R/St.R
In dpc10ster/rjafroc-master: Artificial Intelligence Systems and Observer Performance
Defines functions
Explanations
MyOutput
St
Documented in
St
#' @name St
#' @title DBM or OR significance testing for a one treatment factorial or
#' two-treatment crossed modality factorial dataset (not SPLIT_PLOT)
#'
#' @description Performs DBM or OR significance testing for the dataset.
#'
#' @param dataset The dataset to be analyzed, see \code{\link{RJafroc-package}}.
#' The dataset design can be "FCTRL" or "FCTRL-X-MOD".
#'
#' @param FOM The figure of merit, see \code{\link{UtilFigureOfMerit}}
#'
#' @param method The significance testing method to be used: \code{"DBM"} for
#' the Dorfman-Berbaum-Metz method or \code{"OR"} for the Obuchowski-Rockette
#' method (default).
#'
#' @param covEstMethod The covariance matrix estimation method in \code{ORH}
#' analysis (for \code{method = "DBM"} the jackknife is always used).
#' \itemize{
#' \item \code{"Jackknife"} (default),
#' \item \code{"Bootstrap"}, in which case \code{nBoots} is relevant, default
#' 200,
#' \item \code{"DeLong"}; requires \code{FOM = "Wilcoxon" or "ROI" or "HrAuc"}.
#' }
#'
#' @param analysisOption Determines which factors are regarded as random and
#' which are fixed:
#' \itemize{
#' \item \code{"RRRC"} = random-reader random case (default),
#' \item \code{"FRRC"} = fixed-reader random case,
#' \item \code{"RRFC"} = random-reader fixed case,
#' }
#'
#' @param alpha The significance level (alpha) of the test of the null
#' hypothesis that all modality effects are zero (default: alpha = 0.05).
#'
#' @param FPFValue Only needed for \code{LROC} data \strong{and} FOM = "PCL" or
#' "ALROC"; where to evaluate a partial curve based figure of merit. (default:
#' FPFValue = 0.2).
#'
#' @param nBoots The number of bootstraps (defaults to 200), only needed if
#' \code{covEstMethod = "bootstrap"} and \code{method = "OR"}
#'
#' @param seed For bootstraps the seed of the RNG (default: seed = \code{NULL}),
#' only needed if \code{method = "OR"} and \code{covEstMethod = "bootstrap"}.
#'
#' @param details Amount of explanations in output, default is 0 for no
#' explanations and 1 for explanations.
#'
#'
#' @return A list containing the results of the analysis.
#'
#' @examples
#' result <- St(dataset02,FOM = "Wilcoxon", method = "DBM")
#' result <- St(dataset02,FOM = "Wilcoxon", method = "OR")
#' result <- St(datasetX, FOM = "wAFROC", method = "OR", analysisOption = "RRRC")
#'
#' \donttest{
#' result <- St(dataset05, FOM = "wAFROC")
#' result <- St(dataset05, FOM = "HrAuc", method = "DBM")
#' }
#'
#' @note \code{details} = 0 should suffice for factorial dataset analysis since
#' the names of the output lists are self-explanatory. For cross-modality
#' analysis \code{details} = 1 is suggested to better understand the output.
#'
#' @references Dorfman DD, Berbaum KS, Metz CE (1992) ROC characteristic rating
#' analysis: Generalization to the Population of Readers and Patients with the
#' Jackknife method, Invest. Radiol. 27, 723-731.
#'
#' Obuchowski NA, Rockette HE (1995) Hypothesis Testing of the Diagnostic
#' Accuracy for Multiple Diagnostic Tests: An ANOVA Approach with Dependent
#' Observations, Communications in Statistics: Simulation and Computation 24,
#' 285-308.
#'
#' Hillis SL (2014) A marginal-mean ANOVA approach for analyzing multireader
#' multicase radiological imaging data, Statistics in medicine 33, 330-360.
#'
#' Thompson JD, Chakraborty DP, Szczepura K, et al. (2016) Effect of
#' reconstruction methods and x-ray tube current-time product on nodule
#' detection in an anthropomorphic thorax phantom: a crossed-modality JAFROC
#' observer study. Medical Physics. 43(3):1265-1274.
#'
#' Chakraborty DP (2017) \emph{Observer Performance Methods for Diagnostic Imaging - Foundations,
#' Modeling, and Applications with R-Based Examples}, CRC Press, Boca Raton, FL.
#' \url{https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840}
#'
#'
#' @importFrom stats pf pt qt
#' @importFrom Rcpp evalCpp
#' @useDynLib RJafroc
#'
#'
#' @export
St <- function(dataset,
FOM,
method = "OR",
covEstMethod = "jackknife",
analysisOption = "RRRC",
alpha = 0.05,
FPFValue = 0.2,
nBoots = 200,
seed = NULL,
details = 0)
{
isValidDataset(dataset, FOM, method, covEstMethod, analysisOption)
if (method == "DBM"){
ret <- StDBMAnalysis(dataset,
FOM,
analysisOption,
alpha,
FPFValue,
details)
return(ret)
} else { # method == "OR"
ret <- StORAnalysis(dataset,
FOM,
covEstMethod,
analysisOption,
alpha,
FPFValue,
nBoots,
seed,
details)
return(ret)
}
}
MyOutput <- function(fileName) {
fn <- system.file("extdata", fileName, package = "RJafroc", mustWork = TRUE)
x <- readLines(fn)
for (i in 1:length(x)) cat(sprintf("%-s\n", x[i]))
}
DisclaimerDsSummary <- function (dataset, FOM, method, analysisOption) {
MyOutput("OUTPUT/DISCLAIMER.txt")
cat(paste("R version:", R.version$version.string,"\n"))
cat(paste("RJafroc version:", packageVersion("RJafroc"),"\n"))
dateTime <- paste0("Run date: ", base::format(Sys.time(), "%b %d %Y %a %X %Z"))
cat(paste(dateTime, "\n"))
cat(sprintf("Input Data Set : %s\n", dataset$descriptions$name))
cat(sprintf("Data type : %s\n", dataset$descriptions$type))
cat(sprintf("Design type : %s\n", dataset$descriptions$design))
cat(sprintf("FOM selected : %s\n", FOM))
cat(sprintf("Analysis method : %s\n", method))
cat(sprintf("Analysis Option : %s\n", analysisOption))
}
Explanations <- function(dataset, FOM, method, analysisOption, details) {
if (details > 0) {
DisclaimerDsSummary(dataset, FOM, method, analysisOption)
}
if (dataset$descriptions$design == "FCTRL")
{
NL <- dataset$ratings$NL
LL <- dataset$ratings$LL
lesionID <- dataset$lesions$IDs
maxNL <- dim(NL)[4]
dataType <- dataset$descriptions$type
modalityID <- dataset$descriptions$modalityID
I <- length(modalityID)
readerID <- dataset$descriptions$readerID
J <- length(readerID)
K <- dim(NL)[3]
K2 <- dim(LL)[3]
K1 <- K - K2
UNINITIALIZED <- RJafrocEnv$UNINITIALIZED
nLesionPerCase <- rowSums(lesionID != UNINITIALIZED)
if (details > 0) {
if (method == "OR") {
cat(sprintf("Significance testing method : %s\n", toupper("Obuchowski-Rockette-Hillis")))
} else {
cat(sprintf("Significance testing method : %s\n", toupper("Dorfman-Berbaum-Metz-Hillis")))
}
cat(sprintf("Number of Readers : %d\n", J))
cat(sprintf("# Treatments modality : %d\n", I))
cat(sprintf("Number of Normal Cases : %d\n", K1))
cat(sprintf("Number of Abnormal Cases : %d\n", K2))
cat(sprintf("Fraction of Normal Cases : %f\n", K1/K))
if (FOM %in% c("MaxNLF", "HrSp")) {
K <- K1
cat(sprintf("choice of FOM implies only non-diseased cases are used in the analysis\n"))
} else if (FOM %in% c("MaxLLF", "HrSe")) {
K <- K2
cat(sprintf("choice of FOM implies only diseased cases are used in the analysis\n"))
}
if (dataType == "FROC") {
cat(sprintf("Min number of lesions per diseased case : %d\n",
min(nLesionPerCase)))
cat(sprintf("Max number of lesions per diseased case : %d\n",
max(nLesionPerCase)))
cat(sprintf("Mean number of lesions per diseased case : %f\n",
mean(nLesionPerCase)))
cat(sprintf("Total number of lesions : %d\n",
sum(nLesionPerCase)))
}
cat(sprintf("Excel file modality IDs : %s\n", paste(names(modalityID), collapse = ", ")))
cat(sprintf("Excel file reader IDs : %s\n\n", paste(names(readerID), collapse = ", ")))
MyOutput("OUTPUT/OVERVIEW.txt")
}
if (details > 0) {
if (method == "OR") {
MyOutput(paste0("OUTPUT/OR/FCTRL/", "FOMS-ANOVA", ".txt"))
MyOutput(paste0("OUTPUT/OR/FCTRL/", analysisOption, ".txt"))
MyOutput("OUTPUT/RESULTS.txt")
} else {
MyOutput(paste0("OUTPUT/DBM/FCTRL/", "FOMS-ANOVA", ".txt"))
MyOutput(paste0("OUTPUT/DBM/FCTRL/", analysisOption, ".txt"))
MyOutput("OUTPUT/RESULTS.txt")
}
}
} else {
NL <- dataset$ratings$NL
LL <- dataset$ratings$LL
lesionID <- dataset$lesions$IDs
maxNL <- dim(NL)[5]
dataType <- dataset$descriptions$type
modalityID1 <- dataset$descriptions$modalityID1
modalityID2 <- dataset$descriptions$modalityID2
I1 <- length(modalityID1)
I2 <- length(modalityID2)
readerID <- dataset$descriptions$readerID
J <- length(readerID)
K <- dim(NL)[4]
K2 <- dim(LL)[4]
K1 <- K - K2
UNINITIALIZED <- RJafrocEnv$UNINITIALIZED
nLesionPerCase <- rowSums(lesionID != UNINITIALIZED)
if (details > 0) {
if (method == "OR") {
cat(sprintf("Significance testing method : %s\n", toupper("Obuchowski-Rockette-Hillis")))
} else {
cat(sprintf("Significance testing method : %s\n", toupper("Dorfman-Berbaum-Metz-Hillis")))
}
cat(sprintf("Number of Readers : %d\n", J))
cat(sprintf("# treatments 1st modality : %d\n", I1))
cat(sprintf("# treatments 2nd modality : %d\n", I2))
cat(sprintf("Number of Normal Cases : %d\n", K1))
cat(sprintf("Number of Abnormal Cases : %d\n", K2))
cat(sprintf("Fraction of Normal Cases : %f\n", K1/K))
if (FOM %in% c("MaxNLF", "HrSp")) {
K <- K1
cat(sprintf("choice of FOM implies only non-diseased cases are used in the analysis\n"))
} else if (FOM %in% c("MaxLLF", "HrSe")) {
K <- K2
cat(sprintf("choice of FOM implies only diseased cases are used in the analysis\n"))
}
if (dataType == "FROC") {
cat(sprintf("Min number of lesions per diseased case : %d\n",
min(nLesionPerCase)))
cat(sprintf("Max number of lesions per diseased case : %d\n",
max(nLesionPerCase)))
cat(sprintf("Mean number of lesions per diseased case : %f\n",
mean(nLesionPerCase)))
cat(sprintf("Total number of lesions : %d\n",
sum(nLesionPerCase)))
cat(sprintf("Excel file modality IDs 1st treatment : %s\n", paste(names(modalityID1), collapse = ", ")))
cat(sprintf("Excel file modality IDs 2nd treatment : %s\n", paste(names(modalityID2), collapse = ", ")))
cat(sprintf("Excel file reader IDs : %s\n\n", paste(names(readerID), collapse = ", ")))
MyOutput("OUTPUT/OVERVIEW.txt")
UNINITIALIZED <- RJafrocEnv$UNINITIALIZED
nLesionPerCase <- rowSums(lesionID != UNINITIALIZED)
if (dataType == "FROC") {
cat(sprintf("Min number of lesions per diseased case : %d\n",
min(nLesionPerCase)))
cat(sprintf("Max number of lesions per diseased case : %d\n",
max(nLesionPerCase)))
cat(sprintf("Mean number of lesions per diseased case : %f\n",
mean(nLesionPerCase)))
cat(sprintf("Total number of lesions : %d\n",
sum(nLesionPerCase)))
}
}
}
if (details > 0) {
MyOutput(paste0("OUTPUT/DBM/FCTRLX/", analysisOption, "-", method, "-", dataset$descriptions$design, ".txt"))
}
if (details > 0) {
MyOutput("OUTPUT/RESULTS.txt")
}
}
}
dpc10ster/rjafroc-master documentation built on Jan. 31, 2024, 1:07 p.m.
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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 Explanations MyOutput St
Documented in St
#' @name St
#' @title DBM or OR significance testing for a one treatment factorial or
#' two-treatment crossed modality factorial dataset (not SPLIT_PLOT)
#'
#' @description Performs DBM or OR significance testing for the dataset.
#'
#' @param dataset The dataset to be analyzed, see \code{\link{RJafroc-package}}.
#' The dataset design can be "FCTRL" or "FCTRL-X-MOD".
#'
#' @param FOM The figure of merit, see \code{\link{UtilFigureOfMerit}}
#'
#' @param method The significance testing method to be used: \code{"DBM"} for
#' the Dorfman-Berbaum-Metz method or \code{"OR"} for the Obuchowski-Rockette
#' method (default).
#'
#' @param covEstMethod The covariance matrix estimation method in \code{ORH}
#' analysis (for \code{method = "DBM"} the jackknife is always used).
#' \itemize{
#' \item \code{"Jackknife"} (default),
#' \item \code{"Bootstrap"}, in which case \code{nBoots} is relevant, default
#' 200,
#' \item \code{"DeLong"}; requires \code{FOM = "Wilcoxon" or "ROI" or "HrAuc"}.
#' }
#'
#' @param analysisOption Determines which factors are regarded as random and
#' which are fixed:
#' \itemize{
#' \item \code{"RRRC"} = random-reader random case (default),
#' \item \code{"FRRC"} = fixed-reader random case,
#' \item \code{"RRFC"} = random-reader fixed case,
#' }
#'
#' @param alpha The significance level (alpha) of the test of the null
#' hypothesis that all modality effects are zero (default: alpha = 0.05).
#'
#' @param FPFValue Only needed for \code{LROC} data \strong{and} FOM = "PCL" or
#' "ALROC"; where to evaluate a partial curve based figure of merit. (default:
#' FPFValue = 0.2).
#'
#' @param nBoots The number of bootstraps (defaults to 200), only needed if
#' \code{covEstMethod = "bootstrap"} and \code{method = "OR"}
#'
#' @param seed For bootstraps the seed of the RNG (default: seed = \code{NULL}),
#' only needed if \code{method = "OR"} and \code{covEstMethod = "bootstrap"}.
#'
#' @param details Amount of explanations in output, default is 0 for no
#' explanations and 1 for explanations.
#'
#'
#' @return A list containing the results of the analysis.
#'
#' @examples
#' result <- St(dataset02,FOM = "Wilcoxon", method = "DBM")
#' result <- St(dataset02,FOM = "Wilcoxon", method = "OR")
#' result <- St(datasetX, FOM = "wAFROC", method = "OR", analysisOption = "RRRC")
#'
#' \donttest{
#' result <- St(dataset05, FOM = "wAFROC")
#' result <- St(dataset05, FOM = "HrAuc", method = "DBM")
#' }
#'
#' @note \code{details} = 0 should suffice for factorial dataset analysis since
#' the names of the output lists are self-explanatory. For cross-modality
#' analysis \code{details} = 1 is suggested to better understand the output.
#'
#' @references Dorfman DD, Berbaum KS, Metz CE (1992) ROC characteristic rating
#' analysis: Generalization to the Population of Readers and Patients with the
#' Jackknife method, Invest. Radiol. 27, 723-731.
#'
#' Obuchowski NA, Rockette HE (1995) Hypothesis Testing of the Diagnostic
#' Accuracy for Multiple Diagnostic Tests: An ANOVA Approach with Dependent
#' Observations, Communications in Statistics: Simulation and Computation 24,
#' 285-308.
#'
#' Hillis SL (2014) A marginal-mean ANOVA approach for analyzing multireader
#' multicase radiological imaging data, Statistics in medicine 33, 330-360.
#'
#' Thompson JD, Chakraborty DP, Szczepura K, et al. (2016) Effect of
#' reconstruction methods and x-ray tube current-time product on nodule
#' detection in an anthropomorphic thorax phantom: a crossed-modality JAFROC
#' observer study. Medical Physics. 43(3):1265-1274.
#'
#' Chakraborty DP (2017) \emph{Observer Performance Methods for Diagnostic Imaging - Foundations,
#' Modeling, and Applications with R-Based Examples}, CRC Press, Boca Raton, FL.
#' \url{https://www.routledge.com/Observer-Performance-Methods-for-Diagnostic-Imaging-Foundations-Modeling/Chakraborty/p/book/9781482214840}
#'
#'
#' @importFrom stats pf pt qt
#' @importFrom Rcpp evalCpp
#' @useDynLib RJafroc
#'
#'
#' @export
St <- function(dataset,
FOM,
method = "OR",
covEstMethod = "jackknife",
analysisOption = "RRRC",
alpha = 0.05,
FPFValue = 0.2,
nBoots = 200,
seed = NULL,
details = 0)
{
isValidDataset(dataset, FOM, method, covEstMethod, analysisOption)
if (method == "DBM"){
ret <- StDBMAnalysis(dataset,
FOM,
analysisOption,
alpha,
FPFValue,
details)
return(ret)
} else { # method == "OR"
ret <- StORAnalysis(dataset,
FOM,
covEstMethod,
analysisOption,
alpha,
FPFValue,
nBoots,
seed,
details)
return(ret)
}
}
MyOutput <- function(fileName) {
fn <- system.file("extdata", fileName, package = "RJafroc", mustWork = TRUE)
x <- readLines(fn)
for (i in 1:length(x)) cat(sprintf("%-s\n", x[i]))
}
DisclaimerDsSummary <- function (dataset, FOM, method, analysisOption) {
MyOutput("OUTPUT/DISCLAIMER.txt")
cat(paste("R version:", R.version$version.string,"\n"))
cat(paste("RJafroc version:", packageVersion("RJafroc"),"\n"))
dateTime <- paste0("Run date: ", base::format(Sys.time(), "%b %d %Y %a %X %Z"))
cat(paste(dateTime, "\n"))
cat(sprintf("Input Data Set : %s\n", dataset$descriptions$name))
cat(sprintf("Data type : %s\n", dataset$descriptions$type))
cat(sprintf("Design type : %s\n", dataset$descriptions$design))
cat(sprintf("FOM selected : %s\n", FOM))
cat(sprintf("Analysis method : %s\n", method))
cat(sprintf("Analysis Option : %s\n", analysisOption))
}
Explanations <- function(dataset, FOM, method, analysisOption, details) {
if (details > 0) {
DisclaimerDsSummary(dataset, FOM, method, analysisOption)
}
if (dataset$descriptions$design == "FCTRL")
{
NL <- dataset$ratings$NL
LL <- dataset$ratings$LL
lesionID <- dataset$lesions$IDs
maxNL <- dim(NL)[4]
dataType <- dataset$descriptions$type
modalityID <- dataset$descriptions$modalityID
I <- length(modalityID)
readerID <- dataset$descriptions$readerID
J <- length(readerID)
K <- dim(NL)[3]
K2 <- dim(LL)[3]
K1 <- K - K2
UNINITIALIZED <- RJafrocEnv$UNINITIALIZED
nLesionPerCase <- rowSums(lesionID != UNINITIALIZED)
if (details > 0) {
if (method == "OR") {
cat(sprintf("Significance testing method : %s\n", toupper("Obuchowski-Rockette-Hillis")))
} else {
cat(sprintf("Significance testing method : %s\n", toupper("Dorfman-Berbaum-Metz-Hillis")))
}
cat(sprintf("Number of Readers : %d\n", J))
cat(sprintf("# Treatments modality : %d\n", I))
cat(sprintf("Number of Normal Cases : %d\n", K1))
cat(sprintf("Number of Abnormal Cases : %d\n", K2))
cat(sprintf("Fraction of Normal Cases : %f\n", K1/K))
if (FOM %in% c("MaxNLF", "HrSp")) {
K <- K1
cat(sprintf("choice of FOM implies only non-diseased cases are used in the analysis\n"))
} else if (FOM %in% c("MaxLLF", "HrSe")) {
K <- K2
cat(sprintf("choice of FOM implies only diseased cases are used in the analysis\n"))
}
if (dataType == "FROC") {
cat(sprintf("Min number of lesions per diseased case : %d\n",
min(nLesionPerCase)))
cat(sprintf("Max number of lesions per diseased case : %d\n",
max(nLesionPerCase)))
cat(sprintf("Mean number of lesions per diseased case : %f\n",
mean(nLesionPerCase)))
cat(sprintf("Total number of lesions : %d\n",
sum(nLesionPerCase)))
}
cat(sprintf("Excel file modality IDs : %s\n", paste(names(modalityID), collapse = ", ")))
cat(sprintf("Excel file reader IDs : %s\n\n", paste(names(readerID), collapse = ", ")))
MyOutput("OUTPUT/OVERVIEW.txt")
}
if (details > 0) {
if (method == "OR") {
MyOutput(paste0("OUTPUT/OR/FCTRL/", "FOMS-ANOVA", ".txt"))
MyOutput(paste0("OUTPUT/OR/FCTRL/", analysisOption, ".txt"))
MyOutput("OUTPUT/RESULTS.txt")
} else {
MyOutput(paste0("OUTPUT/DBM/FCTRL/", "FOMS-ANOVA", ".txt"))
MyOutput(paste0("OUTPUT/DBM/FCTRL/", analysisOption, ".txt"))
MyOutput("OUTPUT/RESULTS.txt")
}
}
} else {
NL <- dataset$ratings$NL
LL <- dataset$ratings$LL
lesionID <- dataset$lesions$IDs
maxNL <- dim(NL)[5]
dataType <- dataset$descriptions$type
modalityID1 <- dataset$descriptions$modalityID1
modalityID2 <- dataset$descriptions$modalityID2
I1 <- length(modalityID1)
I2 <- length(modalityID2)
readerID <- dataset$descriptions$readerID
J <- length(readerID)
K <- dim(NL)[4]
K2 <- dim(LL)[4]
K1 <- K - K2
UNINITIALIZED <- RJafrocEnv$UNINITIALIZED
nLesionPerCase <- rowSums(lesionID != UNINITIALIZED)
if (details > 0) {
if (method == "OR") {
cat(sprintf("Significance testing method : %s\n", toupper("Obuchowski-Rockette-Hillis")))
} else {
cat(sprintf("Significance testing method : %s\n", toupper("Dorfman-Berbaum-Metz-Hillis")))
}
cat(sprintf("Number of Readers : %d\n", J))
cat(sprintf("# treatments 1st modality : %d\n", I1))
cat(sprintf("# treatments 2nd modality : %d\n", I2))
cat(sprintf("Number of Normal Cases : %d\n", K1))
cat(sprintf("Number of Abnormal Cases : %d\n", K2))
cat(sprintf("Fraction of Normal Cases : %f\n", K1/K))
if (FOM %in% c("MaxNLF", "HrSp")) {
K <- K1
cat(sprintf("choice of FOM implies only non-diseased cases are used in the analysis\n"))
} else if (FOM %in% c("MaxLLF", "HrSe")) {
K <- K2
cat(sprintf("choice of FOM implies only diseased cases are used in the analysis\n"))
}
if (dataType == "FROC") {
cat(sprintf("Min number of lesions per diseased case : %d\n",
min(nLesionPerCase)))
cat(sprintf("Max number of lesions per diseased case : %d\n",
max(nLesionPerCase)))
cat(sprintf("Mean number of lesions per diseased case : %f\n",
mean(nLesionPerCase)))
cat(sprintf("Total number of lesions : %d\n",
sum(nLesionPerCase)))
cat(sprintf("Excel file modality IDs 1st treatment : %s\n", paste(names(modalityID1), collapse = ", ")))
cat(sprintf("Excel file modality IDs 2nd treatment : %s\n", paste(names(modalityID2), collapse = ", ")))
cat(sprintf("Excel file reader IDs : %s\n\n", paste(names(readerID), collapse = ", ")))
MyOutput("OUTPUT/OVERVIEW.txt")
UNINITIALIZED <- RJafrocEnv$UNINITIALIZED
nLesionPerCase <- rowSums(lesionID != UNINITIALIZED)
if (dataType == "FROC") {
cat(sprintf("Min number of lesions per diseased case : %d\n",
min(nLesionPerCase)))
cat(sprintf("Max number of lesions per diseased case : %d\n",
max(nLesionPerCase)))
cat(sprintf("Mean number of lesions per diseased case : %f\n",
mean(nLesionPerCase)))
cat(sprintf("Total number of lesions : %d\n",
sum(nLesionPerCase)))
}
}
}
if (details > 0) {
MyOutput(paste0("OUTPUT/DBM/FCTRLX/", analysisOption, "-", method, "-", dataset$descriptions$design, ".txt"))
}
if (details > 0) {
MyOutput("OUTPUT/RESULTS.txt")
}
}
}
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