = FALSE,
calculateNRI = FALSE,
refVar,
nriThresholds = "",
idiNriBootRuns = 1000) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("psychopdaroc requires jmvcore to be installed (restart may be required)")
if ( ! missing(dependentVars)) dependentVars <- jmvcore::resolveQuo(jmvcore::enquo(dependentVars))
if ( ! missing(classVar)) classVar <- jmvcore::resolveQuo(jmvcore::enquo(classVar))
if ( ! missing(subGroup)) subGroup <- jmvcore::resolveQuo(jmvcore::enquo(subGroup))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(dependentVars), dependentVars, NULL),
`if`( ! missing(classVar), classVar, NULL),
`if`( ! missing(subGroup), subGroup, NULL))
for (v in classVar) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in subGroup) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- psychopdarocOptions$new(
dependentVars = dependentVars,
classVar = classVar,
positiveClass = positiveClass,
subGroup = subGroup,
method = method,
metric = metric,
direction = direction,
specifyCutScore = specifyCutScore,
tol_metric = tol_metric,
break_ties = break_ties,
allObserved = allObserved,
boot_runs = boot_runs,
usePriorPrev = usePriorPrev,
priorPrev = priorPrev,
costratioFP = costratioFP,
sensSpecTable = sensSpecTable,
showThresholdTable = showThresholdTable,
maxThresholds = maxThresholds,
delongTest = delongTest,
plotROC = plotROC,
combinePlots = combinePlots,
cleanPlot = cleanPlot,
showOptimalPoint = showOptimalPoint,
displaySE = displaySE,
smoothing = smoothing,
showConfidenceBands = showConfidenceBands,
legendPosition = legendPosition,
directLabel = directLabel,
interactiveROC = interactiveROC,
showCriterionPlot = showCriterionPlot,
showPrevalencePlot = showPrevalencePlot,
showDotPlot = showDotPlot,
precisionRecallCurve = precisionRecallCurve,
partialAUC = partialAUC,
partialAUCfrom = partialAUCfrom,
partialAUCto = partialAUCto,
rocSmoothingMethod = rocSmoothingMethod,
bootstrapCI = bootstrapCI,
bootstrapReps = bootstrapReps,
quantileCIs = quantileCIs,
quantiles = quantiles,
compareClassifiers = compareClassifiers,
calculateIDI = calculateIDI,
calculateNRI = calculateNRI,
refVar = refVar,
nriThresholds = nriThresholds,
idiNriBootRuns = idiNriBootRuns)
analysis <- psychopdarocClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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