Extract Confidence Intervals from bbnp_density Object

Extracts confidence intervals for density estimates

Usage

# S3 method for class 'bbnp_density'
confint(object, parm = NULL, level = NULL, ...)

Arguments

object: An object of class bbnp_density
parm: Not used (included for S3 generic compatibility)
level: Optional confidence level. If supplied, it must match the level used when the object was fitted; changing it requires refitting.
...: Additional arguments (unused)

Value

For range estimation: a matrix with columns "lower" and "upper" For point estimation: a named vector with elements "lower" and "upper"

Examples

# \donttest{
set.seed(1)
x <- runif(500) + runif(500)
fit <- bias_bound_density(x, bw = 0.1)
confint(fit)
#>             lower     upper
#>   [1,] 0.00000000 0.1227684
#>   [2,] 0.00000000 0.1227684
#>   [3,] 0.00000000 0.1227684
#>   [4,] 0.00000000 0.1227684
#>   [5,] 0.00000000 0.1400639
#>   [6,] 0.00000000 0.1549714
#>   [7,] 0.00000000 0.1710650
#>   [8,] 0.00000000 0.1888868
#>   [9,] 0.00000000 0.2086318
#>  [10,] 0.00000000 0.2303904
#>  [11,] 0.00000000 0.2540918
#>  [12,] 0.00000000 0.2796188
#>  [13,] 0.00000000 0.3067886
#>  [14,] 0.00000000 0.3354011
#>  [15,] 0.00000000 0.3652693
#>  [16,] 0.01564274 0.3961032
#>  [17,] 0.03856372 0.4276752
#>  [18,] 0.06222824 0.4596677
#>  [19,] 0.08634571 0.4917557
#>  [20,] 0.11066143 0.5236656
#>  [21,] 0.13500829 0.5552381
#>  [22,] 0.15924248 0.5863398
#>  [23,] 0.18319616 0.6168024
#>  [24,] 0.20675359 0.6465212
#>  [25,] 0.22984745 0.6754482
#>  [26,] 0.25240157 0.7035202
#>  [27,] 0.27443739 0.7307914
#>  [28,] 0.29598154 0.7573173
#>  [29,] 0.31706824 0.7831589
#>  [30,] 0.33779146 0.8084467
#>  [31,] 0.35819305 0.8332438
#>  [32,] 0.37833537 0.8576363
#>  [33,] 0.39831283 0.8817464
#>  [34,] 0.41815715 0.9056190
#>  [35,] 0.43778378 0.9291584
#>  [36,] 0.45711001 0.9522717
#>  [37,] 0.47610157 0.9749244
#>  [38,] 0.49457259 0.9969014
#>  [39,] 0.51238483 1.0180456
#>  [40,] 0.52941783 1.0382218
#>  [41,] 0.54539504 1.0571106
#>  [42,] 0.56007874 1.0744398
#>  [43,] 0.57323511 1.0899427
#>  [44,] 0.58461671 1.1033366
#>  [45,] 0.59397119 1.1143330
#>  [46,] 0.60116925 1.1227872
#>  [47,] 0.60605864 1.1285263
#>  [48,] 0.60852054 1.1314150
#>  [49,] 0.60860600 1.1315153
#>  [50,] 0.60635146 1.1288699
#>  [51,] 0.60175874 1.1234793
#>  [52,] 0.59506364 1.1156165
#>  [53,] 0.58639389 1.1054265
#>  [54,] 0.57592929 1.0931147
#>  [55,] 0.56398897 1.0790498
#>  [56,] 0.55087345 1.0635794
#>  [57,] 0.53681601 1.0469725
#>  [58,] 0.52208765 1.0295440
#>  [59,] 0.50703047 1.0116946
#>  [60,] 0.49176551 0.9935648
#>  [61,] 0.47648200 0.9753775
#>  [62,] 0.46134415 0.9573272
#>  [63,] 0.44633931 0.9393983
#>  [64,] 0.43147727 0.9216021
#>  [65,] 0.41680748 0.9039977
#>  [66,] 0.40218513 0.8864107
#>  [67,] 0.38754001 0.8687550
#>  [68,] 0.37276424 0.8508982
#>  [69,] 0.35764033 0.8325732
#>  [70,] 0.34196530 0.8135275
#>  [71,] 0.32564502 0.7936375
#>  [72,] 0.30853448 0.7727147
#>  [73,] 0.29044944 0.7505183
#>  [74,] 0.27139975 0.7270407
#>  [75,] 0.25130813 0.7021631
#>  [76,] 0.23018106 0.6758646
#>  [77,] 0.20815131 0.6482775
#>  [78,] 0.18534129 0.6195180
#>  [79,] 0.16186244 0.5896845
#>  [80,] 0.13793780 0.5590141
#>  [81,] 0.11377728 0.5277261
#>  [82,] 0.08960459 0.4960565
#>  [83,] 0.06574357 0.4643750
#>  [84,] 0.04248449 0.4330148
#>  [85,] 0.02004580 0.4022188
#>  [86,] 0.00000000 0.3722933
#>  [87,] 0.00000000 0.3435165
#>  [88,] 0.00000000 0.3160674
#>  [89,] 0.00000000 0.2902024
#>  [90,] 0.00000000 0.2660730
#>  [91,] 0.00000000 0.2437305
#>  [92,] 0.00000000 0.2232449
#>  [93,] 0.00000000 0.2046694
#>  [94,] 0.00000000 0.1879515
#>  [95,] 0.00000000 0.1730361
#>  [96,] 0.00000000 0.1598241
#>  [97,] 0.00000000 0.1480154
#>  [98,] 0.00000000 0.1370993
#>  [99,] 0.00000000 0.1227684
#> [100,] 0.00000000 0.1227684
# }