Generate binary data (LFC model)
Arguments
- n
(numeric)
integer, total sample size- prev
(numeric)
disease and healthy prevalence (length 2, adds up to 1)- random
(logical)
random sampling (TRUE) or fixed prevalence (FALSE)- m
(numeric)
integer, number of models- se
(numeric)
sensitivity (length 1)- sp
(numeric)
specificity (length 1)- B
(numeric)
integer, between 1 and m, specifies how many sensitivity values are projected to 1- L
(numeric)
worst alternative is computed under side condition Acc <= L (default value L=1 corresponds to true LFC where values are projected to 1)- Rse
(matrix)
correlation matrix for empirical sensitivities (m x m)- Rsp
(maxtrix)
correlation matrix for empirical specificities (m x m)- modnames
(modnames)
character, model names (length m)- ...
(any)
further arguments (currently unused)
Value
(list)
list of matrices including generated binary datasets
(1: correct prediction, 0: incorrect prediction) for each subgroup (specificity, sensitivity)
Examples
data <- draw_data_lfc()
head(data)
#> $specificity
#> model1 model2 model3 model4 model5 model6 model7 model8 model9 model10
#> [1,] 1 1 1 0 1 1 1 1 1 1
#> [2,] 1 1 1 1 1 0 1 1 1 1
#> [3,] 1 1 1 1 0 1 1 1 0 1
#> [4,] 1 1 1 1 1 1 1 1 0 0
#> [5,] 1 1 1 1 1 1 1 1 1 0
#> [6,] 1 1 1 1 0 1 1 1 1 1
#> [7,] 1 1 1 1 1 0 1 1 1 1
#> [8,] 1 1 1 1 0 1 1 1 0 1
#> [9,] 1 1 1 0 1 1 1 1 1 0
#> [10,] 1 1 1 1 1 0 1 1 1 0
#> [11,] 1 1 1 1 1 1 1 1 0 1
#> [12,] 1 1 1 1 0 0 1 1 1 1
#> [13,] 1 1 1 0 1 1 1 1 0 0
#> [14,] 1 1 1 1 1 1 1 1 1 1
#> [15,] 1 1 1 1 1 1 1 1 1 1
#> [16,] 1 1 1 1 1 1 1 1 1 0
#> [17,] 1 1 1 1 1 0 1 1 1 1
#> [18,] 1 1 1 1 1 1 1 1 1 1
#> [19,] 1 1 1 1 1 1 1 1 0 1
#> [20,] 1 1 1 1 1 1 1 1 1 1
#> [21,] 1 1 1 1 1 1 1 1 1 0
#> [22,] 1 1 1 0 0 0 1 1 1 1
#> [23,] 1 1 1 1 1 0 1 1 0 1
#> [24,] 1 1 1 1 1 1 1 1 0 1
#> [25,] 1 1 1 1 1 1 1 1 0 1
#> [26,] 1 1 1 1 1 1 1 1 0 1
#> [27,] 1 1 1 0 1 1 1 1 0 1
#> [28,] 1 1 1 1 0 1 1 1 1 1
#> [29,] 1 1 1 1 1 1 1 1 1 1
#> [30,] 1 1 1 1 1 1 1 1 1 0
#> [31,] 1 1 1 0 1 0 1 1 1 1
#> [32,] 1 1 1 1 1 1 1 1 1 1
#> [33,] 1 1 1 1 0 1 1 1 1 1
#> [34,] 1 1 1 1 1 1 1 1 1 1
#> [35,] 1 1 1 1 1 1 1 1 1 0
#> [36,] 1 1 1 1 1 1 1 1 1 1
#> [37,] 1 1 1 1 1 1 1 1 1 1
#> [38,] 1 1 1 1 1 0 1 1 1 1
#> [39,] 1 1 1 1 1 0 1 1 1 1
#> [40,] 1 1 1 1 1 1 1 1 1 0
#> [41,] 1 1 1 1 1 0 1 1 1 1
#> [42,] 1 1 1 1 1 1 1 1 1 1
#> [43,] 1 1 1 1 1 0 1 1 0 1
#> [44,] 1 1 1 1 0 0 1 1 1 1
#> [45,] 1 1 1 0 1 1 1 1 1 1
#> [46,] 1 1 1 1 1 1 1 1 1 1
#> [47,] 1 1 1 1 1 1 1 1 1 1
#> [48,] 1 1 1 1 1 1 1 1 1 0
#> [49,] 1 1 1 0 1 1 1 1 1 1
#> [50,] 1 1 1 1 1 0 1 1 0 0
#>
#> $sensitivity
#> model1 model2 model3 model4 model5 model6 model7 model8 model9 model10
#> [1,] 1 1 1 1 1 1 1 1 1 1
#> [2,] 1 0 0 1 1 1 0 1 1 1
#> [3,] 1 1 1 1 1 1 1 1 1 1
#> [4,] 0 1 1 1 1 1 1 1 1 1
#> [5,] 1 1 1 1 1 1 1 1 1 1
#> [6,] 1 1 1 1 1 1 1 1 1 1
#> [7,] 1 1 0 1 1 1 1 1 1 1
#> [8,] 0 1 1 1 1 1 1 1 1 1
#> [9,] 1 0 0 1 1 1 1 1 1 1
#> [10,] 0 1 1 1 1 1 1 0 1 1
#> [11,] 1 0 1 1 1 1 1 1 1 1
#> [12,] 1 1 1 1 1 1 1 1 1 1
#> [13,] 0 1 1 1 1 1 1 1 1 1
#> [14,] 0 1 1 1 1 1 1 0 1 1
#> [15,] 1 0 1 1 1 1 1 1 1 1
#> [16,] 1 1 1 1 1 1 1 0 1 1
#> [17,] 1 1 1 1 1 1 1 1 1 1
#> [18,] 1 1 1 1 1 1 1 1 1 1
#> [19,] 0 1 0 1 1 1 1 1 1 1
#> [20,] 1 0 1 1 1 1 1 1 1 1
#> [21,] 1 1 1 1 1 1 1 1 1 1
#> [22,] 1 1 1 1 1 1 1 1 1 1
#> [23,] 0 1 1 1 1 1 1 1 1 1
#> [24,] 1 1 1 1 1 1 1 1 1 1
#> [25,] 1 0 0 1 1 1 1 1 1 1
#> [26,] 1 1 1 1 1 1 0 1 1 1
#> [27,] 0 1 1 1 1 1 1 0 1 1
#> [28,] 1 1 0 1 1 1 1 1 1 1
#> [29,] 1 1 1 1 1 1 1 1 1 1
#> [30,] 0 0 1 1 1 1 1 1 1 1
#> [31,] 0 0 0 1 1 1 1 0 1 1
#> [32,] 1 1 1 1 1 1 1 1 1 1
#> [33,] 0 1 1 1 1 1 1 1 1 1
#> [34,] 1 1 0 1 1 1 1 1 1 1
#> [35,] 0 1 1 1 1 1 1 1 1 1
#> [36,] 1 0 1 1 1 1 1 0 1 1
#> [37,] 0 1 1 1 1 1 0 1 1 1
#> [38,] 1 1 1 1 1 1 1 1 1 1
#> [39,] 1 0 1 1 1 1 1 1 1 1
#> [40,] 1 0 1 1 1 1 1 1 1 1
#> [41,] 1 1 1 1 1 1 0 1 1 1
#> [42,] 1 1 1 1 1 1 1 0 1 1
#> [43,] 1 0 1 1 1 1 1 0 1 1
#> [44,] 1 1 1 1 1 1 1 0 1 1
#> [45,] 1 1 1 1 1 1 1 0 1 1
#> [46,] 0 1 1 1 1 1 1 1 1 1
#> [47,] 1 1 0 1 1 1 1 1 1 1
#> [48,] 1 1 1 1 1 1 1 1 1 1
#> [49,] 1 1 1 1 1 1 1 0 1 1
#> [50,] 0 1 1 1 1 1 1 1 1 1
#>