Extract Confidence Intervals from bbnp_regression Object
confint.bbnp_regression.RdExtracts confidence intervals for conditional expectation estimates
Usage
# S3 method for class 'bbnp_regression'
confint(object, parm = NULL, level = 0.95, ...)Value
For range estimation: a matrix with columns "lower" and "upper" For point estimation: a named vector with elements "lower" and "upper"
Examples
# \donttest{
X <- gen_sample_data(size = 500, dgp = "2_fold_uniform", seed = 1)
Y <- 2 * X - X^2 + rnorm(length(X), sd = 0.3)
fit <- biasBound_condExpectation(Y, X, h = 0.1)
confint(fit)
#> lower upper
#> [1,] -2.0883000265 2.0556689
#> [2,] -1.6681340686 1.7475692
#> [3,] -1.3474126633 1.5250201
#> [4,] -1.0955838386 1.3613155
#> [5,] -0.8928126877 1.2393250
#> [6,] -0.7260135464 1.1477496
#> [7,] -0.5862570222 1.0789527
#> [8,] -0.4673157885 1.0275073
#> [9,] -0.3645485069 0.9895017
#> [10,] -0.2745220353 0.9619789
#> [11,] -0.1947598050 0.9427846
#> [12,] -0.1233042406 0.9302849
#> [13,] -0.0586515319 0.9232426
#> [14,] 0.0003681468 0.9206455
#> [15,] 0.0546896095 0.9216480
#> [16,] 0.1050829681 0.9256296
#> [17,] 0.1521416994 0.9320200
#> [18,] 0.1963441928 0.9403921
#> [19,] 0.2380796701 0.9503545
#> [20,] 0.2776245948 0.9615892
#> [21,] 0.3151904407 0.9738060
#> [22,] 0.3509144109 0.9867219
#> [23,] 0.3849315997 1.0001066
#> [24,] 0.4173167921 1.0137412
#> [25,] 0.4481259610 1.0274575
#> [26,] 0.4774201010 1.0410968
#> [27,] 0.5052354003 1.0545203
#> [28,] 0.5315693529 1.0675858
#> [29,] 0.5564222630 1.0801509
#> [30,] 0.5798152111 1.0921563
#> [31,] 0.6017514797 1.1035236
#> [32,] 0.6222532896 1.1142204
#> [33,] 0.6413336791 1.1242204
#> [34,] 0.6590162221 1.1335222
#> [35,] 0.6753240893 1.1421255
#> [36,] 0.6903068816 1.1500567
#> [37,] 0.7040205498 1.1573477
#> [38,] 0.7165189206 1.1640394
#> [39,] 0.7278597966 1.1701762
#> [40,] 0.7381054706 1.1758129
#> [41,] 0.7473056143 1.1809852
#> [42,] 0.7554972281 1.1857230
#> [43,] 0.7627259432 1.1900638
#> [44,] 0.7690455957 1.1940630
#> [45,] 0.7744920600 1.1977493
#> [46,] 0.7790834594 1.2011308
#> [47,] 0.7828289850 1.2042141
#> [48,] 0.7857319947 1.2069943
#> [49,] 0.7877840758 1.2094531
#> [50,] 0.7889775581 1.2115651
#> [51,] 0.7892830321 1.2132938
#> [52,] 0.7886752703 1.2146071
#> [53,] 0.7871067591 1.2154463
#> [54,] 0.7845378040 1.2157599
#> [55,] 0.7809160934 1.2154721
#> [56,] 0.7761835901 1.2145190
#> [57,] 0.7703032389 1.2128437
#> [58,] 0.7632325945 1.2103817
#> [59,] 0.7549283812 1.2070685
#> [60,] 0.7453465748 1.2028321
#> [61,] 0.7344703315 1.1976375
#> [62,] 0.7223010880 1.1914626
#> [63,] 0.7088602553 1.1843001
#> [64,] 0.6941749664 1.1761666
#> [65,] 0.6782887197 1.1670972
#> [66,] 0.6612592367 1.1571598
#> [67,] 0.6431406731 1.1464241
#> [68,] 0.6240201515 1.1350078
#> [69,] 0.6039707088 1.1230273
#> [70,] 0.5830706870 1.1106317
#> [71,] 0.5613995086 1.0979627
#> [72,] 0.5390172977 1.0851818
#> [73,] 0.5159735836 1.0724444
#> [74,] 0.4922943794 1.0599111
#> [75,] 0.4679952965 1.0477503
#> [76,] 0.4430641619 1.0361399
#> [77,] 0.4174646074 1.0252496
#> [78,] 0.3910814331 1.0152216
#> [79,] 0.3638240842 1.0062298
#> [80,] 0.3355224026 0.9984571
#> [81,] 0.3060049425 0.9920918
#> [82,] 0.2750850893 0.9873547
#> [83,] 0.2425054710 0.9844946
#> [84,] 0.2079478495 0.9837793
#> [85,] 0.1710462094 0.9855258
#> [86,] 0.1313553883 0.9901441
#> [87,] 0.0883385085 0.9981111
#> [88,] 0.0413380945 1.0100210
#> [89,] -0.0104120749 1.0266276
#> [90,] -0.0678891006 1.0488633
#> [91,] -0.1322545844 1.0778723
#> [92,] -0.2050106369 1.1150903
#> [93,] -0.2879614475 1.1624041
#> [94,] -0.3834883405 1.2222197
#> [95,] -0.4945551944 1.2976573
#> [96,] -0.6251286491 1.3928315
#> [97,] -0.7807187709 1.5132906
#> [98,] -0.9687069156 1.6665635
#> [99,] -1.1994096211 1.8632905
#> [100,] -1.4877811068 2.1187901
# }