selSurv separates Cox-Lasso fitting from the inferential question asked after selection. The main user-facing functions correspond to different inferential targets rather than interchangeable ways to compute the same confidence interval.
The following small example is used only to illustrate the interface.
set.seed(2026)
n <- 120
p <- 6
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
colnames(X) <- paste0("x", seq_len(p))
beta <- c(0.8, -0.6, 0.4, 0, 0, 0)
T <- rexp(n, rate = 0.06 * exp(drop(X %*% beta)))
C <- rexp(n, rate = 0.04)
time <- pmin(T, C)
status <- as.integer(T <= C)library(selSurv)
fit <- coxlasso_fit(X, time, status, lambda_choice = "1se")
fit
#> $X
#> x1 x2 x3 x4 x5
#> [1,] 0.520589073 1.906204189 0.263244987 1.12186282 0.64220857
#> [2,] -1.079690762 2.131797718 0.110421189 -2.10066370 1.15655108
#> [3,] 0.139238115 0.231456363 1.195763621 -2.09087186 -0.17357348
#> [4,] -0.084748785 0.896945738 0.733309514 -1.29110001 -0.96456910
#> [5,] -0.666639615 -1.738792712 -0.004511692 1.07199152 1.85379902
#> [6,] -2.516089032 0.468847940 -0.021215500 0.89487015 1.68436511
#> [7,] -0.735146797 -0.544147673 0.138501733 -0.84263230 1.05731214
#> [8,] -1.020122263 -0.165414154 -1.070127070 0.04629749 0.81456163
#> [9,] 0.113554441 0.552166563 1.236062067 0.62336815 -1.19459693
#> [10,] -0.473790982 1.033330212 -0.539810095 0.48509195 -2.02885659
#> [11,] -0.408214704 -0.046178805 -0.296228915 0.53194447 2.12408654
#> [12,] -0.730433279 2.638707313 0.688218803 0.19742268 0.29921906
#> [13,] -0.221436599 0.589005749 -0.399007304 0.32656678 -0.82464305
#> [14,] -0.225816524 -0.202377334 -0.103484853 -1.44023199 1.52434568
#> [15,] -2.546881446 0.441560138 1.529534041 2.11470908 -0.61482041
#> [16,] 1.347001499 -0.100257702 -0.634636458 -0.18467231 -0.45976066
#> [17,] 0.616408146 -1.093403992 -0.226854589 0.49210433 0.44364099
#> [18,] 0.217564338 0.503247339 1.501084093 -1.36132699 -1.39598147
#> [19,] -0.804718830 0.949240091 -0.342463482 -0.36521756 -0.50247786
#> [20,] 0.689746778 0.382802056 -0.553727556 0.75612656 -1.06059878
#> [21,] -0.328672014 0.371578211 0.257626871 0.14650281 1.17460716
#> [22,] -0.164681577 0.157216402 0.613121759 0.26194725 0.33037146
#> [23,] -1.392028880 0.847939890 -0.652287367 -1.30615904 -0.28843322
#> [24,] 1.465824761 -1.283392657 -0.138281029 0.49216403 1.32156210
#> [25,] 0.048206808 1.158278608 -0.123638002 -0.51110400 -0.04547403
#> [26,] 1.908083835 -0.909106213 -0.964705216 -1.66057454 -0.21479390
#> [27,] 1.730944527 0.334990705 0.019043184 0.62237637 0.13480451
#> [28,] 0.058145837 0.775854082 1.691692353 0.01573101 -0.64499750
#> [29,] 0.645328162 0.237140152 0.885809071 -0.73388713 0.05707192
#> [30,] 1.725628987 -1.559870135 -0.729930145 0.27521114 0.93118467
#> [31,] -0.528966917 0.024326751 0.351884683 -1.85873859 0.20366345
#> [32,] 0.166392026 0.334635189 -0.247330893 2.07889037 1.62579612
#> [33,] -0.254723575 0.989410999 -0.163591832 -2.36088033 -0.59335596
#> [34,] 0.332782359 0.500375114 0.872360560 0.32713540 -0.30856908
#> [35,] 0.182432813 0.576907865 0.605938214 -0.22752273 1.33644833
#> [36,] 1.164593750 1.142690940 0.927240813 -1.18059531 -1.20646409
#> [37,] 0.593492851 -0.707447814 0.458044437 0.33042153 -1.40888181
#> [38,] -0.891779729 -0.618559641 -0.801114024 -0.40693478 0.64442462
#> [39,] 0.577253310 -0.008314196 -0.497123153 1.52522324 -0.40886743
#> [40,] -0.824905581 0.338919734 -1.212967815 -1.10318387 -0.97923637
#> [41,] -1.157253858 1.405406299 0.619712599 1.33689257 -0.92723122
#> [42,] 0.777998774 -0.865269852 1.359663258 0.55271040 0.79270412
#> [43,] -1.204221786 -0.873953957 -1.621064763 -1.05652642 -0.68909904
#> [44,] 0.306714102 0.774318201 0.079559499 -0.19018299 -0.04165164
#> [45,] -0.833658541 -0.401890242 0.512593137 -0.90922418 -0.78561878
#> [46,] 1.418141164 -0.215137831 -0.219101938 -2.00091551 0.43445546
#> [47,] 0.711786118 0.605256131 -0.825938695 1.90897087 -0.66556690
#> [48,] -0.402497723 -0.614567684 -1.013578244 1.56898021 0.44993613
#> [49,] 0.799362219 -0.724422931 1.282066246 0.14030123 -0.54804046
#> [50,] 0.426484436 -0.256942637 -0.212144499 -1.42055946 1.85693691
#> [51,] -1.169898670 -0.392072736 1.184877237 0.51306680 0.40207812
#> [52,] -0.206200160 -0.686701962 -0.650612437 -0.98746120 -1.86460319
#> [53,] -0.930610309 0.875840340 1.287367270 0.59722392 1.37493052
#> [54,] 0.449397566 -0.481057986 0.538933216 0.86786040 -0.07460351
#> [55,] -0.644806455 0.068357591 0.025540554 0.18925776 0.22779390
#> [56,] -0.231422690 0.137024893 1.163947533 -0.06930641 1.38865619
#> [57,] -1.236368011 -1.858768519 -0.367625732 1.07625934 0.40582213
#> [58,] -0.960955298 0.346276571 -0.363369570 1.07089132 2.56136760
#> [59,] 0.133956282 1.501633820 -0.006753365 0.42163338 -0.52633457
#> [60,] -0.999052723 -0.013738120 -0.100472499 -0.12454978 0.35240739
#> [61,] -0.141470691 -0.775245198 -0.148592078 0.93719207 0.47516114
#> [62,] 0.167329900 1.311158136 -0.182952755 0.20061913 0.79190511
#> [63,] -0.198788613 0.260524557 -0.858147838 1.44817884 0.83127103
#> [64,] -0.291207188 1.014017008 0.958159182 -1.28218257 0.54869793
#> [65,] -1.734397034 0.215594208 -0.839076942 -0.08455421 -1.24227498
#> [66,] -0.272728332 0.613404626 -0.759724279 1.10161395 -0.41337033
#> [67,] -1.799929482 1.445988481 -0.866514820 -0.69069958 0.07103869
#> [68,] 1.152740977 0.658085901 1.659025669 -1.72541539 -0.49427182
#> [69,] -1.003319486 0.375234496 0.556942277 0.56588569 0.32214327
#> [70,] 0.148210044 -0.674688276 0.601386130 -0.59615842 -0.79642913
#> [71,] 0.519496681 0.580480573 -1.969786821 -0.48934502 0.90711628
#> [72,] 0.005436294 -0.414229332 -0.380107749 0.76889336 0.20753915
#> [73,] 1.347020835 2.180503985 1.157773544 2.11461189 0.54784241
#> [74,] -0.847033417 0.342857644 1.389634654 0.04478098 -0.22169528
#> [75,] 0.443398017 -0.798024662 -2.011508425 0.94724107 -0.68179718
#> [76,] -0.977149468 1.641036166 -2.336308500 -2.12450871 1.45985707
#> [77,] 2.124491136 -0.460005112 -1.368459770 0.09042833 -0.86250789
#> [78,] 0.687698961 -1.586470989 -1.373094260 -1.73914879 -0.80038386
#> [79,] -0.343368220 -1.044915674 0.737388550 -1.70228833 -0.68514197
#> [80,] 0.785169473 0.041653666 0.454709342 1.40829640 1.82132263
#> [81,] -1.155577461 -0.432026377 1.776681328 0.69816811 -0.10474422
#> [82,] 1.508916346 0.458591148 0.194985588 0.57608544 1.11147453
#> [83,] -1.155496160 -0.229351986 -0.344612622 -0.53433606 -1.02752209
#> [84,] -1.556811810 -1.856962210 1.612676635 -1.28198635 -0.42412358
#> [85,] -0.055247925 -0.289674173 0.775709364 0.42961422 -0.15914942
#> [86,] 0.849193163 1.767141238 0.704279126 -1.12943768 0.65817084
#> [87,] -0.011096723 -0.442682897 1.072154216 2.41976368 -0.30346456
#> [88,] -0.760313832 -0.588864402 -0.214130770 0.07827610 0.69656280
#> [89,] 1.175799252 -0.120588258 0.363631064 0.31622523 0.11277226
#> [90,] 2.414449709 1.653061282 1.083563787 -1.57940021 0.18025656
#> [91,] -1.958512768 -0.871354569 -1.952276157 0.51890562 2.12120443
#> [92,] 1.473589893 0.780668271 0.015510179 1.13539238 -0.67724516
#> [93,] -0.474883961 -0.613877869 -0.870980920 0.31240446 0.97527964
#> [94,] 0.981170984 -0.327591311 -0.900934215 0.05554413 1.09733862
#> [95,] -1.824355220 -0.355216016 -0.776112068 1.56545282 1.66738580
#> [96,] -0.260610817 0.780934074 0.361430751 0.71883077 0.32411985
#> [97,] -0.958845313 0.608670172 -0.017349312 0.63432571 0.25078495
#> [98,] -0.490295710 1.076179017 -1.118195360 1.06590669 0.15330914
#> [99,] -1.077296714 1.065557652 0.586691375 0.34188855 0.71522931
#> [100,] 0.369425911 2.161646413 0.486195010 3.14336714 0.61054162
#> [101,] 1.216266481 0.070456482 0.473894456 0.34995083 -0.28181689
#> [102,] -0.493843236 -2.535225220 0.361427453 1.07333545 0.88599813
#> [103,] -0.227784721 -0.541334145 1.217683644 0.91643787 0.59412693
#> [104,] -1.111648887 -0.775939574 -0.673116960 0.71765690 0.70011828
#> [105,] 0.995833089 -0.295955418 -0.598848242 1.05548503 2.81491280
#> [106,] 0.561828891 1.151372905 0.735873147 0.35059220 0.27707810
#> [107,] -1.564278557 -0.568887868 0.601739651 -1.93603848 -0.51374124
#> [108,] 1.481077019 -0.805786340 1.108891940 -0.80074194 0.42221415
#> [109,] -1.169205150 0.492593862 -0.011390598 2.34293958 -0.26556434
#> [110,] -1.052013537 0.700758337 0.605035961 1.92087839 -0.77035422
#> [111,] -1.471845525 -0.243125292 1.123528153 -0.10953864 -0.37650065
#> [112,] -1.193436921 0.412481177 -0.705362054 -2.51282079 0.11022293
#> [113,] -0.347979425 0.498165309 1.762472072 -0.75962363 0.97256143
#> [114,] 0.532822543 0.367679115 -0.730020695 -1.50546286 0.35699402
#> [115,] -0.710988576 -0.570602869 -1.634157627 -1.67824571 -0.82416732
#> [116,] -0.198989138 0.738816518 -0.123993780 0.95009863 -0.15563068
#> [117,] -0.281373324 -2.268291326 0.204139470 0.12495359 -1.52971660
#> [118,] 1.170926275 0.614041040 -0.683239219 1.43839197 -0.20222751
#> [119,] 2.288733186 0.403635806 -1.016362272 -1.32535122 -1.31151723
#> [120,] -1.068856614 2.051579327 -0.671434166 -1.00393459 -1.39007894
#> x6
#> [1,] 0.962138142
#> [2,] -0.178134779
#> [3,] 0.003289601
#> [4,] 0.511615166
#> [5,] -0.456060688
#> [6,] -0.210904119
#> [7,] -0.572554869
#> [8,] -0.353336223
#> [9,] -1.049656024
#> [10,] 0.788385521
#> [11,] 0.619231580
#> [12,] -0.785768492
#> [13,] 1.781604052
#> [14,] -0.249928557
#> [15,] -0.301097397
#> [16,] -1.499332110
#> [17,] 0.034205348
#> [18,] -0.911047222
#> [19,] 0.351385587
#> [20,] 0.195414362
#> [21,] 0.859629927
#> [22,] -0.674155139
#> [23,] -1.662585651
#> [24,] 0.390045781
#> [25,] 0.441365682
#> [26,] 1.283965924
#> [27,] -0.163185802
#> [28,] 0.499569043
#> [29,] 0.460144067
#> [30,] -0.476282040
#> [31,] -1.556642390
#> [32,] -0.689022539
#> [33,] 0.148407389
#> [34,] 0.135429491
#> [35,] -1.372341840
#> [36,] -0.093103677
#> [37,] -1.118207934
#> [38,] 0.744827358
#> [39,] 0.077682586
#> [40,] -0.499928792
#> [41,] 0.611743479
#> [42,] -0.869034776
#> [43,] 1.166157990
#> [44,] -0.769731639
#> [45,] -1.807244367
#> [46,] -0.917465741
#> [47,] -0.356456665
#> [48,] -0.176553707
#> [49,] -0.799311276
#> [50,] -0.392796426
#> [51,] 0.273725082
#> [52,] 0.258141983
#> [53,] 0.110412064
#> [54,] -0.915161123
#> [55,] 0.212936650
#> [56,] 1.403335341
#> [57,] -0.808434809
#> [58,] 1.919533562
#> [59,] 0.865668884
#> [60,] -1.263907721
#> [61,] -0.956625877
#> [62,] -0.303370061
#> [63,] -0.002203605
#> [64,] -0.913071355
#> [65,] -0.649643176
#> [66,] 0.376311078
#> [67,] 0.277996383
#> [68,] -0.754720890
#> [69,] -0.473084718
#> [70,] -0.913950427
#> [71,] -2.603299709
#> [72,] -0.593467498
#> [73,] -0.638979895
#> [74,] -0.105916956
#> [75,] -0.980291155
#> [76,] 0.476235460
#> [77,] -1.412391284
#> [78,] 1.631003152
#> [79,] -1.127985882
#> [80,] 0.438451090
#> [81,] 1.900650234
#> [82,] -0.700760424
#> [83,] -0.371739384
#> [84,] -0.678503248
#> [85,] 0.101451284
#> [86,] 0.553279834
#> [87,] -1.522220996
#> [88,] -1.021549302
#> [89,] -1.390682138
#> [90,] 0.433339796
#> [91,] 0.369698592
#> [92,] -0.709493646
#> [93,] -1.018949403
#> [94,] -1.640746253
#> [95,] 0.682710512
#> [96,] -0.751876714
#> [97,] 0.716236912
#> [98,] 1.995001408
#> [99,] 1.346136378
#> [100,] -0.826907119
#> [101,] 1.138465168
#> [102,] -0.670743489
#> [103,] 0.288305337
#> [104,] 0.163840250
#> [105,] 0.543705814
#> [106,] 0.008960783
#> [107,] 0.291439729
#> [108,] 0.632100223
#> [109,] -2.122294725
#> [110,] -0.273116169
#> [111,] 0.379269651
#> [112,] 0.207411583
#> [113,] 0.130075483
#> [114,] 1.729015230
#> [115,] -0.131777384
#> [116,] 0.090180039
#> [117,] -0.122655945
#> [118,] 2.347985972
#> [119,] -0.622362195
#> [120,] 1.096080130
#>
#> $time
#> [1] 4.936751609 5.516385077 13.385710381 15.635809367 23.452096062
#> [6] 2.822469105 8.546377707 34.279468166 9.812183178 8.391416362
#> [11] 4.849622696 19.359421054 18.405488388 11.781124151 24.293544655
#> [16] 3.125722768 0.491530236 23.099733422 52.797889809 3.624531790
#> [21] 0.879900517 0.830538757 7.084386723 1.249764450 5.873960717
#> [26] 3.216452355 1.725259391 1.108818272 0.584268995 3.677324674
#> [31] 17.710100166 29.038253358 10.098133875 1.568787663 0.716951222
#> [36] 5.626208754 0.019957142 47.880577976 8.741046288 22.428305176
#> [41] 14.980036759 1.373946502 67.329386802 1.326827714 34.128322544
#> [46] 0.150728389 3.123093214 3.665713681 1.709637303 9.435513135
#> [51] 9.245330852 17.605376583 2.574465163 15.306641895 6.829018646
#> [56] 7.785840367 9.031298139 3.008732470 10.652280040 0.573365623
#> [61] 11.019062489 4.863416229 4.352653929 16.457719775 14.792467724
#> [66] 59.853267065 15.689490864 4.886552042 12.666771142 6.404760212
#> [71] 0.538400725 27.022404821 10.154293794 15.973802916 0.009122426
#> [76] 49.424027823 12.204867336 0.062702229 4.926715956 4.434407367
#> [81] 8.939466241 1.040075880 13.266593521 1.604360578 24.775995546
#> [86] 5.439994918 16.038272860 17.768320240 0.196885595 2.339793322
#> [91] 12.394830222 24.303424656 21.232719811 13.638228065 16.677692172
#> [96] 13.146576844 0.722168596 18.827794723 70.815290681 6.928499298
#> [101] 7.287743999 0.187341362 2.453581957 2.570306301 33.648896161
#> [106] 4.066202230 20.627933445 2.294389278 16.525187821 1.807963944
#> [111] 2.415036690 41.278396568 10.817429334 4.763817242 16.436503765
#> [116] 6.883017882 2.132509086 1.435819652 8.411551795 32.766008036
#>
#> $status
#> [1] 1 0 1 1 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 1 0 0 0 1 1 1 1 0 1 1 0 1 1 1 0 0 0
#> [38] 1 0 0 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 0 1 1 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1 1
#> [75] 1 0 1 1 1 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 1 0 1 0 1 1
#> [112] 0 1 0 1 0 1 0 1 1
#>
#> $y
#> [1] 4.936751609 5.516385077+ 13.385710381 15.635809367 23.452096062+
#> [6] 2.822469105+ 8.546377707+ 34.279468166+ 9.812183178+ 8.391416362
#> [11] 4.849622696 19.359421054+ 18.405488388+ 11.781124151 24.293544655+
#> [16] 3.125722768 0.491530236 23.099733422+ 52.797889809+ 3.624531790
#> [21] 0.879900517+ 0.830538757+ 7.084386723+ 1.249764450 5.873960717
#> [26] 3.216452355 1.725259391 1.108818272+ 0.584268995 3.677324674
#> [31] 17.710100166+ 29.038253358 10.098133875 1.568787663 0.716951222+
#> [36] 5.626208754+ 0.019957142+ 47.880577976 8.741046288+ 22.428305176+
#> [41] 14.980036759 1.373946502 67.329386802 1.326827714 34.128322544+
#> [46] 0.150728389+ 3.123093214 3.665713681 1.709637303 9.435513135
#> [51] 9.245330852+ 17.605376583 2.574465163+ 15.306641895 6.829018646
#> [56] 7.785840367+ 9.031298139 3.008732470 10.652280040+ 0.573365623+
#> [61] 11.019062489 4.863416229+ 4.352653929 16.457719775+ 14.792467724+
#> [66] 59.853267065 15.689490864+ 4.886552042 12.666771142+ 6.404760212+
#> [71] 0.538400725+ 27.022404821+ 10.154293794 15.973802916 0.009122426
#> [76] 49.424027823+ 12.204867336 0.062702229 4.926715956 4.434407367
#> [81] 8.939466241+ 1.040075880 13.266593521+ 1.604360578+ 24.775995546+
#> [86] 5.439994918 16.038272860 17.768320240+ 0.196885595 2.339793322
#> [91] 12.394830222 24.303424656 21.232719811+ 13.638228065 16.677692172+
#> [96] 13.146576844+ 0.722168596+ 18.827794723+ 70.815290681+ 6.928499298+
#> [101] 7.287743999 0.187341362 2.453581957+ 2.570306301+ 33.648896161
#> [106] 4.066202230 20.627933445+ 2.294389278 16.525187821+ 1.807963944
#> [111] 2.415036690 41.278396568+ 10.817429334 4.763817242+ 16.436503765
#> [116] 6.883017882+ 2.132509086 1.435819652+ 8.411551795 32.766008036
#>
#> $cvfit
#>
#> Call: (function (x, y, weights = NULL, offset = NULL, lambda = NULL, type.measure = c("default", "mse", "deviance", "class", "auc", "mae", "C"), nfolds = 10, foldid = NULL, alignment = c("lambda", "fraction"), grouped = TRUE, keep = FALSE, parallel = FALSE, gamma = c(0, 0.25, 0.5, 0.75, 1), relax = FALSE, trace.it = 0, ...) { type.measure = match.arg(type.measure) alignment = match.arg(alignment) if (!is.null(lambda) && length(lambda) < 2) stop("Need more than one value of lambda for cv.glmnet") if (!is.null(lambda) && alignment == "fraction") { warning("fraction of path alignment not available if lambda given as argument; switched to alignment=`lambda`") alignment = "lambda" } N = nrow(x) if (is.null(weights)) weights = rep(1, N) else weights = as.double(weights) y = drop(y) cv.call = glmnet.call = match.call(expand.dots = TRUE) which = match(c("type.measure", "nfolds", "foldid", "grouped", "keep"), names(glmnet.call), FALSE) if (any(which)) glmnet.call = glmnet.call[-which] glmnet.call[[1]] = as.name("glmnet") if (glmnet.control()$itrace) trace.it = 1 else { if (trace.it) { glmnet.control(itrace = 1) on.exit(glmnet.control(itrace = 0)) } } if (is.null(foldid)) foldid = sample(rep(seq(nfolds), length = N)) else nfolds = max(foldid) if (nfolds < 3) stop("nfolds must be bigger than 3; nfolds=10 recommended") if (relax) cv.relaxed.raw(x, y, weights, offset, lambda, type.measure, nfolds, foldid, alignment, grouped, keep, parallel, trace.it, glmnet.call, cv.call, gamma, ...) else cv.glmnet.raw(x, y, weights, offset, lambda, type.measure, nfolds, foldid, alignment, grouped, keep, parallel, trace.it, glmnet.call, cv.call, ...) })(x = structure(c(0.520589072918523, -1.07969076235228, 0.139238115019273, -0.0847487849485765, -0.666639615284596, -2.51608903200946, -0.735146797456677, -1.02012226313509, 0.113554441297307, -0.473790981840095, -0.408214704337928, -0.730433278593614, -0.221436599406174, -0.225816524428951, -2.5468814461283, 1.34700149929703, 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#>
#> Measure: Partial Likelihood Deviance
#>
#> Lambda Index Measure SE Nonzero
#> min 0.0358 26 4.869 0.4314 4
#> 1se 0.3666 1 5.146 0.4545 0
#>
#> $lambda
#> [1] 0.366566
#>
#> $lambda_choice
#> [1] "1se"
#>
#> $alpha
#> [1] 1
#>
#> $standardize
#> [1] TRUE
#>
#> $cv_type_measure
#> [1] "deviance"
#>
#> $cv_grouped
#> [1] TRUE
#>
#> $selection_ties
#> [1] "breslow"
#>
#> $refit_ties
#> [1] "efron"
#>
#> $beta_lasso
#> x1 x2 x3 x4 x5 x6
#> 0 0 0 0 0 0
#>
#> $support
#> integer(0)
#>
#> $selected
#> character(0)
#>
#> $refit
#> $refit$coef
#> numeric(0)
#>
#> $refit$fit
#> NULL
#>
#> $refit$support
#> integer(0)
#>
#> $refit$converged
#> [1] TRUE
#>
#>
#> $theta_refit
#> x1 x2 x3 x4 x5 x6
#> 0 0 0 0 0 0
#>
#> $coef
#> x1 x2 x3 x4 x5 x6
#> 0 0 0 0 0 0
#>
#> $se
#> x1 x2 x3 x4 x5 x6
#> 0 0 0 0 0 0
#>
#> $call
#> coxlasso_fit(X = X, time = time, status = status, lambda_choice = "1se")
#>
#> attr(,"class")
#> [1] "selSurv_fit"After fitting the Cox-Lasso, the main routes are:
# Full-model coefficient target: debiased Wald inference
db <- coxlasso_debias(fit)
# Full-model coefficient target: wild bootstrap and Efron's bootstrap
db_boot <- coxlasso_boot(
fit,
inference = "debiased",
type = c("wild", "efron"),
B = 500,
conf_level = 0.95,
seed = 2026
)
# Selected-submodel target: observed support held fixed
fixed <- coxlasso_boot(
fit,
inference = "conditional",
B = 500,
conf_level = 0.95,
seed = 2026
)
# Selection and inference on disjoint samples
split <- coxlasso_split(X, time, status, seed = 2026)
# Procedure-level re-selection
reselected <- coxlasso_boot_reselect(
X, time, status,
B = 500,
conf_level = 0.95,
methods = c("fixed_support", "reselect_fixed_lambda", "reselect_cv_min"),
seed = 2026
)These calls should not be interpreted as interchangeable confidence intervals. Debiased inference addresses a full-model coefficient target, fixed-support model-based bootstrap inference addresses coefficients of the selected working model, sample splitting uses a selected model obtained from an independent selection sample, and re-selection describes the repeated data-adaptive procedure.
summary(db)
summary(db_boot)
summary(fixed)
summary(split)
rs <- summary(reselected)
rs$diagnostics
rs$intervalsFor publication-scale bootstrap analyses, use substantially larger B than values used in package smoke tests and inspect method-specific diagnostics together with confidence intervals.