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, survdistr
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 | - |
| strata | untyped | NULL | - |
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.
Stratification
Parameter strata can be set to the name of one task column used as the
stratification variable.
During training, the column is removed from the data, converted to an integer,
and is attached to the survival response via glmnet::stratifySurv().
During prediction, the same task column is passed as newstrata for survival
distribution predictions (distr) in the survfit function.
Constant interpolation via survdistr is used to align the survival probabilities
to a common set of time points across all strata.
Note that if an observation in the test set has a stratum not present in the
training set, the prediction will fail, as the baseline hazard cannot be estimated
for unseen strata.
The rest of the prediction types do not depend on the strata column.
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$selected_features()
Returns the set of selected features as reported by glmnet::predict.glmnet()
with type set to "nonzero".
Arguments
lambda(
numeric(1))
Customlambda, defaults to the active lambda depending on parameter set.
Returns
(character()) of feature names.
Examples
# Define the Learner
learner = lrn("surv.cv_glmnet")
print(learner)
#>
#> ── <LearnerSurvCVGlmnet> (surv.cv_glmnet): Regularized Generalized Linear Model
#> • Model: -
#> • Parameters: cox.ties=breslow, use_pred_offset=TRUE
#> • Packages: mlr3, mlr3proba, mlr3extralearners, glmnet, and survdistr
#> • Predict Types: [crank], distr, and lp
#> • Feature Types: logical, integer, and numeric
#> • Encapsulation: none (fallback: -)
#> • Properties: offset, selected_features, and weights
#> • Other settings: use_weights = 'use', predict_raw = 'FALSE'
# Define a Task
task = tsk("grace")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$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 0.00313 45 2.763 0.1356 6
#> 1se 0.06742 12 2.890 0.1290 3
#>
#> $x
#> age los revasc revascdays stchange sysbp
#> [1,] 28 9 0 180 1 107
#> [2,] 33 2 0 2 0 150
#> [3,] 35 5 1 2 0 172
#> [4,] 35 10 1 9 0 106
#> [5,] 34 5 0 5 0 120
#> [6,] 35 2 1 1 1 112
#> [7,] 37 9 0 180 1 151
#> [8,] 38 13 1 0 1 161
#> [9,] 38 2 0 115 0 150
#> [10,] 36 1 0 180 1 155
#> [11,] 35 0 0 180 1 119
#> [12,] 38 12 1 8 1 120
#> [13,] 36 5 1 0 1 115
#> [14,] 33 6 1 1 1 115
#> [15,] 38 16 1 10 0 160
#> [16,] 40 12 1 9 0 153
#> [17,] 37 1 1 0 1 146
#> [18,] 38 5 1 3 0 125
#> [19,] 42 2 0 2 0 140
#> [20,] 40 11 1 10 1 120
#> [21,] 42 2 0 180 0 100
#> [22,] 41 2 1 1 0 166
#> [23,] 40 1 1 0 1 145
#> [24,] 42 4 0 180 0 162
#> [25,] 42 15 1 13 1 125
#> [26,] 40 3 1 1 0 170
#> [27,] 42 12 1 10 1 170
#> [28,] 43 2 1 1 1 116
#> [29,] 42 2 0 180 1 124
#> [30,] 44 5 1 1 0 170
#> [31,] 41 13 1 1 0 140
#> [32,] 45 9 1 7 0 110
#> [33,] 41 5 1 4 1 141
#> [34,] 44 2 1 1 1 150
#> [35,] 48 15 0 180 1 160
#> [36,] 45 3 0 150 0 130
#> [37,] 43 29 0 180 1 180
#> [38,] 45 4 1 0 0 124
#> [39,] 43 10 0 180 0 185
#> [40,] 47 6 1 0 1 116
#> [41,] 46 13 1 10 0 100
#> [42,] 44 0 1 0 1 96
#> [43,] 49 5 0 73 1 136
#> [44,] 46 2 1 1 1 122
#> [45,] 46 6 1 0 1 100
#> [46,] 44 4 1 0 1 114
#> [47,] 47 2 0 180 0 108
#> [48,] 45 5 0 180 1 190
#> [49,] 46 5 1 3 0 130
#> [50,] 46 4 0 180 1 121
#> [51,] 44 2 0 180 0 142
#> [52,] 46 15 0 180 1 120
#> [53,] 47 3 1 1 1 120
#> [54,] 48 3 0 180 0 154
#> [55,] 47 5 1 3 1 130
#> [56,] 47 9 1 6 0 170
#> [57,] 49 4 0 180 0 117
#> [58,] 47 10 0 10 1 140
#> [59,] 50 1 1 0 1 129
#> [60,] 48 2 1 0 0 184
#> [61,] 50 4 1 1 0 125
#> [62,] 50 6 1 2 1 140
#> [63,] 49 7 1 7 1 110
#> [64,] 46 3 1 1 1 140
#> [65,] 46 9 1 9 1 122
#> [66,] 49 2 0 2 0 105
#> [67,] 49 15 1 11 1 160
#> [68,] 47 2 0 180 0 150
#> [69,] 49 23 0 179 1 112
#> [70,] 46 6 1 0 1 156
#> [71,] 52 2 0 180 1 170
#> [72,] 50 7 1 0 1 92
#> [73,] 51 3 1 2 0 113
#> [74,] 50 9 0 180 0 130
#> [75,] 49 7 1 4 1 90
#> [76,] 47 8 0 180 0 160
#> [77,] 47 6 0 180 1 162
#> [78,] 52 2 0 180 0 155
#> [79,] 46 3 0 180 1 120
#> [80,] 46 1 1 1 0 145
#> [81,] 48 17 1 10 0 111
#> [82,] 47 2 1 1 0 110
#> [83,] 52 4 1 4 0 152
#> [84,] 49 9 1 3 0 102
#> [85,] 53 5 0 77 0 159
#> [86,] 54 6 1 3 0 129
#> [87,] 51 3 1 1 0 140
#> [88,] 50 2 0 5 1 106
#> [89,] 50 14 1 13 0 170
#> [90,] 48 3 1 2 0 150
#> [91,] 51 25 1 1 0 202
#> [92,] 49 5 1 2 1 150
#> [93,] 53 4 0 4 0 140
#> [94,] 52 14 1 7 1 200
#> [95,] 48 6 0 180 0 160
#> [96,] 48 11 1 10 0 120
#> [97,] 53 4 1 0 1 156
#> [98,] 54 9 1 0 1 138
#> [99,] 49 16 0 16 0 125
#> [100,] 55 3 1 1 0 150
#> [101,] 54 23 1 10 0 131
#> [102,] 55 6 1 2 1 114
#> [103,] 54 9 1 1 0 130
#> [104,] 55 4 1 2 0 150
#> [105,] 52 4 0 180 1 180
#> [106,] 51 13 1 11 0 145
#> [107,] 50 5 1 4 1 150
#> [108,] 52 4 0 180 0 183
#> [109,] 53 8 1 0 1 130
#> [110,] 50 7 1 0 1 127
#> [111,] 56 4 1 1 1 130
#> [112,] 55 2 0 2 0 145
#> [113,] 54 1 0 180 0 162
#> [114,] 54 7 1 0 1 100
#> [115,] 56 3 0 180 1 193
#> [116,] 55 5 1 4 1 120
#> [117,] 52 8 0 180 0 119
#> [118,] 53 18 1 9 1 150
#> [119,] 54 3 0 180 1 180
#> [120,] 55 6 0 180 0 170
#> [121,] 53 10 1 9 0 172
#> [122,] 55 6 0 180 1 100
#> [123,] 55 6 1 5 1 138
#> [124,] 54 12 1 0 1 190
#> [125,] 55 2 0 134 1 140
#> [126,] 54 3 0 180 0 128
#> [127,] 56 3 0 8 1 139
#> [128,] 57 3 0 3 0 120
#> [129,] 54 7 1 2 0 129
#> [130,] 54 2 1 1 0 135
#> [131,] 52 9 1 3 0 170
#> [132,] 54 2 1 1 1 176
#> [133,] 57 5 1 3 1 138
#> [134,] 57 1 0 1 1 100
#> [135,] 52 15 1 14 0 130
#> [136,] 53 3 1 0 1 200
#> [137,] 58 8 0 8 1 130
#> [138,] 57 0 0 0 1 150
#> [139,] 53 21 1 13 1 130
#> [140,] 59 3 1 1 0 172
#> [141,] 58 6 1 0 1 90
#> [142,] 53 15 1 10 1 130
#> [143,] 54 17 1 8 1 227
#> [144,] 55 9 1 2 1 147
#> [145,] 56 5 0 5 1 150
#> [146,] 57 4 1 2 1 185
#> [147,] 53 4 0 147 1 145
#> [148,] 55 3 1 2 0 140
#> [149,] 55 5 0 5 1 131
#> [150,] 56 4 0 4 0 164
#> [151,] 59 15 1 10 0 140
#> [152,] 58 9 1 0 1 180
#> [153,] 55 5 1 0 0 140
#> [154,] 56 7 1 5 1 120
#> [155,] 55 2 0 2 0 106
#> [156,] 59 9 1 1 1 125
#> [157,] 59 3 0 180 0 120
#> [158,] 58 4 1 0 1 160
#> [159,] 60 5 1 1 0 138
#> [160,] 57 5 0 180 1 130
#> [161,] 57 10 1 9 0 103
#> [162,] 59 5 0 180 1 155
#> [163,] 58 26 1 0 1 189
#> [164,] 58 4 1 3 0 120
#> [165,] 60 0 1 0 1 80
#> [166,] 59 2 1 1 0 140
#> [167,] 58 8 0 161 1 140
#> [168,] 58 14 1 6 0 190
#> [169,] 61 4 1 3 0 151
#> [170,] 61 3 1 2 1 102
#> [171,] 58 1 0 1 1 100
#> [172,] 61 20 1 13 0 130
#> [173,] 57 13 1 10 0 110
#> [174,] 57 2 1 0 1 116
#> [175,] 57 4 1 3 0 138
#> [176,] 57 11 0 180 1 150
#> [177,] 61 3 0 17 0 143
#> [178,] 56 14 0 45 0 130
#> [179,] 57 3 1 2 0 120
#> [180,] 56 13 1 6 1 158
#> [181,] 55 4 1 3 1 160
#> [182,] 60 12 1 0 1 114
#> [183,] 61 4 1 0 1 115
#> [184,] 56 8 1 8 0 120
#> [185,] 59 11 1 8 1 190
#> [186,] 57 1 0 1 0 126
#> [187,] 57 15 1 13 1 110
#> [188,] 59 5 1 2 0 182
#> [189,] 59 10 0 180 0 160
#> [190,] 61 13 0 13 0 210
#> [191,] 58 8 1 5 0 152
#> [192,] 62 7 1 2 1 180
#> [193,] 57 3 1 0 0 100
#> [194,] 61 18 0 170 0 140
#> [195,] 58 8 1 3 1 150
#> [196,] 60 7 0 7 0 147
#> [197,] 61 6 0 6 0 134
#> [198,] 60 17 1 8 1 140
#> [199,] 58 3 1 0 1 146
#> [200,] 62 4 1 3 0 173
#> [201,] 58 2 0 30 0 202
#> [202,] 59 16 1 9 1 133
#> [203,] 61 5 0 5 1 110
#> [204,] 61 5 0 5 1 160
#> [205,] 58 11 1 9 0 179
#> [206,] 57 2 1 1 0 159
#> [207,] 62 1 1 0 1 172
#> [208,] 63 3 1 1 0 180
#> [209,] 63 1 0 180 1 130
#> [210,] 63 4 1 3 0 222
#> [211,] 62 3 0 180 1 105
#> [212,] 63 15 1 10 1 126
#> [213,] 64 4 0 180 0 130
#> [214,] 61 9 1 9 1 150
#> [215,] 58 9 1 9 0 110
#> [216,] 62 7 0 7 0 150
#> [217,] 59 1 0 22 1 162
#> [218,] 59 4 0 180 0 196
#> [219,] 60 7 1 5 1 141
#> [220,] 60 7 1 1 1 90
#> [221,] 65 13 0 180 1 100
#> [222,] 63 1 0 1 0 162
#> [223,] 63 1 0 1 0 130
#> [224,] 62 6 0 180 0 170
#> [225,] 64 10 1 9 0 160
#> [226,] 62 6 0 6 0 120
#> [227,] 63 12 1 10 0 200
#> [228,] 60 8 0 17 1 130
#> [229,] 61 6 1 1 1 117
#> [230,] 66 1 1 0 1 120
#> [231,] 63 10 1 0 1 148
#> [232,] 63 14 1 9 0 123
#> [233,] 65 36 1 11 0 140
#> [234,] 63 4 1 3 0 162
#> [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,] 60 6 0 180 0 130
#> [242,] 64 21 1 10 0 190
#> [243,] 61 12 1 11 0 154
#> [244,] 64 9 0 180 0 150
#> [245,] 61 4 0 180 1 113
#> [246,] 63 16 1 7 1 110
#> [247,] 64 7 0 180 1 120
#> [248,] 66 6 1 1 1 130
#> [249,] 62 3 1 1 1 199
#> [250,] 65 6 0 9 0 112
#> [251,] 63 5 1 4 0 170
#> [252,] 62 13 1 11 0 180
#> [253,] 67 11 0 11 1 100
#> [254,] 64 2 0 2 0 201
#> [255,] 66 18 1 5 0 142
#> [256,] 66 16 1 11 1 169
#> [257,] 63 9 1 8 1 160
#> [258,] 63 3 1 2 0 120
#> [259,] 63 2 1 0 0 140
#> [260,] 67 6 0 180 1 170
#> [261,] 68 5 1 4 1 150
#> [262,] 64 13 1 12 1 150
#> [263,] 66 7 1 0 1 115
#> [264,] 66 13 1 0 0 118
#> [265,] 65 3 0 3 0 105
#> [266,] 67 4 1 3 0 130
#> [267,] 66 6 1 0 1 140
#> [268,] 65 2 1 1 1 170
#> [269,] 68 1 0 180 1 166
#> [270,] 64 10 1 9 1 110
#> [271,] 63 10 0 16 1 160
#> [272,] 66 14 0 180 0 130
#> [273,] 68 18 0 180 1 260
#> [274,] 65 17 1 14 1 100
#> [275,] 65 18 1 3 0 120
#> [276,] 63 1 1 0 1 155
#> [277,] 63 10 0 18 1 130
#> [278,] 67 11 0 11 0 150
#> [279,] 68 14 0 79 0 172
#> [280,] 65 15 1 12 1 150
#> [281,] 66 11 1 0 0 100
#> [282,] 69 12 0 15 1 140
#> [283,] 63 2 0 180 0 150
#> [284,] 65 11 1 6 0 130
#> [285,] 69 21 1 10 0 180
#> [286,] 66 9 1 8 0 130
#> [287,] 65 8 1 0 1 90
#> [288,] 67 1 0 180 1 160
#> [289,] 68 10 1 10 1 150
#> [290,] 65 1 1 0 0 133
#> [291,] 63 2 1 0 0 99
#> [292,] 66 19 1 12 1 150
#> [293,] 69 6 0 99 1 140
#> [294,] 64 4 0 179 0 160
#> [295,] 70 15 1 12 1 132
#> [296,] 64 11 0 11 0 125
#> [297,] 67 2 0 18 0 131
#> [298,] 66 4 0 180 0 177
#> [299,] 68 4 1 0 1 160
#> [300,] 69 4 1 3 1 150
#> [301,] 69 8 0 93 0 140
#> [302,] 64 21 0 21 1 155
#> [303,] 66 6 0 180 0 140
#> [304,] 68 18 1 0 1 160
#> [305,] 65 6 0 101 1 115
#> [306,] 68 4 0 4 1 190
#> [307,] 70 7 1 0 1 190
#> [308,] 69 8 0 180 1 153
#> [309,] 66 4 0 180 0 130
#> [310,] 67 10 1 9 0 200
#> [311,] 68 18 1 14 1 170
#> [312,] 69 0 0 0 1 148
#> [313,] 65 2 0 180 0 130
#> [314,] 68 7 1 0 1 150
#> [315,] 67 14 1 13 0 130
#> [316,] 65 14 1 13 1 150
#> [317,] 71 7 0 7 0 230
#> [318,] 66 2 0 2 1 228
#> [319,] 71 3 0 103 0 133
#> [320,] 69 3 0 3 1 130
#> [321,] 70 22 1 13 0 103
#> [322,] 67 5 0 5 0 130
#> [323,] 68 6 0 180 0 145
#> [324,] 69 8 1 5 1 195
#> [325,] 72 3 1 0 1 132
#> [326,] 72 7 0 7 1 110
#> [327,] 69 8 1 7 1 108
#> [328,] 66 2 1 1 0 123
#> [329,] 69 19 0 180 0 130
#> [330,] 69 11 1 0 1 120
#> [331,] 66 2 0 180 0 130
#> [332,] 67 7 1 4 0 122
#> [333,] 69 8 1 2 0 121
#> [334,] 67 13 1 9 0 130
#> [335,] 70 3 0 123 0 130
#> [336,] 70 9 0 180 1 142
#> [337,] 72 5 1 4 0 170
#> [338,] 67 22 1 1 1 140
#> [339,] 68 3 0 19 0 135
#> [340,] 67 12 1 8 0 120
#> [341,] 69 1 0 1 1 110
#> [342,] 67 1 0 1 1 60
#> [343,] 67 4 0 60 1 136
#> [344,] 69 5 0 76 0 120
#> [345,] 67 8 1 0 1 130
#> [346,] 72 13 1 11 1 195
#> [347,] 68 10 1 8 1 160
#> [348,] 70 35 1 0 1 105
#> [349,] 72 30 1 0 1 145
#> [350,] 70 7 0 7 0 102
#> [351,] 68 7 1 2 0 135
#> [352,] 73 20 1 0 1 170
#> [353,] 71 6 0 9 0 120
#> [354,] 69 10 1 6 1 120
#> [355,] 67 8 0 180 1 170
#> [356,] 73 13 0 152 1 130
#> [357,] 72 2 0 2 1 100
#> [358,] 72 6 1 5 0 115
#> [359,] 71 1 0 173 1 188
#> [360,] 68 23 0 180 1 220
#> [361,] 70 3 0 180 0 121
#> [362,] 71 3 1 2 0 150
#> [363,] 68 4 1 3 0 210
#> [364,] 72 5 0 28 0 120
#> [365,] 69 8 1 1 0 164
#> [366,] 68 7 0 180 1 130
#> [367,] 72 16 1 1 1 130
#> [368,] 73 7 0 7 1 140
#> [369,] 68 15 1 13 1 130
#> [370,] 70 3 0 3 1 159
#> [371,] 72 6 0 180 1 130
#> [372,] 73 0 0 180 1 161
#> [373,] 74 8 1 0 1 85
#> [374,] 73 4 0 180 1 154
#> [375,] 71 3 1 1 0 150
#> [376,] 74 20 0 20 1 180
#> [377,] 71 20 1 10 0 140
#> [378,] 73 3 1 0 1 136
#> [379,] 70 5 1 0 1 190
#> [380,] 71 3 1 2 1 190
#> [381,] 73 10 1 8 0 106
#> [382,] 69 12 1 1 1 149
#> [383,] 74 4 0 4 0 120
#> [384,] 72 5 1 3 1 160
#> [385,] 70 3 0 180 1 154
#> [386,] 72 15 1 0 1 150
#> [387,] 71 7 1 2 0 143
#> [388,] 71 13 1 8 0 121
#> [389,] 69 2 1 1 1 80
#> [390,] 70 4 1 0 1 140
#> [391,] 74 7 1 0 1 117
#> [392,] 72 10 1 8 1 153
#> [393,] 69 7 0 180 1 144
#> [394,] 72 15 1 13 0 156
#> [395,] 75 2 1 1 0 145
#> [396,] 72 10 1 9 1 160
#> [397,] 73 10 1 10 1 120
#> [398,] 73 1 0 1 1 80
#> [399,] 75 9 1 7 0 140
#> [400,] 71 4 0 4 0 134
#> [401,] 72 15 1 12 1 120
#> [402,] 70 7 1 4 0 184
#> [403,] 72 1 1 1 0 168
#> [404,] 72 7 0 57 1 145
#> [405,] 73 10 0 180 0 162
#> [406,] 72 11 0 11 1 140
#> [407,] 70 3 0 3 0 150
#> [408,] 76 25 1 12 1 170
#> [409,] 73 12 1 12 1 140
#> [410,] 72 2 0 180 0 120
#> [411,] 75 1 0 180 1 140
#> [412,] 72 4 1 0 1 197
#> [413,] 71 3 1 0 0 144
#> [414,] 73 5 0 180 0 126
#> [415,] 74 34 1 8 1 233
#> [416,] 76 3 1 0 1 120
#> [417,] 72 5 0 180 0 154
#> [418,] 76 5 0 5 1 130
#> [419,] 77 11 0 11 1 150
#> [420,] 77 4 0 4 0 185
#> [421,] 72 7 1 2 0 142
#> [422,] 73 15 0 15 1 160
#> [423,] 71 16 0 180 0 140
#> [424,] 73 10 1 10 0 124
#> [425,] 74 7 0 180 1 150
#> [426,] 76 1 0 180 0 114
#> [427,] 76 8 1 0 1 141
#> [428,] 73 6 0 6 1 114
#> [429,] 75 23 1 14 1 110
#> [430,] 72 4 0 85 1 120
#> [431,] 76 17 1 0 1 200
#> [432,] 73 4 1 3 1 125
#> [433,] 76 13 1 10 0 110
#> [434,] 75 4 1 0 1 122
#> [435,] 75 7 0 7 0 190
#> [436,] 75 0 0 0 1 130
#> [437,] 75 12 0 12 1 160
#> [438,] 74 8 1 0 1 105
#> [439,] 76 13 1 8 1 148
#> [440,] 74 6 0 180 0 160
#> [441,] 75 1 0 1 1 125
#> [442,] 74 2 0 180 0 111
#> [443,] 72 5 0 180 0 120
#> [444,] 78 12 1 11 1 160
#> [445,] 76 44 1 10 0 105
#> [446,] 76 5 0 180 0 185
#> [447,] 74 10 1 0 1 135
#> [448,] 76 5 1 0 1 167
#> [449,] 74 8 1 8 1 170
#> [450,] 75 9 0 180 1 140
#> [451,] 73 33 1 12 1 175
#> [452,] 77 5 1 0 0 123
#> [453,] 73 10 1 9 0 146
#> [454,] 77 12 0 180 0 130
#> [455,] 77 1 1 0 1 90
#> [456,] 76 12 1 11 1 120
#> [457,] 73 7 1 0 0 174
#> [458,] 74 6 0 79 1 140
#> [459,] 75 3 1 1 1 171
#> [460,] 74 9 1 8 0 200
#> [461,] 75 6 0 180 0 150
#> [462,] 79 10 1 8 0 190
#> [463,] 78 18 0 18 1 144
#> [464,] 76 29 0 47 0 90
#> [465,] 74 2 0 180 0 100
#> [466,] 78 8 1 6 1 110
#> [467,] 74 7 0 7 0 161
#> [468,] 76 13 1 1 1 170
#> [469,] 78 32 1 9 1 198
#> [470,] 79 6 0 180 0 170
#> [471,] 80 10 1 6 1 147
#> [472,] 78 0 0 180 1 212
#> [473,] 78 13 1 5 0 130
#> [474,] 75 5 0 119 1 150
#> [475,] 78 15 0 180 1 270
#> [476,] 80 8 0 8 1 120
#> [477,] 74 10 1 8 0 135
#> [478,] 78 12 1 9 0 150
#> [479,] 75 4 1 0 0 212
#> [480,] 77 2 1 0 1 143
#> [481,] 75 11 1 4 0 162
#> [482,] 75 3 0 3 0 0
#> [483,] 76 7 0 29 1 150
#> [484,] 80 9 0 23 1 128
#> [485,] 80 6 0 6 1 150
#> [486,] 78 11 1 1 1 140
#> [487,] 79 11 0 180 0 160
#> [488,] 78 14 1 0 1 140
#> [489,] 78 11 1 8 1 118
#> [490,] 76 4 0 4 1 160
#> [491,] 76 10 1 8 0 180
#> [492,] 75 2 1 1 1 204
#> [493,] 78 11 0 180 1 135
#> [494,] 76 1 0 1 1 140
#> [495,] 77 31 1 3 1 161
#> [496,] 76 1 0 1 1 90
#> [497,] 78 7 1 0 1 110
#> [498,] 79 3 0 3 0 120
#> [499,] 77 7 0 180 1 170
#> [500,] 77 6 0 6 1 144
#> [501,] 79 4 1 0 1 120
#> [502,] 77 9 1 4 0 141
#> [503,] 82 5 0 8 1 120
#> [504,] 80 40 1 0 1 138
#> [505,] 78 4 0 59 1 112
#> [506,] 79 10 0 10 1 120
#> [507,] 80 15 1 0 1 90
#> [508,] 81 4 1 2 1 126
#> [509,] 79 28 0 164 0 100
#> [510,] 78 32 0 180 1 130
#> [511,] 81 3 0 180 0 184
#> [512,] 81 2 0 175 0 172
#> [513,] 80 5 1 1 1 108
#> [514,] 78 4 0 180 0 175
#> [515,] 79 3 0 3 1 101
#> [516,] 78 26 1 5 0 194
#> [517,] 81 4 1 1 1 104
#> [518,] 77 5 0 85 0 188
#> [519,] 80 2 1 1 0 168
#> [520,] 79 6 0 6 0 152
#> [521,] 80 6 1 0 1 119
#> [522,] 78 2 0 180 0 148
#> [523,] 80 5 0 5 1 130
#> [524,] 79 10 0 180 1 150
#> [525,] 78 12 0 180 0 134
#> [526,] 79 1 0 125 0 193
#> [527,] 84 22 1 10 0 180
#> [528,] 79 4 0 4 1 121
#> [529,] 80 6 0 6 1 110
#> [530,] 83 9 1 5 1 170
#> [531,] 82 5 0 180 0 110
#> [532,] 83 5 0 180 0 148
#> [533,] 79 7 1 6 0 130
#> [534,] 81 5 0 177 0 41
#> [535,] 80 11 1 8 0 170
#> [536,] 79 4 0 4 1 183
#> [537,] 78 9 1 4 1 120
#> [538,] 82 8 1 1 0 128
#> [539,] 79 1 0 180 1 170
#> [540,] 81 15 0 180 1 140
#> [541,] 81 20 1 9 0 170
#> [542,] 83 8 0 8 0 115
#> [543,] 80 6 1 0 1 150
#> [544,] 80 11 1 8 0 110
#> [545,] 81 8 0 180 0 146
#> [546,] 80 8 1 7 0 160
#> [547,] 79 7 0 177 0 197
#> [548,] 79 0 1 0 1 96
#> [549,] 85 4 0 180 0 90
#> [550,] 81 2 1 1 0 198
#> [551,] 83 2 0 2 1 155
#> [552,] 82 6 0 128 1 100
#> [553,] 84 4 0 167 0 198
#> [554,] 80 3 1 1 1 230
#> [555,] 82 23 1 0 0 110
#> [556,] 84 5 0 180 1 203
#> [557,] 81 1 0 1 1 150
#> [558,] 84 1 0 38 1 205
#> [559,] 83 3 0 180 0 174
#> [560,] 81 4 0 90 1 138
#> [561,] 79 9 1 8 0 150
#> [562,] 85 3 1 2 1 160
#> [563,] 84 4 0 89 1 129
#> [564,] 80 6 0 71 1 189
#> [565,] 83 1 0 1 1 100
#> [566,] 83 9 0 180 0 198
#> [567,] 83 3 0 114 0 98
#> [568,] 81 14 1 12 1 128
#> [569,] 83 2 0 154 0 130
#> [570,] 85 9 1 6 1 160
#> [571,] 81 4 0 4 0 175
#> [572,] 84 15 1 13 1 110
#> [573,] 81 1 0 1 1 145
#> [574,] 81 12 0 12 1 163
#> [575,] 81 4 0 4 0 160
#> [576,] 86 12 0 180 1 120
#> [577,] 81 19 1 14 0 120
#> [578,] 82 3 1 2 0 130
#> [579,] 82 15 1 0 0 183
#> [580,] 80 2 0 88 0 135
#> [581,] 83 7 0 126 0 135
#> [582,] 81 16 1 9 0 180
#> [583,] 82 9 0 180 1 134
#> [584,] 84 3 0 180 1 120
#> [585,] 81 13 0 180 0 152
#> [586,] 85 3 0 3 1 118
#> [587,] 81 2 1 0 1 118
#> [588,] 81 4 0 180 0 160
#> [589,] 83 9 0 180 1 149
#> [590,] 82 1 0 180 1 193
#> [591,] 83 4 0 4 0 130
#> [592,] 87 2 0 5 1 137
#> [593,] 86 12 1 0 1 132
#> [594,] 82 14 1 11 1 103
#> [595,] 86 6 1 0 1 140
#> [596,] 84 16 0 70 1 150
#> [597,] 83 19 0 43 0 150
#> [598,] 84 3 1 2 0 125
#> [599,] 83 10 1 0 1 190
#> [600,] 86 2 0 180 1 169
#> [601,] 83 13 1 12 0 170
#> [602,] 84 7 1 2 0 148
#> [603,] 87 2 0 180 0 113
#> [604,] 84 3 0 180 1 170
#> [605,] 86 4 0 38 1 122
#> [606,] 82 4 0 4 0 130
#> [607,] 86 13 0 177 0 163
#> [608,] 85 3 0 3 1 113
#> [609,] 86 6 0 6 1 117
#> [610,] 88 4 0 4 0 100
#> [611,] 83 20 1 3 1 150
#> [612,] 88 4 0 4 1 115
#> [613,] 83 9 0 65 1 150
#> [614,] 86 9 1 7 1 142
#> [615,] 87 2 0 180 1 130
#> [616,] 86 6 0 46 0 173
#> [617,] 88 3 0 115 0 110
#> [618,] 88 2 0 180 1 68
#> [619,] 88 4 0 4 0 86
#> [620,] 89 4 0 4 1 153
#> [621,] 89 5 0 119 1 140
#> [622,] 87 6 0 180 1 110
#> [623,] 87 1 0 1 0 170
#> [624,] 84 8 0 180 1 119
#> [625,] 85 8 0 8 1 136
#> [626,] 84 2 0 110 1 174
#> [627,] 87 15 1 9 1 138
#> [628,] 84 0 0 180 1 136
#> [629,] 89 10 0 46 1 170
#> [630,] 90 14 0 14 1 100
#> [631,] 88 1 0 1 0 135
#> [632,] 91 8 0 8 0 100
#> [633,] 87 2 0 180 0 160
#> [634,] 87 6 1 0 0 125
#> [635,] 91 10 0 145 0 135
#> [636,] 88 7 0 24 0 119
#> [637,] 90 11 1 10 1 186
#> [638,] 87 6 0 126 1 168
#> [639,] 86 10 0 180 1 137
#> [640,] 86 9 1 7 0 130
#> [641,] 90 4 1 0 0 121
#> [642,] 91 1 0 1 1 74
#> [643,] 87 5 0 36 1 150
#> [644,] 90 5 1 0 1 125
#> [645,] 88 3 1 2 0 159
#> [646,] 89 3 1 1 1 160
#> [647,] 92 1 0 1 1 167
#> [648,] 88 5 0 158 0 100
#> [649,] 87 7 0 74 1 105
#> [650,] 89 12 1 0 1 130
#> [651,] 91 5 0 169 1 176
#> [652,] 89 52 0 52 1 130
#> [653,] 92 7 0 7 1 110
#> [654,] 89 4 0 4 1 159
#> [655,] 91 0 0 0 0 0
#> [656,] 90 18 0 180 0 188
#> [657,] 90 19 1 11 1 129
#> [658,] 94 6 0 50 0 78
#> [659,] 90 1 0 1 1 118
#> [660,] 91 2 0 2 1 116
#> [661,] 93 8 0 179 1 110
#> [662,] 94 8 0 8 1 142
#> [663,] 91 1 0 180 0 158
#> [664,] 94 3 0 26 1 144
#> [665,] 91 12 0 53 1 212
#> [666,] 93 0 1 0 1 122
#> [667,] 92 2 0 2 0 112
#> [668,] 93 4 0 180 1 135
#> [669,] 96 3 1 0 1 104
#> [670,] 96 15 1 0 1 140
#>
#> $y
#> [1] 180.0+ 2.0+ 5.0+ 180.0+ 5.0+ 2.0+ 180.0+ 180.0+ 115.0 180.0+
#> [11] 180.0+ 12.0 5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 5.0+ 2.0+ 180.0+
#> [21] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 2.0+ 180.0+ 155.0+
#> [31] 180.0+ 180.0+ 5.0+ 180.0+ 180.0+ 150.0 180.0+ 180.0+ 180.0+ 6.0+
#> [41] 180.0+ 180.0+ 73.0 161.0+ 180.0+ 180.0+ 180.0+ 180.0+ 5.0+ 180.0+
#> [51] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 10.0+ 172.0+ 180.0+
#> [61] 180.0+ 180.0+ 7.0 180.0+ 180.0+ 2.0 179.0+ 180.0+ 179.0+ 180.0+
#> [71] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [81] 88.0+ 180.0+ 4.0+ 180.0+ 77.0 180.0+ 180.0+ 5.0 180.0+ 180.0+
#> [91] 180.0+ 180.0+ 4.0+ 85.0 180.0+ 180.0+ 166.0+ 180.0+ 16.0+ 180.0+
#> [101] 152.0+ 6.0+ 180.0+ 180.0+ 180.0+ 13.0+ 171.0+ 180.0+ 180.0+ 180.0+
#> [111] 180.0+ 2.0 180.0+ 7.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [121] 180.0+ 180.0+ 180.0+ 12.0+ 134.0+ 180.0+ 8.0 3.0+ 180.0+ 180.0+
#> [131] 180.0+ 180.0+ 140.0 1.0 180.0+ 180.0+ 8.0+ 0.5 180.0+ 180.0+
#> [141] 180.0+ 180.0+ 171.0+ 15.0 5.0+ 4.0+ 147.0+ 180.0+ 5.0+ 4.0+
#> [151] 180.0+ 9.0+ 180.0+ 180.0+ 2.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [161] 180.0+ 180.0+ 180.0+ 180.0+ 0.5 180.0+ 161.0+ 171.0+ 180.0+ 3.0
#> [171] 1.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 17.0 45.0 3.0+ 180.0+
#> [181] 180.0+ 172.0+ 180.0+ 8.0 180.0+ 1.0+ 15.0 180.0+ 180.0+ 13.0+
#> [191] 8.0+ 180.0+ 180.0+ 170.0 180.0+ 7.0+ 6.0 180.0+ 3.0+ 180.0+
#> [201] 30.0 180.0+ 5.0 5.0+ 180.0+ 180.0+ 1.0 180.0+ 180.0+ 180.0+
#> [211] 180.0+ 180.0+ 180.0+ 180.0+ 9.0 7.0+ 22.0 180.0+ 84.0 180.0+
#> [221] 180.0+ 1.0 1.0 180.0+ 167.0 6.0+ 180.0+ 17.0 180.0+ 180.0+
#> [231] 180.0+ 14.0+ 36.0 180.0+ 88.0 12.0 180.0+ 180.0+ 180.0+ 180.0+
#> [241] 180.0+ 180.0+ 12.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 9.0
#> [251] 180.0+ 180.0+ 11.0+ 2.0+ 18.0+ 180.0+ 180.0+ 3.0+ 2.0+ 180.0+
#> [261] 5.0+ 13.0 179.0+ 166.0+ 3.0 180.0+ 180.0+ 175.0+ 180.0+ 180.0+
#> [271] 16.0 180.0+ 180.0+ 180.0+ 123.0+ 1.0+ 18.0 11.0+ 79.0 15.0+
#> [281] 180.0+ 15.0 180.0+ 180.0+ 174.0+ 180.0+ 8.0+ 180.0+ 10.0 180.0+
#> [291] 180.0+ 19.0+ 99.0 179.0+ 180.0+ 11.0+ 18.0 180.0+ 180.0+ 152.0+
#> [301] 93.0 21.0+ 180.0+ 18.0+ 101.0 4.0 7.0+ 180.0+ 180.0+ 174.0+
#> [311] 180.0+ 0.5 180.0+ 180.0+ 180.0+ 14.0+ 7.0+ 2.0 103.0 3.0+
#> [321] 180.0+ 5.0+ 180.0+ 180.0+ 180.0+ 7.0 8.0+ 2.0+ 180.0+ 180.0+
#> [331] 180.0+ 7.0 8.0+ 13.0+ 123.0 180.0+ 180.0+ 51.0 19.0 180.0+
#> [341] 1.0 1.0 60.0 76.0 180.0+ 132.0 10.0+ 180.0+ 162.0 7.0+
#> [351] 7.0+ 124.0 9.0 180.0+ 180.0+ 152.0 2.0 180.0+ 173.0+ 180.0+
#> [361] 180.0+ 180.0+ 180.0+ 28.0 180.0+ 180.0+ 16.0+ 7.0+ 15.0 3.0+
#> [371] 180.0+ 180.0+ 180.0+ 180.0+ 3.0+ 20.0 20.0 180.0+ 180.0+ 3.0
#> [381] 87.0 12.0 4.0+ 180.0+ 180.0+ 180.0+ 180.0+ 175.0 2.0 180.0+
#> [391] 180.0+ 10.0+ 180.0+ 180.0+ 180.0+ 159.0 15.0 1.0 180.0+ 4.0+
#> [401] 180.0+ 104.0+ 1.0 57.0 180.0+ 11.0 3.0+ 180.0+ 12.0 180.0+
#> [411] 180.0+ 180.0+ 180.0+ 180.0+ 34.0 180.0+ 180.0+ 5.0 11.0+ 4.0+
#> [421] 7.0 15.0+ 180.0+ 10.0 180.0+ 180.0+ 180.0+ 6.0 180.0+ 85.0
#> [431] 17.0+ 180.0+ 174.0+ 4.0 7.0 0.5 12.0 180.0+ 180.0+ 180.0+
#> [441] 1.0 180.0+ 180.0+ 12.0 180.0+ 180.0+ 180.0+ 180.0+ 8.0 180.0+
#> [451] 33.0 5.0 180.0+ 180.0+ 1.0 12.0 7.0+ 79.0 3.0 168.0+
#> [461] 180.0+ 180.0+ 18.0 47.0 180.0+ 8.0+ 7.0 180.0+ 32.0 180.0+
#> [471] 10.0 180.0+ 172.0 119.0 180.0+ 8.0 180.0+ 180.0+ 4.0+ 2.0
#> [481] 152.0+ 3.0 29.0 23.0 6.0 180.0+ 180.0+ 180.0+ 11.0 4.0
#> [491] 10.0+ 2.0+ 180.0+ 1.0 171.0 1.0 43.0 3.0 180.0+ 6.0
#> [501] 138.0 71.0 8.0 40.0 59.0 10.0+ 180.0+ 93.0 164.0 180.0+
#> [511] 180.0+ 175.0+ 5.0+ 180.0+ 3.0 171.0+ 71.0 85.0 10.0 6.0+
#> [521] 6.0 180.0+ 5.0 180.0+ 180.0+ 125.0 180.0+ 4.0 6.0 9.0+
#> [531] 180.0+ 180.0+ 180.0+ 177.0+ 169.0 4.0 180.0+ 180.0+ 180.0+ 180.0+
#> [541] 20.0 8.0+ 180.0+ 180.0+ 180.0+ 180.0+ 177.0+ 0.5 180.0+ 180.0+
#> [551] 2.0 128.0 167.0 3.0+ 62.0 180.0+ 1.0 38.0 180.0+ 90.0
#> [561] 180.0+ 180.0+ 89.0 71.0 1.0 180.0+ 114.0 180.0+ 154.0 180.0+
#> [571] 4.0+ 180.0+ 1.0 12.0 4.0+ 180.0+ 180.0+ 3.0 83.0 88.0
#> [581] 126.0 180.0+ 180.0+ 180.0+ 180.0+ 3.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [591] 4.0+ 5.0 180.0+ 174.0 6.0 70.0 43.0 180.0+ 180.0+ 180.0+
#> [601] 13.0 180.0+ 180.0+ 180.0+ 38.0 4.0 177.0 3.0+ 6.0+ 4.0+
#> [611] 20.0 4.0 65.0 11.0 180.0+ 46.0 115.0 180.0+ 4.0 4.0
#> [621] 119.0 180.0+ 1.0+ 180.0+ 8.0 110.0 180.0+ 180.0+ 46.0 14.0
#> [631] 1.0+ 8.0 180.0+ 25.0 145.0 24.0 11.0 126.0 180.0+ 180.0+
#> [641] 4.0 1.0 36.0 89.0 75.0 3.0+ 1.0 158.0 74.0 180.0+
#> [651] 169.0 52.0 7.0 4.0 0.5 180.0+ 180.0+ 50.0 1.0+ 2.0
#> [661] 179.0+ 8.0+ 180.0+ 26.0 53.0 0.5 2.0 180.0+ 3.0 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
#> 0.8101081