Orchestrates the cross-validation, metalearner optimization, and prediction for an ensemble of survival base learners.
Arguments
- time
Observed follow-up time.
- event
Observed event indicator.
- X
Training covariate data.frame.
- newdata
Test covariate data.frame for prediction (defaults to X).
- new.times
Times at which to obtain predicted survivals.
- event.library
Character vector of prediction algorithms for the event.
- cens.library
Character vector of prediction algorithms for censoring.
- id
Cluster identification variable.
- verbose
Logical. If TRUE, prints progress messages.
- control
List of control parameters for the Super Learner.
- cvControl
List of control parameters for cross-validation.
- obsWeights
Observation weights.
- metalearner
Character string specifying the optimizer (e.g., "least_squares").
- selection
Character. Specifies how the meta-learner combines the base models. Use
"ensemble"(default) to calculate a weighted average (convex combination) of the base learners. Use"best"to act as a Discrete Super Learner, which assigns a weight of 1.0 to the single model with the lowest cross-validated risk.- nFolds
Number of cross-validation folds (default: 10).
- parallel
Logical. If TRUE, uses future.apply for parallel execution.
Value
A list of class SuperSurv containing:
call: The matched function call.event.predict: Matrix of in-sample cross-validated survival predictions.cens.predict: Matrix of in-sample cross-validated censoring predictions.event.coef: Numeric vector of optimized ensemble weights for the event.cens.coef: Numeric vector of optimized ensemble weights for censoring.event.library.predict: 3D array of cross-validated predictions from individual event learners.event.libraryNames: Data frame detailing the algorithms and screeners used.event.fitLibrary: List of the fitted base learner models (ifsaveFitLibrary = TRUE).times: The time grid used for evaluation.
Examples
if (requireNamespace("glmnet", quietly = TRUE)) {
data("metabric", package = "SuperSurv")
dat <- metabric[1:80, ]
x_cols <- grep("^x", names(dat))[1:5]
X <- dat[, x_cols, drop = FALSE]
new.times <- seq(20, 120, by = 20)
fit <- SuperSurv(
time = dat$duration,
event = dat$event,
X = X,
newdata = X,
new.times = new.times,
event.library = c("surv.coxph", "surv.ridge"),
cens.library = c("surv.coxph"),
control = list(saveFitLibrary = TRUE)
)
fit$event.library.predict
}
#> , , 1
#>
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.9700650 0.8875504 0.7717069 0.72395128 0.61116122 0.59792836
#> [2,] 0.9805888 0.9259467 0.8460772 0.81192611 0.72790811 0.71770347
#> [3,] 0.9651618 0.8700719 0.7390725 0.68599088 0.56299045 0.54879396
#> [4,] 0.9761692 0.9096740 0.8141076 0.77386431 0.67653839 0.66488716
#> [5,] 0.9677792 0.8793698 0.7563380 0.70602374 0.58824219 0.57452850
#> [6,] 0.9736595 0.9005289 0.7964322 0.75297733 0.64890242 0.63654580
#> [7,] 0.9752753 0.9064089 0.8077728 0.76636555 0.66657105 0.65465936
#> [8,] 0.9561697 0.8386857 0.6823761 0.62102711 0.48377082 0.46840340
#> [9,] 0.9764098 0.9105543 0.8158199 0.77589374 0.67924465 0.66766530
#> [10,] 0.9545174 0.8330116 0.6723866 0.60971519 0.47040334 0.45489354
#> [11,] 0.9496885 0.8165930 0.6439281 0.57771768 0.43329593 0.41748179
#> [12,] 0.9672778 0.8775831 0.7530036 0.70214606 0.58332461 0.56951298
#> [13,] 0.9697557 0.8864400 0.7696110 0.72150125 0.60801129 0.59470999
#> [14,] 0.9735555 0.9001511 0.7957066 0.75212235 0.64777964 0.63539548
#> [15,] 0.9586661 0.8473134 0.6977180 0.63847950 0.50464581 0.48953379
#> [16,] 0.9704225 0.8888349 0.7741351 0.72679185 0.61482026 0.60166781
#> [17,] 0.9811924 0.9281857 0.8505282 0.81725373 0.73520113 0.72521559
#> [18,] 0.9333584 0.7628506 0.5554010 0.48045424 0.32714787 0.31129461
#> [19,] 0.9813461 0.9287565 0.8516648 0.81861531 0.73706901 0.72714012
#> [20,] 0.9689175 0.8834364 0.7639571 0.71490016 0.59955238 0.58607100
#> [21,] 0.9692508 0.8846300 0.7662012 0.71751872 0.60290303 0.58949234
#> [22,] 0.9683193 0.8812975 0.7599446 0.71022277 0.59358330 0.57997810
#> [23,] 0.9623370 0.8601194 0.7208298 0.66494912 0.53688027 0.52223852
#> [24,] 0.9634021 0.8638620 0.7276610 0.67281319 0.54658866 0.53210591
#> [25,] 0.7973732 0.4111682 0.1450374 0.09011233 0.02551431 0.02167484
#> [26,] 0.9722567 0.8954471 0.7867007 0.74152625 0.63392028 0.62120354
#> [27,] 0.9579379 0.8447899 0.6932117 0.63334333 0.49847092 0.48327923
#> [28,] 0.9704639 0.8889838 0.7744170 0.72712171 0.61524566 0.60210262
#> [29,] 0.9646462 0.8682491 0.7357129 0.68210604 0.55813781 0.54385435
#> [30,] 0.9683455 0.8813914 0.7601205 0.71042775 0.59384444 0.58024461
#> [31,] 0.9663015 0.8741115 0.7465473 0.69464975 0.57385833 0.55986347
#> [32,] 0.9640272 0.8660640 0.7316966 0.67746762 0.55236279 0.53797832
#> [33,] 0.9223241 0.7280600 0.5018412 0.42340470 0.26981489 0.25455020
#> [34,] 0.9587546 0.8476203 0.6982671 0.63910588 0.50540067 0.49029862
#> [35,] 0.9593479 0.8496809 0.7019602 0.64332199 0.51049155 0.49545809
#> [36,] 0.9771642 0.9133186 0.8212101 0.78228892 0.68779692 0.67644794
#> [37,] 0.9762705 0.9100446 0.8148282 0.77471822 0.67767664 0.66605557
#> [38,] 0.9566820 0.8404510 0.6855002 0.62457320 0.48798776 0.47266872
#> [39,] 0.9759839 0.9089965 0.8127908 0.77230437 0.67446074 0.66275466
#> [40,] 0.9780130 0.9164366 0.8273127 0.78954194 0.69754085 0.68646025
#> [41,] 0.9646555 0.8682817 0.7357731 0.68217558 0.55822454 0.54394261
#> [42,] 0.9754297 0.9069722 0.8088638 0.76765590 0.66828256 0.65641511
#> [43,] 0.9661080 0.8734247 0.7452736 0.69317273 0.57199944 0.55796943
#> [44,] 0.9718391 0.8939383 0.7838240 0.73814780 0.62952310 0.61670370
#> [45,] 0.9553965 0.8360271 0.6776857 0.61571067 0.47747225 0.46203564
#> [46,] 0.9723422 0.8957562 0.7872909 0.74221972 0.63482417 0.62212869
#> [47,] 0.9614940 0.8571658 0.7154632 0.65878384 0.52931104 0.51455081
#> [48,] 0.9723361 0.8957340 0.7872483 0.74216972 0.63475898 0.62206197
#> [49,] 0.9685384 0.8820807 0.7614125 0.71193326 0.59576377 0.58220349
#> [50,] 0.9382690 0.7787253 0.5808162 0.50801215 0.35617683 0.34020019
#> [51,] 0.9471122 0.8079325 0.6291842 0.56127587 0.41463987 0.39872574
#> [52,] 0.9653626 0.8707827 0.7403849 0.68750957 0.56489142 0.55072952
#> [53,] 0.9758352 0.9084530 0.8117355 0.77105471 0.67279791 0.66104815
#> [54,] 0.9509140 0.8207366 0.6510477 0.58569062 0.44244383 0.42669194
#> [55,] 0.9678553 0.8796411 0.7568451 0.70661392 0.58899189 0.57529330
#> [56,] 0.9697381 0.8863768 0.7694918 0.72136191 0.60783232 0.59452716
#> [57,] 0.9689737 0.8836378 0.7643356 0.71534167 0.60011687 0.58664734
#> [58,] 0.9275221 0.7442981 0.5264748 0.44946616 0.29553446 0.27994553
#> [59,] 0.9556087 0.8367562 0.6789702 0.61716576 0.47919332 0.46377525
#> [60,] 0.9696117 0.8859235 0.7686371 0.72036333 0.60655020 0.59321742
#> [61,] 0.9713819 0.8922887 0.7806850 0.73446496 0.62474174 0.61181230
#> [62,] 0.9774237 0.9142711 0.8230718 0.78450025 0.69076270 0.67949473
#> [63,] 0.9170021 0.7117092 0.4776788 0.39814641 0.24566747 0.23080489
#> [64,] 0.9685732 0.8822052 0.7616459 0.71220527 0.59611076 0.58255766
#> [65,] 0.9401464 0.7848591 0.5908008 0.51892086 0.36790049 0.35190425
#> [66,] 0.9662002 0.8737517 0.7458800 0.69387585 0.57288409 0.55887077
#> [67,] 0.9642751 0.8669386 0.7333026 0.67932170 0.55466872 0.54032425
#> [68,] 0.9618095 0.8582704 0.7174676 0.66108521 0.53213215 0.51741552
#> [69,] 0.9527257 0.8268913 0.6617005 0.59766031 0.45630036 0.44065885
#> [70,] 0.9758005 0.9083262 0.8114893 0.77076319 0.67241022 0.66065029
#> [71,] 0.9771012 0.9130877 0.8207591 0.78175347 0.68707946 0.67571096
#> [72,] 0.9763523 0.9103439 0.8154105 0.77540836 0.67859705 0.66700045
#> [73,] 0.9739871 0.9017188 0.7987201 0.75567457 0.65244886 0.64017981
#> [74,] 0.9735417 0.9001012 0.7956107 0.75200936 0.64763131 0.63524352
#> [75,] 0.9671950 0.8772880 0.7524536 0.70150681 0.58251530 0.56868774
#> [76,] 0.9506040 0.8196868 0.6492399 0.58366412 0.44011245 0.42434389
#> [77,] 0.9764282 0.9106218 0.8159514 0.77604964 0.67945270 0.66787888
#> [78,] 0.9670820 0.8768859 0.7517045 0.70063645 0.58141400 0.56756483
#> [79,] 0.9740552 0.9019662 0.7991964 0.75623629 0.65318828 0.64093759
#> [80,] 0.9646597 0.8682968 0.7358007 0.68220756 0.55826443 0.54398321
#>
#> , , 2
#>
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] 0.9643506 0.8686855 0.7472102 0.6979231 0.5834153 0.5700587
#> [2,] 0.9679272 0.8812466 0.7697494 0.7239963 0.6163733 0.6036863
#> [3,] 0.9627565 0.8631301 0.7373522 0.6865768 0.5692622 0.5556427
#> [4,] 0.9650443 0.8711114 0.7515362 0.7029133 0.5896765 0.5764409
#> [5,] 0.9626628 0.8628043 0.7367763 0.6859150 0.5684402 0.5548059
#> [6,] 0.9646004 0.8695584 0.7487654 0.6997162 0.5856626 0.5723490
#> [7,] 0.9648457 0.8704163 0.7502954 0.7014813 0.5878775 0.5746068
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#> [11,] 0.9597442 0.8527041 0.7190342 0.6655877 0.5433875 0.5293277
#> [12,] 0.9623768 0.8618105 0.7350207 0.6838985 0.5659382 0.5522592
#> [13,] 0.9638129 0.8668084 0.7438719 0.6940767 0.5786045 0.5651568
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#> [59,] 0.9607306 0.8561076 0.7249878 0.6723959 0.5517365 0.5378130
#> [60,] 0.9631816 0.8646087 0.7399695 0.6895858 0.5730043 0.5594528
#> [61,] 0.9636388 0.8662016 0.7427943 0.6928361 0.5770556 0.5635790
#> [62,] 0.9660172 0.8745221 0.7576401 0.7099657 0.5985630 0.5855043
#> [63,] 0.9554108 0.8378700 0.6933815 0.6364049 0.5080836 0.4935103
#> [64,] 0.9632639 0.8648952 0.7404772 0.6901697 0.5737314 0.5601932
#> [65,] 0.9587925 0.8494296 0.7133302 0.6590774 0.5354434 0.5212591
#> [66,] 0.9623250 0.8616307 0.7347032 0.6835339 0.5654862 0.5517992
#> [67,] 0.9618145 0.8598595 0.7315804 0.6799500 0.5610496 0.5472847
#> [68,] 0.9620640 0.8607249 0.7331054 0.6816997 0.5632142 0.5494871
#> [69,] 0.9600527 0.8537674 0.7208915 0.6677102 0.5459857 0.5319678
#> [70,] 0.9655702 0.8729537 0.7548301 0.7067174 0.5944644 0.5813234
#> [71,] 0.9648628 0.8704762 0.7504024 0.7016046 0.5880324 0.5747647
#> [72,] 0.9650724 0.8712097 0.7517118 0.7031159 0.5899312 0.5767006
#> [73,] 0.9647693 0.8701489 0.7498184 0.7009308 0.5871865 0.5739024
#> [74,] 0.9643169 0.8685679 0.7470009 0.6976817 0.5831131 0.5697506
#> [75,] 0.9634945 0.8656985 0.7419014 0.6918084 0.5757736 0.5622732
#> [76,] 0.9599247 0.8533263 0.7201206 0.6668291 0.5449067 0.5308713
#> [77,] 0.9659514 0.8742909 0.7572256 0.7094864 0.5979577 0.5848867
#> [78,] 0.9635080 0.8657456 0.7419851 0.6919047 0.5758938 0.5623955
#> [79,] 0.9654354 0.8724810 0.7539843 0.7057402 0.5932333 0.5800678
#> [80,] 0.9624487 0.8620602 0.7354617 0.6844048 0.5665661 0.5528983
#>