DfFroc2Roc {RJafroc}

Convert an FROC dataset to an ROC dataset

Description

Convert an FROC dataset to a highest rating inferred ROC dataset

Usage

DfFroc2Roc(dataset)

Arguments

dataset

The FROC dataset to be converted, RJafroc-package.

Details

The first member of the ROC dataset is NL, whose 3rd dimension has length (K1 + K2), the total number of cases. Ratings of cases (K1 + 1) through (K1 + K2) are -Inf. This is because in an ROC dataset FPs are only possible on non-diseased cases.The second member of the list is LL. Its 3rd dimension has length K2, the number of diseased cases. This is because TPs are only possible on diseased cases. For each case the inferred ROC rating is the highest of all FROC ratings on that case. If a case has no marks, a finite ROC rating, guaranteed to be smaller than the rating on any marked case, is assigned to it. The dataset structure is shown below:

• NL Ratings array [1:I, 1:J, 1:(K1+K2), 1], of false positives, FPs

• LL Ratings array [1:I, 1:J, 1:K2, 1], of true positives, TPs

• perCase array [1:K2], number of lesions per diseased case

• IDs array [1:K2, 1], labels of lesions on diseased cases

• weights array [1:K2, 1], weights (or clinical importances) of lesions

• dataType "ROC", the data type

• modalityID [1:I] inherited modality labels

• readerID [1:J] inherited reader labels

Value

An ROC dataset with finite ratings in NL[,,1:K1,1] and LL[,,1:K2,1].

Examples

rocDataSet <- DfFroc2Roc(dataset05)
rocSpDataSet <- DfFroc2Roc(datasetFROCSpC)

## in the following example, because of the smaller number of cases, 
## it is easy to see the process at work:

set.seed(1);K1 <- 3;K2 <- 5
mu <- 1;nu <- 0.5;lambda <- 2;zeta1 <- 0
lambda_i <- UtilRSM2Intrinsic(mu,lambda,nu)$lambda_i
nu_i <- UtilRSM2Intrinsic(mu,lambda,nu)$nu_i
Lmax <- 2;Lk2 <- floor(runif(K2, 1, Lmax + 1))
frocDataRaw <- SimulateFrocDataset(mu, lambda_i, nu_i, zeta1, I = 1, J = 1, 
K1, K2, perCase = Lk2)
hrData <- DfFroc2Roc(frocDataRaw)

## print("frocDataRaw$ratings$NL[1,1,,] = ")
## print("hrData$ratings$NL[1,1,1:K1,] = ")
## print("frocDataRaw$ratings$LL[1,1,,] = ")
## print("hrData$ratings$LL[1,1,,] = ")

## following is the output

## [1] "frocDataRaw$ratings$NL[1,1,,] = "
## [,1]      [,2]      [,3] [,4]
## [1,] 2.4046534 0.7635935      -Inf -Inf
## [2,]      -Inf      -Inf      -Inf -Inf
## [3,] 0.2522234      -Inf      -Inf -Inf
## [4,] 0.4356833      -Inf      -Inf -Inf
## [5,]      -Inf      -Inf      -Inf -Inf
## [6,]      -Inf      -Inf      -Inf -Inf
## [7,]      -Inf      -Inf      -Inf -Inf
## [8,] 0.8041895 0.3773956 0.1333364 -Inf

## > ## print("hrData$ratings$NL[1,1,1:K1,] = ")
## [1] "hrData$ratings$NL[1,1,1:K1,] = "
## [1] 2.4046534      -Inf 0.2522234
## > ## print("frocDataRaw$ratings$LL[1,1,,] = ")
## [1] "frocDataRaw$ratings$LL[1,1,,] = "
## [,1] [,2]
## [1,]      -Inf -Inf
## [2,] 1.5036080 -Inf
## [3,] 0.8442045 -Inf
## [4,] 1.0467262 -Inf
## [5,]      -Inf -Inf
## > ## print("hrData$ratings$LL[1,1,,] = ")
## [1] "hrData$ratings$LL[1,1,,] = "
## [1] 0.4356833 1.5036080 0.8442045 1.0467262 0.8041895
## Note that rating of the first and the last diseased case came from NL marks