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 <- rnorm(100)
Y <- X^2 + rnorm(100)
fit <- biasBound_condExpectation(Y, X, h = 0.1)
#> Warning: No feasible xi_n passed Schennach's test in interval [1.0480, 3.3140]. Using theoretical upper bound xi_ub = 3.3140. This may indicate insufficient signal or inappropriate xi bounds.
confint(fit)
#> lower upper
#> [1,] 4.02842097 6.14337948
#> [2,] 4.09160519 6.05059964
#> [3,] 4.09659452 6.15600819
#> [4,] 4.13779175 6.43413943
#> [5,] 4.22706693 7.01284683
#> [6,] 4.38861076 8.17108379
#> [7,] 4.64057054 10.55608742
#> [8,] 4.91570507 15.60586437
#> [9,] 4.93518651 21.44543775
#> [10,] 4.41279435 15.71030999
#> [11,] 3.69084771 8.73600673
#> [12,] 3.16904196 5.72632363
#> [13,] 2.85965570 4.45055297
#> [14,] 2.68654118 3.84764431
#> [15,] 2.58514442 3.53637979
#> [16,] 2.52371813 3.37398464
#> [17,] 2.48557841 3.29729907
#> [18,] 2.46097443 3.27688987
#> [19,] 2.44612998 3.30348636
#> [20,] 2.42721235 3.36543292
#> [21,] 2.38895948 3.45701267
#> [22,] 2.30179316 3.54606194
#> [23,] 2.12987097 3.53828984
#> [24,] 1.88830609 3.30490970
#> [25,] 1.67103149 2.88400585
#> [26,] 1.56058571 2.51349630
#> [27,] 1.53550824 2.28674299
#> [28,] 1.54113765 2.15993223
#> [29,] 1.54266727 2.07744291
#> [30,] 1.52183432 2.00137707
#> [31,] 1.47095187 1.91039964
#> [32,] 1.38816072 1.79388363
#> [33,] 1.27517271 1.64922880
#> [34,] 1.13720203 1.48123241
#> [35,] 0.98633004 1.30473947
#> [36,] 0.83685046 1.13994980
#> [37,] 0.70005374 1.00225926
#> [38,] 0.58375500 0.90017953
#> [39,] 0.49034498 0.83287724
#> [40,] 0.41396201 0.78919202
#> [41,] 0.34222387 0.75239798
#> [42,] 0.26292195 0.70567093
#> [43,] 0.16395110 0.63369084
#> [44,] 0.03465353 0.52298269
#> [45,] -0.12827963 0.36844554
#> [46,] -0.32368173 0.17999021
#> [47,] -0.53405958 -0.01598504
#> [48,] -0.72880414 -0.20398622
#> [49,] -0.88283874 -0.35891392
#> [50,] -0.97778543 -0.46071078
#> [51,] -1.00963497 -0.50223125
#> [52,] -0.98585284 -0.48783429
#> [53,] -0.91823452 -0.42669383
#> [54,] -0.82224423 -0.33375726
#> [55,] -0.71287222 -0.22495264
#> [56,] -0.60174093 -0.11399173
#> [57,] -0.49742659 -0.01214844
#> [58,] -0.40505016 0.07343253
#> [59,] -0.32691925 0.13857644
#> [60,] -0.26199583 0.18409654
#> [61,] -0.20820424 0.21375334
#> [62,] -0.16294413 0.23258218
#> [63,] -0.12178391 0.24887124
#> [64,] -0.08015432 0.27208798
#> [65,] -0.03202414 0.31471115
#> [66,] 0.02857650 0.38616583
#> [67,] 0.10978449 0.49376299
#> [68,] 0.21758400 0.63844768
#> [69,] 0.35133769 0.81273045
#> [70,] 0.50346726 1.00236149
#> [71,] 0.65945687 1.18580855
#> [72,] 0.80731977 1.34680176
#> [73,] 0.93607724 1.47159245
#> [74,] 1.04313112 1.55725812
#> [75,] 1.13241679 1.61035796
#> [76,] 1.20749539 1.63807763
#> [77,] 1.27471715 1.65373453
#> [78,] 1.33535210 1.67042443
#> [79,] 1.38250210 1.70482316
#> [80,] 1.41360802 1.76709766
#> [81,] 1.44041344 1.84647117
#> [82,] 1.47070011 1.93561320
#> [83,] 1.50511588 2.03060524
#> [84,] 1.54237672 2.13181347
#> [85,] 1.57756847 2.24104566
#> [86,] 1.60067227 2.36209737
#> [87,] 1.59918388 2.51256262
#> [88,] 1.54553135 2.73031071
#> [89,] 1.39693610 3.12857878
#> [90,] 1.11491152 4.21913368
#> [91,] 0.79704795 9.52475728
#> [92,] 0.85634817 41.10662639
#> [93,] 1.46633645 14.06149644
#> [94,] 2.15566664 7.55471508
#> [95,] 2.65142192 6.02728849
#> [96,] 2.99265250 5.53694777
#> [97,] 3.24034414 5.40674488
#> [98,] 3.42568487 5.44229931
#> [99,] 3.56206157 5.58612854
#> [100,] 3.64802837 5.82224283
# }