R/roc.h.R
In sbalci/ClinicoPathJamoviModule: Analysis for Clinicopathological Research
Defines functions
roc
Documented in
roc
# This file is automatically generated, you probably don't want to edit this
rocOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"rocOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
classvar = NULL,
classpos = NULL,
testvar = NULL,
direction = "greatpos",
ci = TRUE,
cimethod = "delong",
youden = TRUE,
optimcrit = TRUE,
pp = FALSE,
pprob = 0.5,
costratioFP = 1,
coords = TRUE,
plotroc = TRUE,
plotci = FALSE,
plotbars = FALSE,
plotprev = FALSE,
plotidr = TRUE, ...) {
super$initialize(
package="ClinicoPath",
name="roc",
requiresData=TRUE,
...)
private$..classvar <- jmvcore::OptionVariable$new(
"classvar",
classvar,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..classpos <- jmvcore::OptionLevel$new(
"classpos",
classpos,
variable="(classvar)")
private$..testvar <- jmvcore::OptionVariable$new(
"testvar",
testvar,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..direction <- jmvcore::OptionList$new(
"direction",
direction,
options=list(
"greatpos",
"lesspos"),
default="greatpos")
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=TRUE)
private$..cimethod <- jmvcore::OptionList$new(
"cimethod",
cimethod,
options=list(
"delong",
"hanley",
"binomial"),
default="delong")
private$..youden <- jmvcore::OptionBool$new(
"youden",
youden,
default=TRUE)
private$..optimcrit <- jmvcore::OptionBool$new(
"optimcrit",
optimcrit,
default=TRUE)
private$..pp <- jmvcore::OptionBool$new(
"pp",
pp,
default=FALSE)
private$..pprob <- jmvcore::OptionNumber$new(
"pprob",
pprob,
default=0.5,
min=0.001,
max=0.999)
private$..costratioFP <- jmvcore::OptionNumber$new(
"costratioFP",
costratioFP,
default=1,
min=0,
max=1000)
private$..coords <- jmvcore::OptionBool$new(
"coords",
coords,
default=TRUE)
private$..plotroc <- jmvcore::OptionBool$new(
"plotroc",
plotroc,
default=TRUE)
private$..plotci <- jmvcore::OptionBool$new(
"plotci",
plotci,
default=FALSE)
private$..plotbars <- jmvcore::OptionBool$new(
"plotbars",
plotbars,
default=FALSE)
private$..plotprev <- jmvcore::OptionBool$new(
"plotprev",
plotprev,
default=FALSE)
private$..plotidr <- jmvcore::OptionBool$new(
"plotidr",
plotidr,
default=TRUE)
self$.addOption(private$..classvar)
self$.addOption(private$..classpos)
self$.addOption(private$..testvar)
self$.addOption(private$..direction)
self$.addOption(private$..ci)
self$.addOption(private$..cimethod)
self$.addOption(private$..youden)
self$.addOption(private$..optimcrit)
self$.addOption(private$..pp)
self$.addOption(private$..pprob)
self$.addOption(private$..costratioFP)
self$.addOption(private$..coords)
self$.addOption(private$..plotroc)
self$.addOption(private$..plotci)
self$.addOption(private$..plotbars)
self$.addOption(private$..plotprev)
self$.addOption(private$..plotidr)
}),
active = list(
classvar = function() private$..classvar$value,
classpos = function() private$..classpos$value,
testvar = function() private$..testvar$value,
direction = function() private$..direction$value,
ci = function() private$..ci$value,
cimethod = function() private$..cimethod$value,
youden = function() private$..youden$value,
optimcrit = function() private$..optimcrit$value,
pp = function() private$..pp$value,
pprob = function() private$..pprob$value,
costratioFP = function() private$..costratioFP$value,
coords = function() private$..coords$value,
plotroc = function() private$..plotroc$value,
plotci = function() private$..plotci$value,
plotbars = function() private$..plotbars$value,
plotprev = function() private$..plotprev$value,
plotidr = function() private$..plotidr$value),
private = list(
..classvar = NA,
..classpos = NA,
..testvar = NA,
..direction = NA,
..ci = NA,
..cimethod = NA,
..youden = NA,
..optimcrit = NA,
..pp = NA,
..pprob = NA,
..costratioFP = NA,
..coords = NA,
..plotroc = NA,
..plotci = NA,
..plotbars = NA,
..plotprev = NA,
..plotidr = NA)
)
rocResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"rocResults",
inherit = jmvcore::Group,
active = list(
summary = function() private$.items[["summary"]],
optimal = function() private$.items[["optimal"]],
coords = function() private$.items[["coords"]],
plotroc = function() private$.items[["plotroc"]],
plotbars = function() private$.items[["plotbars"]],
plotprev = function() private$.items[["plotprev"]],
plotidr = function() private$.items[["plotidr"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="ROC Curve Analysis",
refs=list(
"pROC",
"ROCR",
"cutpointr",
"DiagnosticTests",
"sensspecwiki"))
self$add(jmvcore::Table$new(
options=options,
name="summary",
title="ROC Analysis Summary",
rows=1,
swapRowsColumns=TRUE,
columns=list(
list(
`name`="nobs",
`title`="Number of observations",
`type`="integer"),
list(
`name`="npos",
`title`="Positive class",
`type`="integer"),
list(
`name`="nneg",
`title`="Negative class",
`type`="integer"),
list(
`name`="auc",
`title`="Area Under Curve (AUC)",
`type`="number",
`format`="zto"),
list(
`name`="auc_se",
`title`="Standard Error",
`type`="number",
`format`="zto"),
list(
`name`="auc_lci",
`title`="Lower 95% CI",
`visible`="(ci)",
`type`="number",
`format`="zto"),
list(
`name`="auc_uci",
`title`="Upper 95% CI",
`visible`="(ci)",
`type`="number",
`format`="zto"),
list(
`name`="z",
`title`="z statistic",
`type`="number"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="optimal",
title="Optimal Criterion Values",
visible="(optimcrit)",
rows=0,
columns=list(
list(
`name`="type",
`title`="Criterion",
`type`="text"),
list(
`name`="threshold",
`title`="Value",
`type`="number"),
list(
`name`="sens",
`title`="Sensitivity",
`type`="number",
`format`="pc"),
list(
`name`="spec",
`title`="Specificity",
`type`="number",
`format`="pc"),
list(
`name`="ppv",
`title`="PPV",
`type`="number",
`format`="pc"),
list(
`name`="npv",
`title`="NPV",
`type`="number",
`format`="pc"),
list(
`name`="j",
`title`="Youden Index",
`type`="number",
`format`="zto",
`visible`="(youden)"))))
self$add(jmvcore::Table$new(
options=options,
name="coords",
title="Coordinates of the ROC Curve",
visible="(coords)",
rows=0,
columns=list(
list(
`name`="threshold",
`title`="Criterion (Threshold)",
`type`="number"),
list(
`name`="sens",
`title`="Sensitivity",
`type`="number",
`format`="pc"),
list(
`name`="spec",
`title`="Specificity",
`type`="number",
`format`="pc"),
list(
`name`="ppv",
`title`="PPV",
`type`="number",
`format`="pc"),
list(
`name`="npv",
`title`="NPV",
`type`="number",
`format`="pc"),
list(
`name`="lrp",
`title`="LR+",
`type`="number"),
list(
`name`="lrn",
`title`="LR-",
`type`="number"))))
self$add(jmvcore::Image$new(
options=options,
name="plotroc",
title="ROC Curve",
width=500,
height=500,
renderFun=".plotRoc",
visible="(plotroc)",
requiresData=TRUE))
self$add(jmvcore::Image$new(
options=options,
name="plotbars",
title="Sensitivity/Specificity vs. Criterion Values",
width=500,
height=500,
renderFun=".plotBars",
visible="(plotbars)",
requiresData=TRUE))
self$add(jmvcore::Image$new(
options=options,
name="plotprev",
title="Predictive Values vs. Disease Prevalence",
width=500,
height=500,
renderFun=".plotPrev",
visible="(plotprev)",
requiresData=TRUE))
self$add(jmvcore::Image$new(
options=options,
name="plotidr",
title="Interactive Dot Diagram",
width=500,
height=500,
renderFun=".plotIDR",
visible="(plotidr)",
requiresData=TRUE))}))
rocBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"rocBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "ClinicoPath",
name = "roc",
version = c(0,0,3),
options = options,
results = rocResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' ROC Curve Analysis
#'
#' Function for ROC (Receiver Operating Characteristic) Curve Analysis.
#' Calculates sensitivity, specificity, AUC, Youden index, and more.
#'
#'
#' @examples
#' \donttest{
#' # example will be added
#'}
#' @param data The data as a data frame.
#' @param classvar A string naming the variable from \code{data} that contains
#' the actual status (gold standard / diagnosis), must be a factor with two
#' levels.
#' @param classpos The level of the classification variable that represents
#' the positive status.
#' @param testvar A string naming the variable from \code{data} that contains
#' the test result or measurement, must be numeric.
#' @param direction Specify whether greater values of the test variable
#' indicate a positive test (default) or if lower values indicate a positive
#' test. Note: AUC will always be greater than 0.5, even if it means inverting
#' the predictor internally.
#' @param ci Boolean selection whether to show 95\% confidence intervals.
#' Default is 'true'.
#' @param cimethod Method for calculating confidence intervals for the AUC.
#' Default is 'delong'.
#' @param youden Boolean selection whether to calculate Youden index (J =
#' sensitivity + specificity - 1). Default is 'true'.
#' @param optimcrit Boolean selection whether to calculate the optimal
#' criterion value. Default is 'true'.
#' @param pp Boolean selection whether to use a specified disease prevalence
#' for predictive values. Default is 'false'.
#' @param pprob Prior probability (disease prevalence in the population).
#' Requires a value between 0.001 and 0.999, default 0.500.
#' @param costratioFP Cost ratio of false positive to false negative
#' decisions. Default is 1.000 (equal costs).
#' @param coords Boolean selection whether to show the full list of
#' coordinates of the ROC curve. This includes all possible thresholds with
#' sensitivity, specificity, predictive values, and likelihood ratios.
#' Default is 'true'.
#' @param plotroc Boolean selection whether to create a ROC curve plot.
#' Default is 'true'.
#' @param plotci Boolean selection whether to show 95\% confidence bands on
#' the ROC curve. Default is 'false'.
#' @param plotbars Boolean selection whether to create a plot of sensitivity
#' and specificity versus criterion values. Default is 'false'.
#' @param plotprev Boolean selection whether to create a plot of positive and
#' negative predictive values versus disease prevalence. Default is 'false'.
#' @param plotidr Boolean selection whether to create an interactive dot
#' diagram Default is 'true'.
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$summary} \tab \tab \tab \tab \tab a table \cr
#' \code{results$optimal} \tab \tab \tab \tab \tab a table \cr
#' \code{results$coords} \tab \tab \tab \tab \tab a table \cr
#' \code{results$plotroc} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plotbars} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plotprev} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plotidr} \tab \tab \tab \tab \tab an image \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$summary$asDF}
#'
#' \code{as.data.frame(results$summary)}
#'
#' @export
roc <- function(
data,
classvar,
classpos,
testvar,
direction = "greatpos",
ci = TRUE,
cimethod = "delong",
youden = TRUE,
optimcrit = TRUE,
pp = FALSE,
pprob = 0.5,
costratioFP = 1,
coords = TRUE,
plotroc = TRUE,
plotci = FALSE,
plotbars = FALSE,
plotprev = FALSE,
plotidr = TRUE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("roc requires jmvcore to be installed (restart may be required)")
if ( ! missing(classvar)) classvar <- jmvcore::resolveQuo(jmvcore::enquo(classvar))
if ( ! missing(testvar)) testvar <- jmvcore::resolveQuo(jmvcore::enquo(testvar))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(classvar), classvar, NULL),
`if`( ! missing(testvar), testvar, NULL))
for (v in classvar) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- rocOptions$new(
classvar = classvar,
classpos = classpos,
testvar = testvar,
direction = direction,
ci = ci,
cimethod = cimethod,
youden = youden,
optimcrit = optimcrit,
pp = pp,
pprob = pprob,
costratioFP = costratioFP,
coords = coords,
plotroc = plotroc,
plotci = plotci,
plotbars = plotbars,
plotprev = plotprev,
plotidr = plotidr)
analysis <- rocClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
sbalci/ClinicoPathJamoviModule documentation built on June 13, 2025, 9:34 a.m.
R Package Documentation
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Defines functions roc
Documented in roc
# This file is automatically generated, you probably don't want to edit this
rocOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"rocOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
classvar = NULL,
classpos = NULL,
testvar = NULL,
direction = "greatpos",
ci = TRUE,
cimethod = "delong",
youden = TRUE,
optimcrit = TRUE,
pp = FALSE,
pprob = 0.5,
costratioFP = 1,
coords = TRUE,
plotroc = TRUE,
plotci = FALSE,
plotbars = FALSE,
plotprev = FALSE,
plotidr = TRUE, ...) {
super$initialize(
package="ClinicoPath",
name="roc",
requiresData=TRUE,
...)
private$..classvar <- jmvcore::OptionVariable$new(
"classvar",
classvar,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..classpos <- jmvcore::OptionLevel$new(
"classpos",
classpos,
variable="(classvar)")
private$..testvar <- jmvcore::OptionVariable$new(
"testvar",
testvar,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..direction <- jmvcore::OptionList$new(
"direction",
direction,
options=list(
"greatpos",
"lesspos"),
default="greatpos")
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=TRUE)
private$..cimethod <- jmvcore::OptionList$new(
"cimethod",
cimethod,
options=list(
"delong",
"hanley",
"binomial"),
default="delong")
private$..youden <- jmvcore::OptionBool$new(
"youden",
youden,
default=TRUE)
private$..optimcrit <- jmvcore::OptionBool$new(
"optimcrit",
optimcrit,
default=TRUE)
private$..pp <- jmvcore::OptionBool$new(
"pp",
pp,
default=FALSE)
private$..pprob <- jmvcore::OptionNumber$new(
"pprob",
pprob,
default=0.5,
min=0.001,
max=0.999)
private$..costratioFP <- jmvcore::OptionNumber$new(
"costratioFP",
costratioFP,
default=1,
min=0,
max=1000)
private$..coords <- jmvcore::OptionBool$new(
"coords",
coords,
default=TRUE)
private$..plotroc <- jmvcore::OptionBool$new(
"plotroc",
plotroc,
default=TRUE)
private$..plotci <- jmvcore::OptionBool$new(
"plotci",
plotci,
default=FALSE)
private$..plotbars <- jmvcore::OptionBool$new(
"plotbars",
plotbars,
default=FALSE)
private$..plotprev <- jmvcore::OptionBool$new(
"plotprev",
plotprev,
default=FALSE)
private$..plotidr <- jmvcore::OptionBool$new(
"plotidr",
plotidr,
default=TRUE)
self$.addOption(private$..classvar)
self$.addOption(private$..classpos)
self$.addOption(private$..testvar)
self$.addOption(private$..direction)
self$.addOption(private$..ci)
self$.addOption(private$..cimethod)
self$.addOption(private$..youden)
self$.addOption(private$..optimcrit)
self$.addOption(private$..pp)
self$.addOption(private$..pprob)
self$.addOption(private$..costratioFP)
self$.addOption(private$..coords)
self$.addOption(private$..plotroc)
self$.addOption(private$..plotci)
self$.addOption(private$..plotbars)
self$.addOption(private$..plotprev)
self$.addOption(private$..plotidr)
}),
active = list(
classvar = function() private$..classvar$value,
classpos = function() private$..classpos$value,
testvar = function() private$..testvar$value,
direction = function() private$..direction$value,
ci = function() private$..ci$value,
cimethod = function() private$..cimethod$value,
youden = function() private$..youden$value,
optimcrit = function() private$..optimcrit$value,
pp = function() private$..pp$value,
pprob = function() private$..pprob$value,
costratioFP = function() private$..costratioFP$value,
coords = function() private$..coords$value,
plotroc = function() private$..plotroc$value,
plotci = function() private$..plotci$value,
plotbars = function() private$..plotbars$value,
plotprev = function() private$..plotprev$value,
plotidr = function() private$..plotidr$value),
private = list(
..classvar = NA,
..classpos = NA,
..testvar = NA,
..direction = NA,
..ci = NA,
..cimethod = NA,
..youden = NA,
..optimcrit = NA,
..pp = NA,
..pprob = NA,
..costratioFP = NA,
..coords = NA,
..plotroc = NA,
..plotci = NA,
..plotbars = NA,
..plotprev = NA,
..plotidr = NA)
)
rocResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"rocResults",
inherit = jmvcore::Group,
active = list(
summary = function() private$.items[["summary"]],
optimal = function() private$.items[["optimal"]],
coords = function() private$.items[["coords"]],
plotroc = function() private$.items[["plotroc"]],
plotbars = function() private$.items[["plotbars"]],
plotprev = function() private$.items[["plotprev"]],
plotidr = function() private$.items[["plotidr"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="ROC Curve Analysis",
refs=list(
"pROC",
"ROCR",
"cutpointr",
"DiagnosticTests",
"sensspecwiki"))
self$add(jmvcore::Table$new(
options=options,
name="summary",
title="ROC Analysis Summary",
rows=1,
swapRowsColumns=TRUE,
columns=list(
list(
`name`="nobs",
`title`="Number of observations",
`type`="integer"),
list(
`name`="npos",
`title`="Positive class",
`type`="integer"),
list(
`name`="nneg",
`title`="Negative class",
`type`="integer"),
list(
`name`="auc",
`title`="Area Under Curve (AUC)",
`type`="number",
`format`="zto"),
list(
`name`="auc_se",
`title`="Standard Error",
`type`="number",
`format`="zto"),
list(
`name`="auc_lci",
`title`="Lower 95% CI",
`visible`="(ci)",
`type`="number",
`format`="zto"),
list(
`name`="auc_uci",
`title`="Upper 95% CI",
`visible`="(ci)",
`type`="number",
`format`="zto"),
list(
`name`="z",
`title`="z statistic",
`type`="number"),
list(
`name`="p",
`title`="p-value",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="optimal",
title="Optimal Criterion Values",
visible="(optimcrit)",
rows=0,
columns=list(
list(
`name`="type",
`title`="Criterion",
`type`="text"),
list(
`name`="threshold",
`title`="Value",
`type`="number"),
list(
`name`="sens",
`title`="Sensitivity",
`type`="number",
`format`="pc"),
list(
`name`="spec",
`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`="j",
`title`="Youden Index",
`type`="number",
`format`="zto",
`visible`="(youden)"))))
self$add(jmvcore::Table$new(
options=options,
name="coords",
title="Coordinates of the ROC Curve",
visible="(coords)",
rows=0,
columns=list(
list(
`name`="threshold",
`title`="Criterion (Threshold)",
`type`="number"),
list(
`name`="sens",
`title`="Sensitivity",
`type`="number",
`format`="pc"),
list(
`name`="spec",
`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`="lrp",
`title`="LR+",
`type`="number"),
list(
`name`="lrn",
`title`="LR-",
`type`="number"))))
self$add(jmvcore::Image$new(
options=options,
name="plotroc",
title="ROC Curve",
width=500,
height=500,
renderFun=".plotRoc",
visible="(plotroc)",
requiresData=TRUE))
self$add(jmvcore::Image$new(
options=options,
name="plotbars",
title="Sensitivity/Specificity vs. Criterion Values",
width=500,
height=500,
renderFun=".plotBars",
visible="(plotbars)",
requiresData=TRUE))
self$add(jmvcore::Image$new(
options=options,
name="plotprev",
title="Predictive Values vs. Disease Prevalence",
width=500,
height=500,
renderFun=".plotPrev",
visible="(plotprev)",
requiresData=TRUE))
self$add(jmvcore::Image$new(
options=options,
name="plotidr",
title="Interactive Dot Diagram",
width=500,
height=500,
renderFun=".plotIDR",
visible="(plotidr)",
requiresData=TRUE))}))
rocBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"rocBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "ClinicoPath",
name = "roc",
version = c(0,0,3),
options = options,
results = rocResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' ROC Curve Analysis
#'
#' Function for ROC (Receiver Operating Characteristic) Curve Analysis.
#' Calculates sensitivity, specificity, AUC, Youden index, and more.
#'
#'
#' @examples
#' \donttest{
#' # example will be added
#'}
#' @param data The data as a data frame.
#' @param classvar A string naming the variable from \code{data} that contains
#' the actual status (gold standard / diagnosis), must be a factor with two
#' levels.
#' @param classpos The level of the classification variable that represents
#' the positive status.
#' @param testvar A string naming the variable from \code{data} that contains
#' the test result or measurement, must be numeric.
#' @param direction Specify whether greater values of the test variable
#' indicate a positive test (default) or if lower values indicate a positive
#' test. Note: AUC will always be greater than 0.5, even if it means inverting
#' the predictor internally.
#' @param ci Boolean selection whether to show 95\% confidence intervals.
#' Default is 'true'.
#' @param cimethod Method for calculating confidence intervals for the AUC.
#' Default is 'delong'.
#' @param youden Boolean selection whether to calculate Youden index (J =
#' sensitivity + specificity - 1). Default is 'true'.
#' @param optimcrit Boolean selection whether to calculate the optimal
#' criterion value. Default is 'true'.
#' @param pp Boolean selection whether to use a specified disease prevalence
#' for predictive values. Default is 'false'.
#' @param pprob Prior probability (disease prevalence in the population).
#' Requires a value between 0.001 and 0.999, default 0.500.
#' @param costratioFP Cost ratio of false positive to false negative
#' decisions. Default is 1.000 (equal costs).
#' @param coords Boolean selection whether to show the full list of
#' coordinates of the ROC curve. This includes all possible thresholds with
#' sensitivity, specificity, predictive values, and likelihood ratios.
#' Default is 'true'.
#' @param plotroc Boolean selection whether to create a ROC curve plot.
#' Default is 'true'.
#' @param plotci Boolean selection whether to show 95\% confidence bands on
#' the ROC curve. Default is 'false'.
#' @param plotbars Boolean selection whether to create a plot of sensitivity
#' and specificity versus criterion values. Default is 'false'.
#' @param plotprev Boolean selection whether to create a plot of positive and
#' negative predictive values versus disease prevalence. Default is 'false'.
#' @param plotidr Boolean selection whether to create an interactive dot
#' diagram Default is 'true'.
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$summary} \tab \tab \tab \tab \tab a table \cr
#' \code{results$optimal} \tab \tab \tab \tab \tab a table \cr
#' \code{results$coords} \tab \tab \tab \tab \tab a table \cr
#' \code{results$plotroc} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plotbars} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plotprev} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plotidr} \tab \tab \tab \tab \tab an image \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$summary$asDF}
#'
#' \code{as.data.frame(results$summary)}
#'
#' @export
roc <- function(
data,
classvar,
classpos,
testvar,
direction = "greatpos",
ci = TRUE,
cimethod = "delong",
youden = TRUE,
optimcrit = TRUE,
pp = FALSE,
pprob = 0.5,
costratioFP = 1,
coords = TRUE,
plotroc = TRUE,
plotci = FALSE,
plotbars = FALSE,
plotprev = FALSE,
plotidr = TRUE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("roc requires jmvcore to be installed (restart may be required)")
if ( ! missing(classvar)) classvar <- jmvcore::resolveQuo(jmvcore::enquo(classvar))
if ( ! missing(testvar)) testvar <- jmvcore::resolveQuo(jmvcore::enquo(testvar))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(classvar), classvar, NULL),
`if`( ! missing(testvar), testvar, NULL))
for (v in classvar) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- rocOptions$new(
classvar = classvar,
classpos = classpos,
testvar = testvar,
direction = direction,
ci = ci,
cimethod = cimethod,
youden = youden,
optimcrit = optimcrit,
pp = pp,
pprob = pprob,
costratioFP = costratioFP,
coords = coords,
plotroc = plotroc,
plotci = plotci,
plotbars = plotbars,
plotprev = plotprev,
plotidr = plotidr)
analysis <- rocClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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