Determine the lesion weights distribution 2D matrix of a dataset or manually specify the lesion weights distribution.

UtilLesWghtsDS(dsOrArr, relWeights = 0)

UtilLesWghtsLD(LDOrArr, relWeights = 0)

## Arguments

dsOrArr

A dataset object or a 1D-array, see UtilLesDistr.

relWeights

The relative weights of the lesions: a unit sum vector of length equal to the maximum number of lesions per dis. case. For example, c(0.2, 0.4, 0.1, 0.3) means that on cases with one lesion the weight of the lesion is unity, on cases with two lesions the ratio of the weight of the first lesion to that of the second lesion is 0.2:0.4, i.e., lesion 2 is twice as important as lesion 1. On cases with 4 lesions the weights are in the ratio 0.2:0.4:0.1:0.3. The default is zero, in which case equal lesion weights are assumed.

LDOrArr

The lesion distribution (LD) dataframe object produced by UtilLesDistr or a 1D-array.

## Value

The lesion distribution (LD) dataframe object produced by

UtilLesDistr or a 1D-array.

## Details

Two characteristics of an FROC dataset, apart from the ratings, affect the FOM: the distribution of lesion per case and the distribution of lesion weights. This function addresses the weights. The distribution of lesions is addressed in UtilLesDistr. See PlotRsmOperatingCharacteristics for a function that depends on lesWghtDistr. The underlying assumption is that lesion 1 is the same type across all diseased cases, lesion 2 is the same type across all diseased cases, ..., etc. This allows assignment of weights independent of the case index.

## Examples

UtilLesWghtsDS (dataset01) # FROC data
#>      [,1] [,2] [,3]
#> [1,]    1  1.0 -Inf
#> [2,]    2  0.5  0.5

##      [,1] [,2] [,3]
##[1,]    1  1.0 -Inf
##[2,]    2  0.5  0.5

UtilLesWghtsDS (dataset02) # ROC data
#>      [,1] [,2]
#> [1,]    1    1

##      [,1] [,2]
##[1,]    1  1

UtilLesWghtsDS(c(0.7,0.2,0.1)) # only frequencies supplied
#>      [,1]      [,2]      [,3]      [,4]
#> [1,]    1 1.0000000      -Inf      -Inf
#> [2,]    2 0.5000000 0.5000000      -Inf
#> [3,]    3 0.3333333 0.3333333 0.3333333
## relWeights defaults to zero

## Dataset with 1 to 4 lesions per case, with frequency as per first argument

UtilLesWghtsLD (c(0.6, 0.2, 0.1, 0.1), c(0.2, 0.4, 0.1, 0.3))
#>      [,1]      [,2]      [,3]      [,4] [,5]
#> [1,]    1 1.0000000      -Inf      -Inf -Inf
#> [2,]    2 0.3333333 0.6666667      -Inf -Inf
#> [3,]    3 0.2857143 0.5714286 0.1428571 -Inf
#> [4,]    4 0.2000000 0.4000000 0.1000000  0.3

##       [,1]  [,2]      [,3]      [,4]   [,5]
##[1,]    1 1.0000000      -Inf      -Inf -Inf
##[2,]    2 0.3333333 0.6666667      -Inf -Inf
##[3,]    3 0.2857143 0.5714286 0.1428571 -Inf
##[4,]    4 0.2000000 0.4000000 0.1000000  0.3

## Explanation
##> c(0.2)/sum(c(0.2))
##[1] 1 ## (weights for cases with 1 lesion)
##> c(0.2, 0.4)/sum(c(0.2, 0.4))
##[1] 0.3333333 0.6666667 ## (weights for cases with 2 lesions)
##> c(0.2, 0.4, 0.1)/sum(c(0.2, 0.4, 0.1))
##[1] 0.2857143 0.5714286 0.1428571 ## (weights for cases with 3 lesions)
##> c(0.2, 0.4, 0.1, 0.3)/sum(c(0.2, 0.4, 0.1, 0.3))
##[1] 0.2000000 0.4000000 0.1000000  0.3 ## (weights for cases with 4 lesions)

UtilLesWghtsLD (c(0.1, 0.7, 0.0, 0.2), c(0.4, 0.3, 0.2, 0.1))
#>      [,1]      [,2]      [,3]      [,4] [,5]
#> [1,]    1 1.0000000      -Inf      -Inf -Inf
#> [2,]    2 0.5714286 0.4285714      -Inf -Inf
#> [3,]    3 0.4444444 0.3333333 0.2222222 -Inf
#> [4,]    4 0.4000000 0.3000000 0.2000000  0.1

## Weights are included for non-existent lesionID = 3 but corresponding frequency will be zero
##      [,1]       [,2]       [,3]       [,4] [,5]
## [1,]    1 1.00000000       -Inf       -Inf -Inf
## [2,]    2 0.57142857 0.42857143       -Inf -Inf
## [3,]    3 0.44444444 0.33333333 0.22222222 -Inf
## [4,]    4 0.40000000 0.30000000 0.20000000  0.1

UtilLesWghtsDS(dataset05, relWeights = c(0.78723404, 0.17021277, 0.04255319))
#>      [,1]      [,2]      [,3]       [,4]
#> [1,]    1 1.0000000      -Inf       -Inf
#> [2,]    2 0.8222222 0.1777778       -Inf
#> [3,]    3 0.7872340 0.1702128 0.04255319
##      [,1]       [,2]       [,3]       [,4]
## [1,]    1 1.00000000       -Inf       -Inf
## [2,]    2 0.82222222 0.17777778       -Inf
## [3,]    3 0.78723404 0.17021277 0.04255319