K-index estimation

Usage

k_index(object, newdata = NULL, ncv = NULL)

Arguments

object: a cureit model object
newdata: A base::data.frame() or tibble::tibble() containing all the original predictors used to create object. Defaults to NULL.
ncv: number of folds for cross validation. Defaults to NULL. If specified, newdata will be ignored and cross-validated k-index will be reported.

Examples

p <- cureit(surv_formula = Surv(ttdeath, death) ~ age, 
   cure_formula = ~ age,
   data = trial) 
#> Warning: 0 of 100 did not converge.
k_index(p)
#> $k_index
#> [1] 0.5132231
#> 
#> $boot_sd
#> [1] 0.01953527
#> 
#> $boot_kindex_2.5
#> [1] 0.5150871
#> 
#> $boot_kindex_97.5
#> [1] 0.5822562
#> 
#> $boot_kindex_mean
#> [1] 0.5382193
#> 
#> $boot_kindex
#>             [,1]
#>   [1,] 0.5498863
#>   [2,] 0.5348491
#>   [3,] 0.5136226
#>   [4,] 0.5540027
#>   [5,] 0.5574568
#>   [6,] 0.5467716
#>   [7,] 0.5202600
#>   [8,] 0.5815434
#>   [9,] 0.5871131
#>  [10,] 0.5242782
#>  [11,] 0.5232019
#>  [12,] 0.5526659
#>  [13,] 0.5243869
#>  [14,] 0.5343037
#>  [15,] 0.5192987
#>  [16,] 0.5366065
#>  [17,] 0.5201453
#>  [18,] 0.5522204
#>  [19,] 0.5212160
#>  [20,] 0.5261992
#>  [21,] 0.5400891
#>  [22,] 0.5384449
#>  [23,] 0.5317926
#>  [24,] 0.5521411
#>  [25,] 0.5506115
#>  [26,] 0.5574251
#>  [27,] 0.5181474
#>  [28,] 0.5368782
#>  [29,] 0.5224586
#>  [30,] 0.5491817
#>  [31,] 0.5219758
#>  [32,] 0.5482820
#>  [33,] 0.5362289
#>  [34,] 0.5829012
#>  [35,] 0.5169262
#>  [36,] 0.5602911
#>  [37,] 0.5365410
#>  [38,] 0.5727508
#>  [39,] 0.5281046
#>  [40,] 0.5485364
#>  [41,] 0.5679131
#>  [42,] 0.5147313
#>  [43,] 0.5308361
#>  [44,] 0.5651296
#>  [45,] 0.5219140
#>  [46,] 0.5154804
#>  [47,] 0.5524599
#>  [48,] 0.5237921
#>  [49,] 0.5696470
#>  [50,] 0.5432816
#>  [51,] 0.5181834
#>  [52,] 0.5189333
#>  [53,] 0.5202260
#>  [54,] 0.5266868
#>  [55,] 0.5125613
#>  [56,] 0.5329621
#>  [57,] 0.5158279
#>  [58,] 0.5185825
#>  [59,] 0.5212637
#>  [60,] 0.5329167
#>  [61,] 0.5294660
#>  [62,] 0.5451775
#>  [63,] 0.5368512
#>  [64,] 0.5265215
#>  [65,] 0.5321446
#>  [66,] 0.5619447
#>  [67,] 0.5253649
#>  [68,] 0.5378376
#>  [69,] 0.5213080
#>  [70,] 0.5439126
#>  [71,] 0.5258981
#>  [72,] 0.5366972
#>  [73,] 0.5340763
#>  [74,] 0.5167006
#>  [75,] 0.5776745
#>  [76,] 0.5263458
#>  [77,] 0.5713213
#>  [78,] 0.5186070
#>  [79,] 0.5369914
#>  [80,] 0.5177848
#>  [81,] 0.5284658
#>  [82,] 0.5206236
#>  [83,] 0.5381082
#>  [84,] 0.5167573
#>  [85,] 0.5189629
#>  [86,] 0.5606847
#>  [87,] 0.5168547
#>  [88,] 0.5228434
#>  [89,] 0.5544511
#>  [90,] 0.5224287
#>  [91,] 0.5776491
#>  [92,] 0.5219854
#>  [93,] 0.5971466
#>  [94,] 0.5452225
#>  [95,] 0.5549130
#>  [96,] 0.5333350
#>  [97,] 0.5714909
#>  [98,] 0.5559964
#>  [99,] 0.5354938
#> [100,] 0.5528301
#>