Df2RJafrocDataset: Convert ratings arrays to an RJafroc dataset

Description

Converts ratings arrays, ROC or FROC, not LROC, to an RJafroc dataset, thereby allowing the user to leverage the file I/O, plotting and analyses capabilities of RJafroc.

Usage

Df2RJafrocDataset (NL, LL, ...)

Arguments

Non-lesion localizations array (or FP array for ROC data).

Lesion localizations array (or TP array for ROC data).

...

Other elements of RJafroc dataset that may, depending on the context, need to be specified. lesionNum must be specified if an FROC dataset is to be returned. It is a K2-length array specifying the numbers of lesions in each diseased case in the dataset.

Value

A dataset with the structure described in RJafroc-package.

Details

The function "senses" the data type (ROC or FROC) from the the absence or presence of lesionNum. ROC data can be NL[1:K1] and LL[1:K2] or NL[1:I,1:J,1:K1] and LL[1:I,1:J,1:K2]. FROC data can be NL[1:K1,1:maxNL] and LL[1:K2, 1:maxLL] or NL[1:I,1:J,1:K1,1:maxNL] and LL[1:I,1:J,1:K2,1:maxLL]. Here maxNL/maxLL = maximum numbers of NLs/LLs, per case, over entire dataset. Equal weights are assigned to every lesion (FROC data). Consecutive characters/integers starting from "1" are assigned to lesionID, modalityID and readerID.

Examples

Run this code

# NOT RUN {
set.seed(1)
NL <- rnorm(5)
LL <- rnorm(7)*1.5 + 2
dataset <- Df2RJafrocDataset(NL, LL) # an ROC dataset

I <- 2;J <- 3;set.seed(1)
K1 <- 25;K2 <- 35
z1 <- array(dim = c(I, J, K1))
z2 <- array(dim = c(I, J, K2))
mu <- 2;sigma <- 1.5
for (i in 1:I) {
 for (j in 1:J) {
   z1[i,j,1:K1] <- rnorm(K1)
   z2[i,j,] <- rnorm(K2) * sigma + mu
 }
}
dataset <- Df2RJafrocDataset(z1, z2) ## note absence of lesionNum;  # an ROC dataset


set.seed(1)
mu <- 1;lambda <- 1;nu <- 1; zeta1 <- 0
K1 <- 5;K2 <- 7
Lmax <- 2;Lk2 <- floor(runif(K2, 1, Lmax + 1))
frocDataRaw <- SimulateFrocDataset(mu, lambda, nu, zeta1, I = 1, J = 1, K1, K2, 
lesionNum = Lk2)
NL <- drop(frocDataRaw$NL)
LL <- drop(frocDataRaw$LL)
dataset <- Df2RJafrocDataset(NL, LL, lesionNum = Lk2) 
## note presence of lesionNum, signalling an FROC dataset

## Simulated FROC dataset, convert to dataset object, and display ROC, FROC and AFROC curves
I <- 2;J <- 3;set.seed(1)
K1 <- 25;K2 <- 35
mu <- 1;nuP <- 0.8;lambdaP <- 1;zeta1 <- 0
lambda <- UtilPhysical2IntrinsicRSM(mu,lambdaP,nuP)$lambda
nu <- UtilPhysical2IntrinsicRSM(mu,lambdaP,nuP)$nu
Lmax <- 2;Lk2 <- floor(runif(K2, 1, Lmax + 1))
z1 <- array(-Inf,dim = c(I,J,K1+K2,40))
z2 <- array(-Inf,dim = c(I,J,K2,40))
dimNL <- array(dim=c(I,J,2)) 
## the last value (2) accommodates case and location indices
dimLL <- array(dim=c(I,J,2))
for (i in 1:I) {
  for (j in 1:J) {
    frocDataRaw <- SimulateFrocDataset(mu, lambda, nu, zeta1, I = 1, 
    J = 1, K1, K2, lesionNum = Lk2)
    dimNL[i,j,] <- dim(drop(frocDataRaw$NL))
    dimLL[i,j,] <- dim(drop(frocDataRaw$LL))
    z1[i,j,,1:dimNL[i,j,2]] <- drop(frocDataRaw$NL) # drop the excess location indices
    z2[i,j,,1:dimLL[i,j,2]] <- drop(frocDataRaw$LL)
  }
}
z1 <- z1[,,,1:max(dimNL[,,2])]
z2 <- z2[,,,1:max(dimLL[,,2])]

dataset <- Df2RJafrocDataset(z1, z2, lesionNum = Lk2)

retPlot <- PlotEmpiricalOperatingCharacteristics(dataset, 
trts = seq(1,I), rdrs = seq(1,J), opChType = "ROC")
print(retPlot$Plot)

retPlot <- PlotEmpiricalOperatingCharacteristics(dataset, 
trts = seq(1,I), rdrs = seq(1,J), opChType = "FROC")
print(retPlot$Plot)

retPlot <- PlotEmpiricalOperatingCharacteristics(dataset, 
trts = seq(1,I), rdrs = seq(1,J), opChType = "AFROC")
print(retPlot$Plot)



# }