Calculate the differences between the empirical attainment functions of two data sets.
Arguments
- x, y
matrix|data.frame()
Data frames corresponding to the input data of left and right sides, respectively. Each data frame has at least three columns, the last one is the set of each point. See alsoread_datasets().- intervals
integer(1)
The absolute range of the differences \([0, 1]\) is partitioned into the number of intervals provided.- maximise
logical()
Whether the objectives must be maximised instead of minimised. Either a single logical value that applies to all objectives or a vector of logical values, with one value per objective.- rectangles
logical(1)
If TRUE, the output is in the form of rectangles of the same color.
Value
With rectangle=FALSE, a data.frame containing points where there
is a transition in the value of the EAF differences. With
rectangle=TRUE, a matrix where the first 4 columns give the
coordinates of two corners of each rectangle. In both cases, the last
column gives the difference in terms of sets in x minus sets in y that
attain each point (i.e., negative values are differences in favour y).
Examples
A1 <- read_datasets(text='
3 2
2 3
2.5 1
1 2
1 2
')
A2 <- read_datasets(text='
4 2.5
3 3
2.5 3.5
3 3
2.5 3.5
2 1
')
d <- eafdiff(A1, A2)
str(d)
#> num [1:9, 1:3] 1 2 2.5 2 2 3 2.5 3 4 2 ...
d
#> [,1] [,2] [,3]
#> [1,] 1.0 2.0 2
#> [2,] 2.0 1.0 -1
#> [3,] 2.5 1.0 0
#> [4,] 2.0 2.0 1
#> [5,] 2.0 3.0 2
#> [6,] 3.0 2.0 2
#> [7,] 2.5 3.5 0
#> [8,] 3.0 3.0 0
#> [9,] 4.0 2.5 1
d <- eafdiff(A1, A2, rectangles = TRUE)
str(d)
#> num [1:9, 1:5] 2 1 2.5 2 2 2 3 3 4 1 ...
#> - attr(*, "dimnames")=List of 2
#> ..$ : NULL
#> ..$ : chr [1:5] "xmin" "ymin" "xmax" "ymax" ...
d
#> xmin ymin xmax ymax diff
#> [1,] 2.0 1.0 2.5 2.0 -1
#> [2,] 1.0 2.0 2.0 Inf 2
#> [3,] 2.5 1.0 Inf 2.0 0
#> [4,] 2.0 2.0 3.0 3.0 1
#> [5,] 2.0 3.5 2.5 Inf 2
#> [6,] 2.0 3.0 3.0 3.5 2
#> [7,] 3.0 2.5 4.0 3.0 2
#> [8,] 3.0 2.0 Inf 2.5 2
#> [9,] 4.0 2.5 Inf 3.0 1