Cross-Validated GLM with Elastic Net Regularization Survival Learner
Source:R/learner_glmnet_surv_cv_glmnet.R
mlr_learners_surv.cv_glmnet.RdGeneralized linear models with elastic net regularization.
Calls glmnet::cv.glmnet() from package glmnet.
Initial parameter values
familyis set to"cox"and cannot be changed.cox.tiesis initialized to"breslow"to keep the tie-handling behavior of earlier glmnet versions, and to silence the glmnet v5.0 warning about the upcoming default change to"efron".
Prediction types
This learner returns three prediction types:
lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated usingglmnet::predict.cv.glmnet().crank: same aslp.distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated usingglmnet:::survfit.cv.glmnet(). Parametersstypeandctyperelate to howlppredictions are transformed into survival predictions and are described insurvival::survfit.coxph(). By default the Breslow estimator is used for computing the baseline hazard.
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet
Parameters
| Id | Type | Default | Levels | Range |
| lambda | untyped | NULL | - | |
| type.measure | character | deviance | deviance, C | - |
| nfolds | integer | 10 | \([3, \infty)\) | |
| foldid | untyped | NULL | - | |
| alignment | character | lambda | lambda, fraction | - |
| grouped | logical | TRUE | TRUE, FALSE | - |
| keep | logical | FALSE | TRUE, FALSE | - |
| parallel | logical | FALSE | TRUE, FALSE | - |
| gamma | untyped | c(0, 0.25, 0.5, 0.75, 1) | - | |
| relax | logical | FALSE | TRUE, FALSE | - |
| trace.it | integer | 0 | \([0, 1]\) | |
| alpha | numeric | 1 | \([0, 1]\) | |
| nlambda | integer | 100 | \([1, \infty)\) | |
| lambda.min.ratio | numeric | - | \([0, 1]\) | |
| standardize | logical | TRUE | TRUE, FALSE | - |
| intercept | logical | TRUE | TRUE, FALSE | - |
| exclude | untyped | NULL | - | |
| penalty.factor | untyped | - | - | |
| lower.limits | untyped | -Inf | - | |
| upper.limits | untyped | Inf | - | |
| cox.ties | character | breslow | breslow, efron | - |
| maxp | integer | - | \([1, \infty)\) | |
| path | logical | FALSE | TRUE, FALSE | - |
| fdev | numeric | 1e-05 | \([0, 1]\) | |
| devmax | numeric | 0.999 | \([0, 1]\) | |
| eps | numeric | 1e-06 | \([0, 1]\) | |
| big | numeric | 9.9e+35 | \((-\infty, \infty)\) | |
| mnlam | integer | 5 | \((-\infty, \infty)\) | |
| pmin | numeric | 1e-09 | \([0, 1]\) | |
| exmx | numeric | 250 | \((-\infty, \infty)\) | |
| prec | numeric | 1e-10 | \((-\infty, \infty)\) | |
| mxit | integer | 100 | \([1, \infty)\) | |
| epsnr | numeric | 1e-06 | \([0, 1]\) | |
| mxitnr | integer | 25 | \([1, \infty)\) | |
| thresh | numeric | 1e-07 | \([0, \infty)\) | |
| maxit | integer | 100000 | \([1, \infty)\) | |
| dfmax | integer | NULL | \([0, \infty)\) | |
| pmax | integer | NULL | \([0, \infty)\) | |
| s | numeric | lambda.1se | \([0, \infty)\) | |
| predict.gamma | numeric | gamma.1se | \([0, 1]\) | |
| exact | logical | FALSE | TRUE, FALSE | - |
| stype | integer | 2 | \([1, 2]\) | |
| ctype | integer | - | \([1, 2]\) | |
| use_pred_offset | logical | - | TRUE, FALSE | - |
Offset
If a Task contains a column with the offset role, it is automatically
incorporated during training via the offset argument in glmnet::glmnet().
During prediction, the offset column from the test set is used only if
use_pred_offset = TRUE (default), passed via the newoffset argument in glmnet::predict.glmnet().
Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied,
effectively disabling the offset adjustment during prediction.
References
Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01 .
See also
as.data.table(mlr_learners)for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html#sec-learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCVGlmnet
Methods
LearnerSurvCVGlmnet$selected_features()
Returns the set of selected features as reported by glmnet::predict.glmnet()
with type set to "nonzero".
Arguments
lambda(
numeric(1))
Customlambda, defaults to the active lambda depending on parameter set.
Returns
(character()) of feature names.
Examples
# Define the Learner
learner = lrn("surv.cv_glmnet")
print(learner)
#>
#> ── <LearnerSurvCVGlmnet> (surv.cv_glmnet): Regularized Generalized Linear Model
#> • Model: -
#> • Parameters: cox.ties=breslow, use_pred_offset=TRUE
#> • Packages: mlr3, mlr3proba, mlr3extralearners, and glmnet
#> • Predict Types: [crank], distr, and lp
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: offset, selected_features, and weights
#> • Other settings: use_weights = 'use', predict_raw = 'FALSE'
# Define a Task
task = tsk("grace")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> $model
#>
#> Call: glmnet::cv.glmnet(x = data, y = target, cox.ties = "breslow", family = "cox")
#>
#> Measure: Partial Likelihood Deviance
#>
#> Lambda Index Measure SE Nonzero
#> min 0.00325 44 2.858 0.1829 6
#> 1se 0.08446 9 3.031 0.1803 3
#>
#> $x
#> age los revasc revascdays stchange sysbp
#> [1,] 28 9 0 180 1 107
#> [2,] 32 5 1 0 1 121
#> [3,] 35 5 1 2 0 172
#> [4,] 34 5 0 5 0 120
#> [5,] 35 2 1 1 1 112
#> [6,] 37 9 0 180 1 151
#> [7,] 38 13 1 0 1 161
#> [8,] 38 2 0 115 0 150
#> [9,] 36 1 0 180 1 155
#> [10,] 35 0 0 180 1 119
#> [11,] 38 12 1 8 1 120
#> [12,] 33 6 1 1 1 115
#> [13,] 40 12 1 9 0 153
#> [14,] 42 3 1 1 1 130
#> [15,] 38 5 1 3 0 125
#> [16,] 42 2 0 2 0 140
#> [17,] 40 11 1 10 1 120
#> [18,] 41 2 1 1 0 166
#> [19,] 40 1 1 0 1 145
#> [20,] 43 4 1 0 1 130
#> [21,] 42 15 1 13 1 125
#> [22,] 40 3 1 1 0 170
#> [23,] 43 2 1 1 1 116
#> [24,] 41 10 1 8 0 150
#> [25,] 41 13 1 1 0 140
#> [26,] 45 9 1 7 0 110
#> [27,] 44 2 1 1 1 150
#> [28,] 43 2 0 180 1 140
#> [29,] 46 15 0 180 0 120
#> [30,] 46 2 1 1 0 126
#> [31,] 44 3 1 0 1 180
#> [32,] 43 29 0 180 1 180
#> [33,] 43 10 0 180 0 185
#> [34,] 47 6 1 0 1 116
#> [35,] 44 0 1 0 1 96
#> [36,] 49 5 0 73 1 136
#> [37,] 45 5 0 5 0 141
#> [38,] 44 4 1 0 1 114
#> [39,] 44 9 1 8 1 135
#> [40,] 46 4 0 180 1 121
#> [41,] 44 2 0 180 0 142
#> [42,] 46 15 0 180 1 120
#> [43,] 47 3 1 1 1 120
#> [44,] 48 3 0 180 0 154
#> [45,] 48 12 1 11 0 200
#> [46,] 47 5 1 3 1 130
#> [47,] 46 3 1 0 1 119
#> [48,] 47 10 0 10 1 140
#> [49,] 47 7 0 180 0 145
#> [50,] 50 6 1 2 1 140
#> [51,] 49 7 1 7 1 110
#> [52,] 46 3 1 1 1 140
#> [53,] 46 9 1 9 1 122
#> [54,] 50 7 0 180 1 110
#> [55,] 51 1 0 1 1 145
#> [56,] 47 2 0 180 0 150
#> [57,] 49 23 0 179 1 112
#> [58,] 52 2 0 180 1 170
#> [59,] 50 4 0 4 1 100
#> [60,] 51 3 1 2 0 113
#> [61,] 50 9 0 180 0 130
#> [62,] 49 7 1 4 1 90
#> [63,] 47 6 0 180 1 162
#> [64,] 51 8 0 180 1 140
#> [65,] 52 2 0 180 0 155
#> [66,] 46 3 0 180 1 120
#> [67,] 48 7 1 0 1 110
#> [68,] 48 17 1 10 0 111
#> [69,] 52 4 1 4 0 152
#> [70,] 49 9 1 3 0 102
#> [71,] 49 15 0 180 1 160
#> [72,] 53 5 0 180 1 140
#> [73,] 54 17 1 12 1 102
#> [74,] 54 6 1 3 0 129
#> [75,] 51 3 1 1 0 140
#> [76,] 50 14 1 13 0 170
#> [77,] 53 8 1 7 0 160
#> [78,] 48 3 1 2 0 150
#> [79,] 51 25 1 1 0 202
#> [80,] 49 5 1 2 1 150
#> [81,] 53 4 0 4 0 140
#> [82,] 52 14 1 7 1 200
#> [83,] 48 11 1 10 0 120
#> [84,] 53 4 1 0 1 156
#> [85,] 51 13 0 99 1 160
#> [86,] 49 16 0 16 0 125
#> [87,] 52 7 1 2 0 154
#> [88,] 54 9 1 1 0 130
#> [89,] 55 4 1 2 0 150
#> [90,] 52 4 0 180 1 180
#> [91,] 51 13 1 11 0 145
#> [92,] 50 5 1 4 1 150
#> [93,] 54 4 1 0 1 121
#> [94,] 55 28 1 13 1 160
#> [95,] 49 6 1 0 1 130
#> [96,] 49 1 0 1 1 110
#> [97,] 50 7 1 1 0 156
#> [98,] 50 7 1 0 1 127
#> [99,] 56 4 1 1 1 130
#> [100,] 52 5 0 175 1 117
#> [101,] 55 1 0 180 0 127
#> [102,] 55 2 0 2 0 145
#> [103,] 54 7 1 0 1 100
#> [104,] 56 3 0 180 1 193
#> [105,] 55 5 1 4 1 120
#> [106,] 52 8 0 180 0 119
#> [107,] 54 3 0 180 1 180
#> [108,] 55 6 0 180 0 170
#> [109,] 52 16 0 16 0 152
#> [110,] 53 10 1 9 0 172
#> [111,] 52 16 1 14 0 170
#> [112,] 53 15 0 15 1 90
#> [113,] 53 4 0 180 1 150
#> [114,] 55 6 0 180 1 100
#> [115,] 54 12 1 0 1 190
#> [116,] 54 3 0 180 0 128
#> [117,] 56 3 0 8 1 139
#> [118,] 55 1 0 2 0 130
#> [119,] 54 7 1 2 0 129
#> [120,] 52 9 1 3 0 170
#> [121,] 57 5 1 3 1 138
#> [122,] 57 1 0 1 1 100
#> [123,] 56 4 1 0 1 140
#> [124,] 52 2 0 180 0 140
#> [125,] 55 11 1 7 0 104
#> [126,] 53 3 1 0 1 200
#> [127,] 57 10 0 180 1 170
#> [128,] 58 8 0 8 1 130
#> [129,] 54 5 0 180 1 108
#> [130,] 55 3 1 1 1 156
#> [131,] 57 0 0 0 1 150
#> [132,] 53 21 1 13 1 130
#> [133,] 59 3 1 1 0 172
#> [134,] 57 4 0 180 1 119
#> [135,] 58 6 1 0 1 90
#> [136,] 54 17 1 8 1 227
#> [137,] 55 9 1 2 1 147
#> [138,] 55 13 0 166 1 140
#> [139,] 56 5 0 5 1 150
#> [140,] 57 4 1 2 1 185
#> [141,] 53 4 0 147 1 145
#> [142,] 53 7 1 0 1 120
#> [143,] 57 11 1 10 1 129
#> [144,] 55 3 1 2 0 140
#> [145,] 54 7 1 0 1 141
#> [146,] 56 4 0 4 0 164
#> [147,] 58 1 1 1 1 200
#> [148,] 56 7 1 5 1 120
#> [149,] 60 11 1 9 0 106
#> [150,] 59 3 0 180 0 120
#> [151,] 58 4 1 0 1 160
#> [152,] 57 2 0 2 1 120
#> [153,] 60 5 1 1 0 138
#> [154,] 57 5 0 180 1 130
#> [155,] 58 11 1 9 1 124
#> [156,] 55 5 1 0 1 160
#> [157,] 58 26 1 0 1 189
#> [158,] 61 9 0 9 1 160
#> [159,] 58 4 1 3 0 120
#> [160,] 59 2 1 1 0 140
#> [161,] 58 8 0 161 1 140
#> [162,] 58 14 1 6 0 190
#> [163,] 61 20 1 13 0 130
#> [164,] 57 13 1 10 0 110
#> [165,] 57 2 1 0 1 116
#> [166,] 58 10 0 10 1 150
#> [167,] 57 4 1 3 0 138
#> [168,] 56 14 0 45 0 130
#> [169,] 58 19 1 13 1 140
#> [170,] 56 18 1 11 1 165
#> [171,] 59 9 1 0 1 80
#> [172,] 55 9 1 7 1 135
#> [173,] 61 4 1 0 1 115
#> [174,] 56 8 1 8 0 120
#> [175,] 61 13 1 12 1 130
#> [176,] 59 11 1 8 1 190
#> [177,] 57 1 0 1 0 126
#> [178,] 57 15 1 13 1 110
#> [179,] 59 5 1 2 0 182
#> [180,] 58 5 1 1 1 135
#> [181,] 61 8 0 77 0 120
#> [182,] 61 13 0 13 0 210
#> [183,] 62 7 1 2 1 180
#> [184,] 57 3 1 0 0 100
#> [185,] 61 18 0 170 0 140
#> [186,] 61 28 1 7 0 133
#> [187,] 58 8 1 3 1 150
#> [188,] 61 7 0 7 1 150
#> [189,] 61 6 0 6 0 134
#> [190,] 59 13 1 2 0 198
#> [191,] 62 4 1 0 0 160
#> [192,] 60 17 1 8 1 140
#> [193,] 58 3 1 0 1 146
#> [194,] 62 4 1 3 0 173
#> [195,] 58 2 0 30 0 202
#> [196,] 59 1 0 180 0 155
#> [197,] 63 6 0 28 1 120
#> [198,] 61 5 0 5 1 110
#> [199,] 58 11 1 9 0 179
#> [200,] 57 2 1 1 0 159
#> [201,] 63 3 1 1 0 180
#> [202,] 63 1 0 180 1 130
#> [203,] 63 4 1 3 0 222
#> [204,] 62 3 0 180 1 105
#> [205,] 63 4 0 180 1 190
#> [206,] 63 15 1 10 1 126
#> [207,] 60 18 1 13 0 132
#> [208,] 59 8 0 180 1 140
#> [209,] 61 9 1 9 1 150
#> [210,] 58 9 1 9 0 110
#> [211,] 62 7 0 7 0 150
#> [212,] 59 1 0 22 1 162
#> [213,] 60 7 1 5 1 141
#> [214,] 60 7 0 7 0 140
#> [215,] 59 5 1 1 0 148
#> [216,] 60 7 1 1 1 90
#> [217,] 65 13 0 180 1 100
#> [218,] 63 1 0 1 0 162
#> [219,] 63 1 0 1 0 130
#> [220,] 61 15 1 13 0 170
#> [221,] 59 4 0 4 0 149
#> [222,] 62 6 0 6 0 120
#> [223,] 63 12 1 10 0 200
#> [224,] 59 10 0 180 1 130
#> [225,] 60 8 0 17 1 130
#> [226,] 61 6 1 1 1 117
#> [227,] 64 12 1 11 0 160
#> [228,] 66 1 1 0 1 120
#> [229,] 64 6 1 0 1 140
#> [230,] 63 10 1 0 1 148
#> [231,] 63 14 1 9 0 123
#> [232,] 65 36 1 11 0 140
#> [233,] 63 4 1 3 0 162
#> [234,] 66 3 1 1 0 127
#> [235,] 61 10 1 2 1 194
#> [236,] 63 12 1 9 0 114
#> [237,] 63 7 0 180 0 120
#> [238,] 66 5 1 0 1 110
#> [239,] 65 8 1 0 0 168
#> [240,] 65 10 1 8 1 120
#> [241,] 64 0 0 0 1 90
#> [242,] 64 9 0 180 0 150
#> [243,] 65 3 0 180 1 190
#> [244,] 64 7 0 180 1 120
#> [245,] 66 6 1 1 1 130
#> [246,] 63 12 0 12 1 150
#> [247,] 62 3 1 1 1 199
#> [248,] 65 6 0 9 0 112
#> [249,] 65 3 1 0 1 80
#> [250,] 63 5 1 4 0 170
#> [251,] 63 2 1 1 0 180
#> [252,] 62 13 1 11 0 180
#> [253,] 67 11 0 11 1 100
#> [254,] 66 18 1 5 0 142
#> [255,] 62 9 0 180 0 145
#> [256,] 61 14 1 5 0 140
#> [257,] 63 9 1 8 1 160
#> [258,] 63 3 1 2 0 120
#> [259,] 63 2 1 0 0 140
#> [260,] 64 19 1 8 1 160
#> [261,] 65 8 1 0 1 140
#> [262,] 67 6 0 180 1 170
#> [263,] 65 15 1 11 1 160
#> [264,] 64 6 1 0 1 125
#> [265,] 66 7 1 0 1 115
#> [266,] 66 13 1 0 0 118
#> [267,] 65 3 0 3 0 105
#> [268,] 64 10 1 9 1 110
#> [269,] 63 7 1 0 0 162
#> [270,] 67 8 1 1 1 130
#> [271,] 68 5 0 5 1 90
#> [272,] 63 10 0 16 1 160
#> [273,] 66 14 0 180 0 130
#> [274,] 68 18 0 180 1 260
#> [275,] 63 8 1 1 1 162
#> [276,] 65 18 1 3 0 120
#> [277,] 63 1 1 0 1 155
#> [278,] 63 10 0 18 1 130
#> [279,] 67 11 0 11 0 150
#> [280,] 68 11 0 180 0 160
#> [281,] 68 14 0 79 0 172
#> [282,] 65 15 1 12 1 150
#> [283,] 66 11 1 0 0 100
#> [284,] 65 4 1 2 1 145
#> [285,] 69 12 0 15 1 140
#> [286,] 63 2 0 180 0 150
#> [287,] 65 11 1 6 0 130
#> [288,] 65 8 1 0 1 90
#> [289,] 66 3 0 3 1 138
#> [290,] 69 1 1 0 0 170
#> [291,] 67 1 0 180 1 160
#> [292,] 65 1 1 0 0 133
#> [293,] 67 7 1 4 1 130
#> [294,] 67 2 0 180 0 184
#> [295,] 65 6 0 6 0 80
#> [296,] 65 10 1 1 1 148
#> [297,] 66 19 1 12 1 150
#> [298,] 67 12 1 12 0 160
#> [299,] 69 6 0 99 1 140
#> [300,] 64 4 0 179 0 160
#> [301,] 66 4 0 180 1 130
#> [302,] 70 15 1 12 1 132
#> [303,] 64 11 0 11 0 125
#> [304,] 64 4 0 180 1 140
#> [305,] 67 2 0 18 0 131
#> [306,] 66 4 0 180 0 177
#> [307,] 68 4 1 0 1 160
#> [308,] 65 13 1 12 1 130
#> [309,] 69 17 1 10 0 140
#> [310,] 69 8 0 93 0 140
#> [311,] 65 6 0 101 1 115
#> [312,] 68 4 0 4 1 190
#> [313,] 71 3 0 5 0 112
#> [314,] 68 7 0 150 0 210
#> [315,] 71 20 1 0 1 160
#> [316,] 67 2 0 180 0 128
#> [317,] 66 9 1 3 1 151
#> [318,] 66 1 1 1 1 165
#> [319,] 70 4 1 0 1 180
#> [320,] 69 8 0 180 1 153
#> [321,] 70 14 0 171 0 166
#> [322,] 67 10 1 9 0 200
#> [323,] 69 3 1 2 0 151
#> [324,] 67 14 1 13 0 130
#> [325,] 69 8 0 180 1 180
#> [326,] 71 7 0 7 0 230
#> [327,] 71 3 0 103 0 133
#> [328,] 69 3 0 3 1 130
#> [329,] 70 22 1 13 0 103
#> [330,] 68 6 0 180 0 145
#> [331,] 69 8 1 5 1 195
#> [332,] 69 6 1 4 1 174
#> [333,] 72 7 0 7 1 110
#> [334,] 69 8 1 7 1 108
#> [335,] 67 3 0 180 0 110
#> [336,] 66 2 1 1 0 123
#> [337,] 69 19 0 180 0 130
#> [338,] 69 11 1 0 1 120
#> [339,] 66 2 0 180 0 130
#> [340,] 67 7 1 4 0 122
#> [341,] 69 4 1 3 0 132
#> [342,] 68 2 0 7 1 130
#> [343,] 67 13 1 9 0 130
#> [344,] 70 3 0 123 0 130
#> [345,] 68 3 0 19 0 135
#> [346,] 67 12 1 8 0 120
#> [347,] 67 1 0 1 1 60
#> [348,] 72 13 1 11 1 195
#> [349,] 68 10 1 8 1 160
#> [350,] 66 24 1 13 0 130
#> [351,] 70 35 1 0 1 105
#> [352,] 72 30 1 0 1 145
#> [353,] 70 7 0 7 0 102
#> [354,] 71 6 0 9 0 120
#> [355,] 72 19 1 8 0 120
#> [356,] 72 12 1 10 0 170
#> [357,] 67 8 0 180 1 170
#> [358,] 67 5 1 0 1 147
#> [359,] 67 9 0 180 0 158
#> [360,] 70 5 0 180 0 150
#> [361,] 72 2 0 2 1 100
#> [362,] 72 6 1 5 0 115
#> [363,] 71 1 0 173 1 188
#> [364,] 68 23 0 180 1 220
#> [365,] 70 3 0 180 0 121
#> [366,] 71 3 1 2 0 150
#> [367,] 68 4 1 3 0 210
#> [368,] 71 5 0 180 0 191
#> [369,] 73 6 0 180 1 117
#> [370,] 69 16 1 10 1 140
#> [371,] 69 8 1 1 0 164
#> [372,] 70 4 0 180 0 180
#> [373,] 69 1 1 0 0 155
#> [374,] 73 6 1 0 1 270
#> [375,] 72 8 1 1 1 150
#> [376,] 71 2 1 0 1 180
#> [377,] 73 7 0 7 1 140
#> [378,] 68 15 1 13 1 130
#> [379,] 70 3 0 3 1 159
#> [380,] 73 0 0 180 1 161
#> [381,] 71 3 1 1 0 150
#> [382,] 74 20 0 20 1 180
#> [383,] 71 20 1 10 0 140
#> [384,] 74 0 1 0 1 90
#> [385,] 73 3 1 0 1 136
#> [386,] 70 5 1 0 1 190
#> [387,] 71 17 1 11 0 160
#> [388,] 71 8 1 7 0 149
#> [389,] 71 3 1 2 1 190
#> [390,] 73 10 1 8 0 106
#> [391,] 69 12 1 1 1 149
#> [392,] 73 4 0 58 1 160
#> [393,] 72 5 1 3 1 160
#> [394,] 70 3 0 180 1 154
#> [395,] 73 6 0 180 0 110
#> [396,] 72 15 1 0 1 150
#> [397,] 71 7 1 2 0 143
#> [398,] 73 17 1 11 0 140
#> [399,] 71 13 1 8 0 121
#> [400,] 69 2 1 1 1 80
#> [401,] 72 10 1 8 1 153
#> [402,] 69 7 0 180 1 144
#> [403,] 72 15 1 13 0 156
#> [404,] 70 8 0 8 0 120
#> [405,] 75 1 0 1 0 133
#> [406,] 73 10 1 9 1 146
#> [407,] 72 10 1 9 1 160
#> [408,] 71 2 0 10 1 112
#> [409,] 73 1 0 1 1 80
#> [410,] 75 13 1 1 1 130
#> [411,] 71 11 1 8 0 110
#> [412,] 70 7 1 4 0 184
#> [413,] 72 1 1 1 0 168
#> [414,] 73 10 0 180 0 162
#> [415,] 72 11 0 11 1 140
#> [416,] 70 3 0 3 0 150
#> [417,] 73 5 1 3 1 112
#> [418,] 76 25 1 12 1 170
#> [419,] 73 12 1 12 1 140
#> [420,] 72 2 0 180 0 120
#> [421,] 75 1 0 180 1 140
#> [422,] 71 3 1 0 0 144
#> [423,] 73 5 0 180 0 126
#> [424,] 73 4 0 180 0 124
#> [425,] 74 34 1 8 1 233
#> [426,] 76 3 1 0 1 120
#> [427,] 72 5 0 180 0 154
#> [428,] 72 3 0 180 0 160
#> [429,] 76 5 0 5 1 130
#> [430,] 77 11 0 11 1 150
#> [431,] 77 4 0 4 0 185
#> [432,] 75 3 1 1 0 180
#> [433,] 72 7 1 2 0 142
#> [434,] 71 16 0 180 0 140
#> [435,] 73 10 1 10 0 124
#> [436,] 74 7 0 180 1 150
#> [437,] 74 3 0 3 1 128
#> [438,] 76 1 0 180 0 114
#> [439,] 74 2 1 1 0 140
#> [440,] 76 8 1 0 1 141
#> [441,] 74 19 1 4 1 200
#> [442,] 73 6 0 6 1 114
#> [443,] 75 23 1 14 1 110
#> [444,] 74 2 0 180 0 190
#> [445,] 73 4 1 3 1 125
#> [446,] 76 13 1 10 0 110
#> [447,] 75 4 1 0 1 122
#> [448,] 75 7 0 7 0 190
#> [449,] 73 13 1 11 0 195
#> [450,] 75 12 0 12 1 160
#> [451,] 75 4 1 2 1 188
#> [452,] 74 6 0 180 0 160
#> [453,] 76 4 0 4 1 155
#> [454,] 75 1 0 1 1 125
#> [455,] 74 2 0 180 0 111
#> [456,] 73 1 0 52 1 105
#> [457,] 73 0 0 180 0 156
#> [458,] 72 5 0 180 0 120
#> [459,] 78 12 1 11 1 160
#> [460,] 76 44 1 10 0 105
#> [461,] 76 5 0 180 0 185
#> [462,] 74 10 1 0 1 135
#> [463,] 76 5 1 0 1 167
#> [464,] 74 8 1 8 1 170
#> [465,] 73 33 1 12 1 175
#> [466,] 77 5 1 0 0 123
#> [467,] 73 10 1 9 0 146
#> [468,] 77 1 1 0 1 90
#> [469,] 76 12 1 11 1 120
#> [470,] 78 5 1 0 1 170
#> [471,] 73 7 1 0 0 174
#> [472,] 74 6 0 79 1 140
#> [473,] 75 3 1 1 1 171
#> [474,] 74 9 1 8 0 200
#> [475,] 75 6 0 180 0 150
#> [476,] 79 10 1 8 0 190
#> [477,] 74 2 1 0 1 130
#> [478,] 78 18 0 18 1 144
#> [479,] 77 3 0 180 0 110
#> [480,] 73 11 1 2 1 110
#> [481,] 74 2 0 180 0 100
#> [482,] 78 7 0 7 1 133
#> [483,] 74 15 0 180 1 172
#> [484,] 74 7 0 7 0 161
#> [485,] 76 13 1 1 1 170
#> [486,] 80 10 1 6 1 147
#> [487,] 78 13 1 5 0 130
#> [488,] 75 5 0 119 1 150
#> [489,] 78 15 0 180 1 270
#> [490,] 80 8 0 8 1 120
#> [491,] 75 13 1 6 0 150
#> [492,] 79 4 0 80 0 145
#> [493,] 78 12 1 9 0 150
#> [494,] 78 2 1 1 0 130
#> [495,] 75 4 1 0 0 212
#> [496,] 77 2 1 0 1 143
#> [497,] 76 11 1 0 0 120
#> [498,] 75 3 0 3 0 0
#> [499,] 77 24 0 24 1 160
#> [500,] 79 8 0 32 1 120
#> [501,] 80 9 0 23 1 128
#> [502,] 80 6 0 6 1 150
#> [503,] 76 3 1 0 1 140
#> [504,] 78 11 1 1 1 140
#> [505,] 79 2 1 0 1 121
#> [506,] 78 11 1 8 1 118
#> [507,] 76 4 0 4 1 160
#> [508,] 76 12 1 10 1 127
#> [509,] 80 3 1 0 1 120
#> [510,] 75 2 1 1 1 204
#> [511,] 78 11 0 180 1 135
#> [512,] 76 1 0 1 1 140
#> [513,] 77 31 1 3 1 161
#> [514,] 76 1 0 1 1 90
#> [515,] 79 3 0 3 0 120
#> [516,] 77 7 0 180 1 170
#> [517,] 77 6 0 6 1 144
#> [518,] 79 4 1 0 1 120
#> [519,] 81 1 0 180 0 120
#> [520,] 80 15 1 12 1 150
#> [521,] 77 9 1 4 0 141
#> [522,] 78 4 0 59 1 112
#> [523,] 80 17 1 12 0 100
#> [524,] 76 7 0 161 0 151
#> [525,] 79 10 0 10 1 120
#> [526,] 80 6 0 173 1 160
#> [527,] 78 32 0 180 1 130
#> [528,] 79 1 0 37 1 140
#> [529,] 81 3 0 180 0 184
#> [530,] 78 7 0 7 1 147
#> [531,] 77 13 1 0 1 190
#> [532,] 78 15 0 15 0 165
#> [533,] 80 5 1 1 1 108
#> [534,] 78 4 0 180 0 175
#> [535,] 79 3 0 3 1 101
#> [536,] 81 4 1 1 1 104
#> [537,] 78 20 1 0 1 109
#> [538,] 80 1 0 1 0 100
#> [539,] 78 3 1 1 1 152
#> [540,] 77 10 1 8 1 130
#> [541,] 80 2 1 1 0 168
#> [542,] 80 6 1 0 1 119
#> [543,] 78 2 0 180 0 148
#> [544,] 80 5 0 5 1 130
#> [545,] 82 1 1 0 1 82
#> [546,] 79 10 0 180 1 150
#> [547,] 77 4 0 180 1 98
#> [548,] 82 21 1 2 0 155
#> [549,] 83 9 1 5 1 170
#> [550,] 79 7 1 6 0 130
#> [551,] 81 11 1 8 0 160
#> [552,] 81 5 0 177 0 41
#> [553,] 80 11 1 8 0 170
#> [554,] 78 9 1 4 1 120
#> [555,] 79 1 0 180 1 170
#> [556,] 81 15 0 180 1 140
#> [557,] 80 7 1 0 1 146
#> [558,] 84 5 1 1 1 85
#> [559,] 83 8 0 8 0 115
#> [560,] 81 16 0 16 1 110
#> [561,] 80 6 1 0 1 150
#> [562,] 80 11 1 8 0 110
#> [563,] 81 8 0 180 0 146
#> [564,] 80 8 1 7 0 160
#> [565,] 79 7 0 177 0 197
#> [566,] 85 4 0 180 0 90
#> [567,] 81 2 1 1 0 198
#> [568,] 83 2 0 2 1 155
#> [569,] 80 3 1 1 1 230
#> [570,] 82 23 1 0 0 110
#> [571,] 84 4 0 4 1 85
#> [572,] 81 1 0 1 1 150
#> [573,] 84 1 0 38 1 205
#> [574,] 83 3 0 180 0 174
#> [575,] 79 9 1 8 0 150
#> [576,] 80 13 1 8 1 140
#> [577,] 80 2 1 0 1 130
#> [578,] 80 6 0 71 1 189
#> [579,] 83 1 0 1 1 100
#> [580,] 82 19 0 19 0 120
#> [581,] 80 30 1 13 0 220
#> [582,] 83 9 0 180 0 198
#> [583,] 79 14 1 0 0 110
#> [584,] 83 3 0 114 0 98
#> [585,] 81 14 1 12 1 128
#> [586,] 83 2 0 154 0 130
#> [587,] 82 0 0 2 1 100
#> [588,] 81 4 0 4 0 175
#> [589,] 84 15 1 13 1 110
#> [590,] 81 12 0 12 1 163
#> [591,] 82 5 1 0 1 146
#> [592,] 81 4 0 4 0 160
#> [593,] 86 12 0 180 1 120
#> [594,] 83 12 1 2 1 170
#> [595,] 81 19 1 14 0 120
#> [596,] 80 2 0 88 0 135
#> [597,] 83 7 0 126 0 135
#> [598,] 86 8 0 8 1 132
#> [599,] 81 16 1 9 0 180
#> [600,] 84 6 0 165 0 145
#> [601,] 86 3 0 3 1 140
#> [602,] 84 3 0 180 1 120
#> [603,] 81 2 1 0 1 118
#> [604,] 82 1 0 180 1 193
#> [605,] 83 4 0 4 0 130
#> [606,] 87 2 0 5 1 137
#> [607,] 86 12 1 0 1 132
#> [608,] 82 14 1 11 1 103
#> [609,] 86 6 1 0 1 140
#> [610,] 84 3 1 2 0 125
#> [611,] 83 10 1 0 1 190
#> [612,] 83 13 1 12 0 170
#> [613,] 84 7 1 2 0 148
#> [614,] 87 2 0 180 0 113
#> [615,] 84 9 0 92 1 110
#> [616,] 86 4 0 38 1 122
#> [617,] 82 4 0 4 0 130
#> [618,] 86 13 0 177 0 163
#> [619,] 86 6 0 6 1 117
#> [620,] 84 13 0 62 1 100
#> [621,] 86 6 1 1 0 112
#> [622,] 88 4 0 4 0 100
#> [623,] 83 20 1 3 1 150
#> [624,] 88 4 0 4 1 115
#> [625,] 85 22 0 22 1 184
#> [626,] 87 2 0 180 1 130
#> [627,] 88 3 0 115 0 110
#> [628,] 88 2 0 180 1 68
#> [629,] 83 3 0 3 1 130
#> [630,] 86 15 1 8 1 109
#> [631,] 88 4 0 4 0 86
#> [632,] 89 4 0 4 1 153
#> [633,] 89 5 0 119 1 140
#> [634,] 87 6 0 180 1 110
#> [635,] 87 1 0 1 0 170
#> [636,] 84 8 0 180 1 119
#> [637,] 87 29 0 29 1 97
#> [638,] 89 10 0 46 1 170
#> [639,] 90 14 0 14 1 100
#> [640,] 86 4 0 180 1 145
#> [641,] 87 2 0 180 0 160
#> [642,] 87 6 1 0 0 125
#> [643,] 91 10 0 145 0 135
#> [644,] 86 3 1 0 1 80
#> [645,] 88 7 0 24 0 119
#> [646,] 88 8 0 50 1 154
#> [647,] 87 6 0 126 1 168
#> [648,] 90 4 1 0 0 121
#> [649,] 87 43 0 178 1 130
#> [650,] 90 5 1 0 1 125
#> [651,] 88 3 1 2 0 159
#> [652,] 89 3 1 1 1 160
#> [653,] 88 5 0 158 0 100
#> [654,] 87 7 0 74 1 105
#> [655,] 92 7 0 7 1 110
#> [656,] 89 4 0 4 1 159
#> [657,] 91 0 0 0 0 0
#> [658,] 89 14 0 180 1 84
#> [659,] 90 18 0 180 0 188
#> [660,] 94 6 0 50 0 78
#> [661,] 92 4 0 76 1 149
#> [662,] 91 1 0 180 0 158
#> [663,] 90 16 0 16 1 106
#> [664,] 96 3 0 12 1 97
#> [665,] 94 3 0 26 1 144
#> [666,] 91 7 0 7 0 135
#> [667,] 93 0 1 0 1 122
#> [668,] 92 5 0 69 0 139
#> [669,] 93 4 0 180 1 135
#> [670,] 96 15 1 0 1 140
#>
#> $y
#> [1] 180.0+ 5.0+ 5.0+ 5.0+ 2.0+ 180.0+ 180.0+ 115.0 180.0+ 180.0+
#> [11] 12.0 180.0+ 180.0+ 180.0+ 5.0+ 2.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [21] 180.0+ 180.0+ 2.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [31] 180.0+ 180.0+ 180.0+ 6.0+ 180.0+ 73.0 5.0+ 180.0+ 180.0+ 180.0+
#> [41] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 10.0+ 180.0+ 180.0+
#> [51] 7.0 180.0+ 180.0+ 180.0+ 1.0 180.0+ 179.0+ 180.0+ 4.0+ 180.0+
#> [61] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 7.0 88.0+ 4.0+ 180.0+
#> [71] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [81] 4.0+ 85.0 180.0+ 166.0+ 99.0 16.0+ 7.0+ 180.0+ 180.0+ 180.0+
#> [91] 13.0+ 171.0+ 180.0+ 28.0 6.0+ 1.0 180.0+ 180.0+ 180.0+ 175.0+
#> [101] 180.0+ 2.0 7.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 16.0+ 180.0+
#> [111] 16.0 15.0+ 180.0+ 180.0+ 12.0+ 180.0+ 8.0 2.0 180.0+ 180.0+
#> [121] 140.0 1.0 165.0 180.0+ 180.0+ 180.0+ 180.0+ 8.0+ 180.0+ 180.0+
#> [131] 0.5 180.0+ 180.0+ 180.0+ 180.0+ 171.0+ 15.0 166.0+ 5.0+ 4.0+
#> [141] 147.0+ 180.0+ 180.0+ 180.0+ 180.0+ 4.0+ 1.0 180.0+ 180.0+ 180.0+
#> [151] 180.0+ 2.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 9.0+ 180.0+ 180.0+
#> [161] 161.0+ 171.0+ 180.0+ 180.0+ 180.0+ 10.0+ 180.0+ 45.0 19.0 180.0+
#> [171] 9.0+ 24.0 180.0+ 8.0 180.0+ 180.0+ 1.0+ 15.0 180.0+ 180.0+
#> [181] 77.0 13.0+ 180.0+ 180.0+ 170.0 94.0 180.0+ 7.0 6.0 180.0+
#> [191] 180.0+ 180.0+ 3.0+ 180.0+ 30.0 180.0+ 28.0 5.0 180.0+ 180.0+
#> [201] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 9.0
#> [211] 7.0+ 22.0 84.0 7.0+ 180.0+ 180.0+ 180.0+ 1.0 1.0 180.0+
#> [221] 4.0+ 6.0+ 180.0+ 180.0+ 17.0 180.0+ 12.0 180.0+ 180.0+ 180.0+
#> [231] 14.0+ 36.0 180.0+ 3.0+ 88.0 12.0 180.0+ 180.0+ 180.0+ 180.0+
#> [241] 0.5 180.0+ 180.0+ 180.0+ 180.0+ 12.0 180.0+ 9.0 3.0 180.0+
#> [251] 180.0+ 180.0+ 11.0+ 18.0+ 180.0+ 180.0+ 180.0+ 3.0+ 2.0+ 103.0
#> [261] 15.0 180.0+ 180.0+ 180.0+ 179.0+ 166.0+ 3.0 180.0+ 7.0+ 8.0
#> [271] 5.0 16.0 180.0+ 180.0+ 180.0+ 123.0+ 1.0+ 18.0 11.0+ 180.0+
#> [281] 79.0 15.0+ 180.0+ 4.0+ 15.0 180.0+ 180.0+ 8.0+ 3.0 175.0
#> [291] 180.0+ 180.0+ 180.0+ 180.0+ 6.0 180.0+ 19.0+ 12.0 99.0 179.0+
#> [301] 180.0+ 180.0+ 11.0+ 180.0+ 18.0 180.0+ 180.0+ 180.0+ 180.0+ 93.0
#> [311] 101.0 4.0 5.0 150.0 180.0+ 180.0+ 180.0+ 1.0 180.0+ 180.0+
#> [321] 171.0 174.0+ 180.0+ 180.0+ 180.0+ 7.0+ 103.0 3.0+ 180.0+ 180.0+
#> [331] 180.0+ 97.0 7.0 8.0+ 180.0+ 2.0+ 180.0+ 180.0+ 180.0+ 7.0
#> [341] 180.0+ 7.0 13.0+ 123.0 19.0 180.0+ 1.0 132.0 10.0+ 180.0+
#> [351] 180.0+ 162.0 7.0+ 9.0 180.0+ 12.0 180.0+ 180.0+ 180.0+ 180.0+
#> [361] 2.0 180.0+ 173.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 16.0+
#> [371] 180.0+ 180.0+ 180.0+ 6.0 180.0+ 180.0+ 7.0+ 15.0 3.0+ 180.0+
#> [381] 3.0+ 20.0 20.0 0.5 180.0+ 180.0+ 180.0+ 8.0 3.0 87.0
#> [391] 12.0 58.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 175.0 2.0
#> [401] 10.0+ 180.0+ 180.0+ 8.0+ 1.0 180.0+ 159.0 10.0 1.0 13.0
#> [411] 180.0+ 104.0+ 1.0 180.0+ 11.0 3.0+ 5.0 180.0+ 12.0 180.0+
#> [421] 180.0+ 180.0+ 180.0+ 180.0+ 34.0 180.0+ 180.0+ 180.0+ 5.0 11.0+
#> [431] 4.0+ 180.0+ 7.0 180.0+ 10.0 180.0+ 3.0 180.0+ 180.0+ 180.0+
#> [441] 180.0+ 6.0 180.0+ 180.0+ 180.0+ 174.0+ 4.0 7.0 180.0+ 12.0
#> [451] 46.0 180.0+ 4.0 1.0 180.0+ 52.0 180.0+ 180.0+ 12.0 180.0+
#> [461] 180.0+ 180.0+ 180.0+ 8.0 33.0 5.0 180.0+ 1.0 12.0 180.0+
#> [471] 7.0+ 79.0 3.0 168.0+ 180.0+ 180.0+ 176.0+ 18.0 180.0+ 11.0
#> [481] 180.0+ 7.0 180.0+ 7.0 180.0+ 10.0 172.0 119.0 180.0+ 8.0
#> [491] 180.0+ 80.0 180.0+ 180.0+ 4.0+ 2.0 11.0 3.0 24.0 32.0
#> [501] 23.0 6.0 3.0+ 180.0+ 180.0+ 11.0 4.0 180.0+ 3.0+ 2.0+
#> [511] 180.0+ 1.0 171.0 1.0 3.0 180.0+ 6.0 138.0 180.0+ 180.0+
#> [521] 71.0 59.0 17.0 161.0 10.0+ 173.0 180.0+ 37.0 180.0+ 7.0+
#> [531] 22.0 15.0+ 5.0+ 180.0+ 3.0 71.0 20.0+ 1.0 3.0+ 10.0
#> [541] 10.0 6.0 180.0+ 5.0 1.0 180.0+ 180.0+ 180.0+ 9.0+ 180.0+
#> [551] 180.0+ 177.0+ 169.0 180.0+ 180.0+ 180.0+ 7.0+ 180.0+ 8.0+ 16.0
#> [561] 180.0+ 180.0+ 180.0+ 180.0+ 177.0+ 180.0+ 180.0+ 2.0 3.0+ 62.0
#> [571] 4.0 1.0 38.0 180.0+ 180.0+ 180.0+ 180.0+ 71.0 1.0 19.0
#> [581] 30.0 180.0+ 180.0+ 114.0 180.0+ 154.0 2.0 4.0+ 180.0+ 12.0
#> [591] 5.0+ 4.0+ 180.0+ 77.0 180.0+ 88.0 126.0 8.0 180.0+ 165.0
#> [601] 3.0 180.0+ 180.0+ 180.0+ 4.0+ 5.0 180.0+ 174.0 6.0 180.0+
#> [611] 180.0+ 13.0 180.0+ 180.0+ 92.0 38.0 4.0 177.0 6.0+ 62.0
#> [621] 6.0+ 4.0+ 20.0 4.0 22.0 180.0+ 115.0 180.0+ 3.0+ 180.0+
#> [631] 4.0 4.0 119.0 180.0+ 1.0+ 180.0+ 29.0 46.0 14.0 180.0+
#> [641] 180.0+ 25.0 145.0 3.0 24.0 50.0 126.0 4.0 178.0+ 89.0
#> [651] 75.0 3.0+ 158.0 74.0 7.0 4.0 0.5 180.0+ 180.0+ 50.0
#> [661] 76.0 180.0+ 16.0 12.0 26.0 7.0+ 0.5 69.0 180.0+ 15.0+
#>
#> $weights
#> NULL
#>
#> $offset
#> NULL
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> surv.cindex
#> 0.8521529