GLM with Elastic Net Regularization Survival Learner
Source:R/learner_glmnet_surv_glmnet.R
mlr_learners_surv.glmnet.RdGeneralized linear models with elastic net regularization.
Calls glmnet::glmnet() from package glmnet.
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.coxnet().crank: same aslp.distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated usingglmnet::survfit.coxnet(). 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.
Caution: This learner is different to learners calling glmnet::cv.glmnet()
in that it does not use the internal optimization of parameter lambda.
Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning).
When lambda is tuned, the glmnet will be trained for each tuning iteration.
While fitting the whole path of lambdas would be more efficient, as is done
by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time
(using parameter s) is currently not supported in mlr3
(at least not in efficient manner).
Tuning the s parameter is, therefore, currently discouraged.
When the data are i.i.d. and efficiency is key, we recommend using the respective
auto-tuning counterpart in mlr_learners_surv.cv_glmnet().
However, in some situations this is not applicable, usually when data are
imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires
custom resampling strategies (blocked design, stratification).
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.coxnet().
Otherwise, if the user sets use_pred_offset = FALSE, a zero offset is applied,
effectively disabling the offset adjustment during prediction.
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 |
| alignment | character | lambda | lambda, fraction | - |
| alpha | numeric | 1 | \([0, 1]\) | |
| big | numeric | 9.9e+35 | \((-\infty, \infty)\) | |
| devmax | numeric | 0.999 | \([0, 1]\) | |
| dfmax | integer | - | \([0, \infty)\) | |
| eps | numeric | 1e-06 | \([0, 1]\) | |
| epsnr | numeric | 1e-08 | \([0, 1]\) | |
| exact | logical | FALSE | TRUE, FALSE | - |
| exclude | untyped | - | - | |
| exmx | numeric | 250 | \((-\infty, \infty)\) | |
| fdev | numeric | 1e-05 | \([0, 1]\) | |
| gamma | untyped | - | - | |
| grouped | logical | TRUE | TRUE, FALSE | - |
| intercept | logical | TRUE | TRUE, FALSE | - |
| keep | logical | FALSE | TRUE, FALSE | - |
| lambda | untyped | - | - | |
| lambda.min.ratio | numeric | - | \([0, 1]\) | |
| lower.limits | untyped | -Inf | - | |
| maxit | integer | 100000 | \([1, \infty)\) | |
| mnlam | integer | 5 | \([1, \infty)\) | |
| mxit | integer | 100 | \([1, \infty)\) | |
| mxitnr | integer | 25 | \([1, \infty)\) | |
| nlambda | integer | 100 | \([1, \infty)\) | |
| use_pred_offset | logical | TRUE | TRUE, FALSE | - |
| parallel | logical | FALSE | TRUE, FALSE | - |
| penalty.factor | untyped | - | - | |
| pmax | integer | - | \([0, \infty)\) | |
| pmin | numeric | 1e-09 | \([0, 1]\) | |
| prec | numeric | 1e-10 | \((-\infty, \infty)\) | |
| predict.gamma | numeric | gamma.1se | \((-\infty, \infty)\) | |
| relax | logical | FALSE | TRUE, FALSE | - |
| s | numeric | 0.01 | \([0, \infty)\) | |
| standardize | logical | TRUE | TRUE, FALSE | - |
| thresh | numeric | 1e-07 | \([0, \infty)\) | |
| trace.it | integer | 0 | \([0, 1]\) | |
| type.logistic | character | Newton | Newton, modified.Newton | - |
| type.multinomial | character | ungrouped | ungrouped, grouped | - |
| upper.limits | untyped | Inf | - | |
| stype | integer | 2 | \([1, 2]\) | |
| ctype | integer | - | \([1, 2]\) |
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/basics.html#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 -> LearnerSurvGlmnet
Methods
Method 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.glmnet")
print(learner)
#>
#> ── <LearnerSurvGlmnet> (surv.glmnet): Regularized Generalized Linear Model ─────
#> • Model: -
#> • Parameters: 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'
# 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: (if (cv) glmnet::cv.glmnet else glmnet::glmnet)(x = data, y = target, family = "cox")
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.186700
#> 2 1 0.66 0.170100
#> 3 2 1.52 0.155000
#> 4 2 2.46 0.141200
#> 5 2 3.27 0.128700
#> 6 3 6.46 0.117300
#> 7 3 8.90 0.106900
#> 8 3 10.75 0.097360
#> 9 3 12.21 0.088710
#> 10 3 13.38 0.080830
#> 11 3 14.35 0.073650
#> 12 5 15.20 0.067110
#> 13 5 15.97 0.061140
#> 14 5 16.62 0.055710
#> 15 5 17.17 0.050760
#> 16 5 17.64 0.046250
#> 17 5 18.03 0.042140
#> 18 6 18.37 0.038400
#> 19 6 18.67 0.034990
#> 20 6 18.92 0.031880
#> 21 6 19.13 0.029050
#> 22 6 19.31 0.026470
#> 23 6 19.46 0.024120
#> 24 6 19.59 0.021970
#> 25 6 19.70 0.020020
#> 26 6 19.79 0.018240
#> 27 6 19.87 0.016620
#> 28 6 19.94 0.015150
#> 29 6 19.99 0.013800
#> 30 6 20.04 0.012570
#> 31 6 20.08 0.011460
#> 32 6 20.11 0.010440
#> 33 6 20.14 0.009512
#> 34 6 20.16 0.008667
#> 35 6 20.18 0.007897
#> 36 6 20.20 0.007195
#> 37 6 20.21 0.006556
#> 38 6 20.22 0.005974
#> 39 6 20.23 0.005443
#> 40 6 20.24 0.004960
#> 41 6 20.24 0.004519
#> 42 6 20.25 0.004118
#> 43 6 20.26 0.003752
#> 44 6 20.26 0.003418
#> 45 6 20.26 0.003115
#>
#> $x
#> age los revasc revascdays stchange sysbp
#> [1,] 28 9 0 180 1 107
#> [2,] 32 5 1 0 1 121
#> [3,] 33 2 0 2 0 150
#> [4,] 35 5 1 2 0 172
#> [5,] 35 2 0 180 0 121
#> [6,] 35 2 1 1 1 112
#> [7,] 37 9 0 180 1 151
#> [8,] 38 2 0 115 0 150
#> [9,] 36 5 1 0 1 115
#> [10,] 33 6 1 1 1 115
#> [11,] 38 12 1 11 1 92
#> [12,] 40 12 1 9 0 153
#> [13,] 37 1 1 0 1 146
#> [14,] 40 2 1 1 1 148
#> [15,] 42 2 0 2 0 140
#> [16,] 40 6 0 180 1 138
#> [17,] 40 11 1 10 1 120
#> [18,] 42 2 0 180 0 100
#> [19,] 43 3 1 0 1 100
#> [20,] 43 4 1 0 1 130
#> [21,] 42 15 1 13 1 125
#> [22,] 40 3 1 1 0 170
#> [23,] 42 2 0 180 1 124
#> [24,] 44 5 1 1 0 170
#> [25,] 41 10 1 8 0 150
#> [26,] 41 13 1 1 0 140
#> [27,] 45 9 1 7 0 110
#> [28,] 45 6 0 180 1 170
#> [29,] 41 5 1 4 1 141
#> [30,] 44 2 1 1 1 150
#> [31,] 43 2 0 180 1 140
#> [32,] 45 2 0 180 1 140
#> [33,] 47 4 1 3 0 118
#> [34,] 48 15 0 180 1 160
#> [35,] 45 3 0 150 0 130
#> [36,] 44 3 1 0 1 180
#> [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,] 47 4 1 3 1 160
#> [43,] 45 5 0 5 0 141
#> [44,] 46 2 1 1 1 122
#> [45,] 46 6 1 0 1 100
#> [46,] 47 2 0 180 0 108
#> [47,] 44 9 1 8 1 135
#> [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,] 45 9 1 0 1 145
#> [54,] 47 3 1 1 1 120
#> [55,] 48 12 1 11 0 200
#> [56,] 47 5 1 3 1 130
#> [57,] 47 9 1 6 0 170
#> [58,] 46 3 1 0 1 119
#> [59,] 49 4 0 180 0 117
#> [60,] 47 10 0 10 1 140
#> [61,] 50 1 1 0 1 129
#> [62,] 48 2 1 0 0 184
#> [63,] 50 4 1 1 0 125
#> [64,] 50 6 1 2 1 140
#> [65,] 46 9 1 9 1 122
#> [66,] 50 7 0 180 1 110
#> [67,] 51 1 0 1 1 145
#> [68,] 47 2 0 180 0 150
#> [69,] 46 6 1 0 1 156
#> [70,] 50 4 0 4 1 100
#> [71,] 51 3 1 2 0 113
#> [72,] 50 9 0 180 0 130
#> [73,] 49 7 1 4 1 90
#> [74,] 47 6 0 180 1 162
#> [75,] 52 2 0 180 0 155
#> [76,] 46 1 1 1 0 145
#> [77,] 50 4 1 1 0 150
#> [78,] 48 7 1 0 1 110
#> [79,] 48 17 1 10 0 111
#> [80,] 52 4 1 4 0 152
#> [81,] 49 9 1 3 0 102
#> [82,] 49 15 0 180 1 160
#> [83,] 53 5 0 180 1 140
#> [84,] 54 17 1 12 1 102
#> [85,] 53 5 0 77 0 159
#> [86,] 53 7 1 0 0 199
#> [87,] 51 3 1 1 0 140
#> [88,] 50 10 1 6 0 122
#> [89,] 50 14 1 13 0 170
#> [90,] 51 25 1 1 0 202
#> [91,] 49 5 1 2 1 150
#> [92,] 52 14 1 7 1 200
#> [93,] 48 6 0 180 0 160
#> [94,] 48 11 1 10 0 120
#> [95,] 53 4 1 0 1 156
#> [96,] 51 13 0 99 1 160
#> [97,] 54 9 1 0 1 138
#> [98,] 49 16 0 16 0 125
#> [99,] 55 3 1 1 0 150
#> [100,] 54 23 1 10 0 131
#> [101,] 52 7 1 2 0 154
#> [102,] 54 9 1 1 0 130
#> [103,] 55 4 1 2 0 150
#> [104,] 52 4 0 180 1 180
#> [105,] 51 13 1 11 0 145
#> [106,] 50 5 1 4 1 150
#> [107,] 54 4 1 0 1 121
#> [108,] 52 4 0 180 0 183
#> [109,] 50 3 0 174 1 153
#> [110,] 55 28 1 13 1 160
#> [111,] 49 6 1 0 1 130
#> [112,] 56 4 1 1 1 130
#> [113,] 52 5 0 175 1 117
#> [114,] 54 1 0 180 0 162
#> [115,] 56 3 0 180 1 193
#> [116,] 56 2 0 180 0 132
#> [117,] 55 5 1 4 1 120
#> [118,] 52 8 0 180 0 119
#> [119,] 53 18 1 9 1 150
#> [120,] 55 6 0 180 0 170
#> [121,] 52 16 0 16 0 152
#> [122,] 53 15 0 15 1 90
#> [123,] 53 4 0 180 1 150
#> [124,] 55 6 0 180 1 100
#> [125,] 54 12 1 0 1 190
#> [126,] 55 2 0 134 1 140
#> [127,] 54 3 0 180 0 128
#> [128,] 56 3 0 8 1 139
#> [129,] 54 7 1 2 0 129
#> [130,] 54 2 1 1 0 135
#> [131,] 52 9 1 3 0 170
#> [132,] 57 5 1 3 1 138
#> [133,] 57 1 0 1 1 100
#> [134,] 56 4 1 0 1 140
#> [135,] 52 2 0 180 0 140
#> [136,] 52 15 1 14 0 130
#> [137,] 56 14 1 11 0 130
#> [138,] 57 10 0 180 1 170
#> [139,] 58 8 0 8 1 130
#> [140,] 54 5 0 180 1 108
#> [141,] 55 3 1 1 1 156
#> [142,] 57 0 0 0 1 150
#> [143,] 53 21 1 13 1 130
#> [144,] 57 4 0 180 1 119
#> [145,] 53 15 1 10 1 130
#> [146,] 54 17 1 8 1 227
#> [147,] 55 9 1 2 1 147
#> [148,] 57 4 1 2 1 185
#> [149,] 53 4 0 147 1 145
#> [150,] 57 11 1 10 1 129
#> [151,] 55 3 1 2 0 140
#> [152,] 55 5 0 5 1 131
#> [153,] 54 7 1 0 1 141
#> [154,] 56 4 0 4 0 164
#> [155,] 58 9 1 0 1 180
#> [156,] 58 1 1 1 1 200
#> [157,] 56 7 1 5 1 120
#> [158,] 60 11 1 9 0 106
#> [159,] 59 3 0 180 0 120
#> [160,] 60 5 1 1 0 138
#> [161,] 57 5 0 180 1 130
#> [162,] 58 11 1 9 1 124
#> [163,] 55 5 1 0 1 160
#> [164,] 57 10 1 9 0 103
#> [165,] 59 6 1 0 1 140
#> [166,] 59 5 0 180 1 155
#> [167,] 59 4 1 0 1 152
#> [168,] 61 9 0 9 1 160
#> [169,] 60 0 1 0 1 80
#> [170,] 59 2 1 1 0 140
#> [171,] 58 8 0 161 1 140
#> [172,] 61 9 1 8 0 150
#> [173,] 58 1 0 1 1 100
#> [174,] 57 13 1 10 0 110
#> [175,] 61 3 0 17 0 143
#> [176,] 59 9 1 0 1 80
#> [177,] 55 4 1 3 1 160
#> [178,] 58 11 0 172 1 135
#> [179,] 56 8 1 8 0 120
#> [180,] 57 1 0 1 0 126
#> [181,] 59 5 1 2 0 182
#> [182,] 61 8 0 77 0 120
#> [183,] 61 13 0 13 0 210
#> [184,] 58 8 1 5 0 152
#> [185,] 62 10 1 0 1 153
#> [186,] 62 7 1 2 1 180
#> [187,] 57 3 1 0 0 100
#> [188,] 61 18 0 170 0 140
#> [189,] 57 7 0 169 0 180
#> [190,] 61 7 0 7 1 150
#> [191,] 60 7 0 7 0 147
#> [192,] 61 6 0 6 0 134
#> [193,] 59 13 1 2 0 198
#> [194,] 57 12 1 9 1 120
#> [195,] 62 4 1 0 0 160
#> [196,] 60 17 1 8 1 140
#> [197,] 58 3 1 0 1 146
#> [198,] 62 4 1 3 0 173
#> [199,] 58 2 0 30 0 202
#> [200,] 59 1 0 180 0 155
#> [201,] 59 16 1 9 1 133
#> [202,] 61 13 0 13 0 120
#> [203,] 58 11 1 9 0 179
#> [204,] 57 2 1 1 0 159
#> [205,] 58 7 0 180 1 150
#> [206,] 63 3 1 1 0 180
#> [207,] 63 1 0 180 1 130
#> [208,] 61 7 0 180 0 135
#> [209,] 62 3 0 180 1 105
#> [210,] 63 15 1 10 1 126
#> [211,] 64 4 0 180 0 130
#> [212,] 60 18 1 13 0 132
#> [213,] 59 8 0 180 1 140
#> [214,] 58 9 1 9 0 110
#> [215,] 62 7 0 7 0 150
#> [216,] 58 2 0 180 0 127
#> [217,] 59 4 0 180 0 196
#> [218,] 60 7 1 5 1 141
#> [219,] 59 5 1 1 0 148
#> [220,] 63 1 0 1 0 162
#> [221,] 63 1 0 1 0 130
#> [222,] 61 15 1 13 0 170
#> [223,] 59 4 0 4 0 149
#> [224,] 60 3 0 3 0 168
#> [225,] 64 10 1 9 0 160
#> [226,] 59 10 0 180 1 130
#> [227,] 60 8 0 17 1 130
#> [228,] 61 6 1 1 1 117
#> [229,] 64 12 1 11 0 160
#> [230,] 66 1 1 0 1 120
#> [231,] 64 6 1 0 1 140
#> [232,] 63 10 1 0 1 148
#> [233,] 63 14 1 9 0 123
#> [234,] 63 4 1 3 0 162
#> [235,] 66 3 1 1 0 127
#> [236,] 61 10 1 2 1 194
#> [237,] 64 32 1 9 1 160
#> [238,] 63 12 1 9 0 114
#> [239,] 63 7 0 180 0 120
#> [240,] 65 8 1 0 0 168
#> [241,] 65 10 1 8 1 120
#> [242,] 64 0 0 0 1 90
#> [243,] 60 6 0 180 0 130
#> [244,] 64 21 1 10 0 190
#> [245,] 64 9 0 180 0 150
#> [246,] 61 4 0 180 1 113
#> [247,] 65 3 0 180 1 190
#> [248,] 63 16 1 7 1 110
#> [249,] 64 7 0 180 1 120
#> [250,] 66 6 1 1 1 130
#> [251,] 63 12 0 12 1 150
#> [252,] 62 3 1 1 1 199
#> [253,] 65 6 0 9 0 112
#> [254,] 65 3 1 0 1 80
#> [255,] 63 5 1 4 0 170
#> [256,] 63 2 1 1 0 180
#> [257,] 62 13 1 11 0 180
#> [258,] 64 2 0 2 0 201
#> [259,] 66 18 1 5 0 142
#> [260,] 62 9 0 180 0 145
#> [261,] 61 15 1 10 0 130
#> [262,] 63 9 1 8 1 160
#> [263,] 63 2 1 0 0 140
#> [264,] 64 19 1 8 1 160
#> [265,] 65 8 1 0 1 140
#> [266,] 67 6 0 180 1 170
#> [267,] 68 5 1 4 1 150
#> [268,] 64 13 1 12 1 150
#> [269,] 64 6 1 0 1 125
#> [270,] 66 13 1 0 0 118
#> [271,] 64 0 0 0 1 148
#> [272,] 66 3 1 0 1 135
#> [273,] 66 6 1 0 1 140
#> [274,] 65 2 1 1 1 170
#> [275,] 64 10 1 9 1 110
#> [276,] 63 7 1 0 0 162
#> [277,] 68 5 0 5 1 90
#> [278,] 66 14 0 180 0 130
#> [279,] 65 17 1 14 1 100
#> [280,] 63 8 1 1 1 162
#> [281,] 63 1 1 0 1 155
#> [282,] 63 10 0 18 1 130
#> [283,] 67 11 0 11 0 150
#> [284,] 68 14 0 79 0 172
#> [285,] 66 12 1 10 1 150
#> [286,] 66 11 1 0 0 100
#> [287,] 65 4 1 2 1 145
#> [288,] 69 12 0 15 1 140
#> [289,] 66 15 1 13 1 160
#> [290,] 63 2 0 180 0 150
#> [291,] 65 11 1 6 0 130
#> [292,] 69 21 1 10 0 180
#> [293,] 66 9 1 8 0 130
#> [294,] 68 14 1 13 1 140
#> [295,] 65 8 1 0 1 90
#> [296,] 66 3 0 3 1 138
#> [297,] 69 1 1 0 0 170
#> [298,] 67 1 0 180 1 160
#> [299,] 68 10 1 10 1 150
#> [300,] 67 7 1 4 1 130
#> [301,] 63 2 1 0 0 99
#> [302,] 65 6 0 6 0 80
#> [303,] 65 10 1 1 1 148
#> [304,] 66 19 1 12 1 150
#> [305,] 66 4 0 180 1 130
#> [306,] 70 15 1 12 1 132
#> [307,] 64 11 0 11 0 125
#> [308,] 64 4 0 180 1 140
#> [309,] 64 0 1 0 1 118
#> [310,] 67 2 0 18 0 131
#> [311,] 66 7 1 5 1 131
#> [312,] 66 4 0 180 0 177
#> [313,] 68 4 1 0 1 160
#> [314,] 65 13 1 12 1 130
#> [315,] 69 8 0 93 0 140
#> [316,] 64 21 0 21 1 155
#> [317,] 66 6 0 180 0 140
#> [318,] 65 1 0 1 1 120
#> [319,] 65 6 0 101 1 115
#> [320,] 68 4 0 4 1 190
#> [321,] 67 2 0 180 0 128
#> [322,] 66 9 1 3 1 151
#> [323,] 69 8 0 180 1 153
#> [324,] 70 14 0 171 0 166
#> [325,] 66 4 0 180 0 130
#> [326,] 67 10 1 9 0 200
#> [327,] 68 18 1 14 1 170
#> [328,] 69 0 0 0 1 148
#> [329,] 65 2 0 180 0 130
#> [330,] 68 7 1 0 1 150
#> [331,] 69 3 1 2 0 151
#> [332,] 67 14 1 13 0 130
#> [333,] 66 2 0 2 1 228
#> [334,] 71 3 0 103 0 133
#> [335,] 69 3 0 3 1 130
#> [336,] 67 1 0 36 1 104
#> [337,] 68 6 0 180 0 145
#> [338,] 69 6 1 4 1 174
#> [339,] 72 3 1 0 1 132
#> [340,] 72 7 0 7 1 110
#> [341,] 69 8 1 7 1 108
#> [342,] 67 3 0 180 0 110
#> [343,] 66 2 1 1 0 123
#> [344,] 69 19 0 180 0 130
#> [345,] 68 18 0 18 1 100
#> [346,] 67 14 0 172 1 140
#> [347,] 67 7 1 4 0 122
#> [348,] 69 4 1 3 0 132
#> [349,] 69 8 1 2 0 121
#> [350,] 70 9 0 180 1 142
#> [351,] 72 5 1 4 0 170
#> [352,] 68 3 0 19 0 135
#> [353,] 69 1 0 1 1 110
#> [354,] 67 4 0 60 1 136
#> [355,] 69 5 0 76 0 120
#> [356,] 72 13 1 11 1 195
#> [357,] 68 10 1 8 1 160
#> [358,] 66 24 1 13 0 130
#> [359,] 70 35 1 0 1 105
#> [360,] 70 7 0 7 0 102
#> [361,] 68 7 1 2 0 135
#> [362,] 71 6 0 9 0 120
#> [363,] 69 10 1 6 1 120
#> [364,] 70 11 0 180 1 210
#> [365,] 72 19 1 8 0 120
#> [366,] 72 12 1 10 0 170
#> [367,] 67 9 0 180 0 158
#> [368,] 73 13 0 152 1 130
#> [369,] 70 5 0 180 0 150
#> [370,] 72 6 1 5 0 115
#> [371,] 68 23 0 180 1 220
#> [372,] 69 3 0 180 0 220
#> [373,] 68 4 1 3 0 210
#> [374,] 72 5 0 28 0 120
#> [375,] 71 5 0 180 0 191
#> [376,] 69 16 1 10 1 140
#> [377,] 69 8 1 1 0 164
#> [378,] 68 7 0 180 1 130
#> [379,] 72 16 1 1 1 130
#> [380,] 70 4 0 180 0 180
#> [381,] 72 8 1 1 1 150
#> [382,] 71 2 1 0 1 180
#> [383,] 73 7 0 7 1 140
#> [384,] 68 15 1 13 1 130
#> [385,] 70 3 0 3 1 159
#> [386,] 72 6 0 180 1 130
#> [387,] 74 8 1 0 1 85
#> [388,] 73 4 0 180 1 154
#> [389,] 69 2 1 0 1 110
#> [390,] 71 3 1 1 0 150
#> [391,] 71 15 1 11 0 165
#> [392,] 74 20 0 20 1 180
#> [393,] 68 9 0 180 1 120
#> [394,] 74 0 1 0 1 90
#> [395,] 70 5 1 0 1 190
#> [396,] 73 10 1 8 0 106
#> [397,] 69 12 1 1 1 149
#> [398,] 70 26 1 11 1 120
#> [399,] 74 4 0 4 0 120
#> [400,] 70 3 0 180 1 154
#> [401,] 73 6 0 180 0 110
#> [402,] 72 15 1 0 1 150
#> [403,] 71 7 1 2 0 143
#> [404,] 74 3 0 3 1 150
#> [405,] 73 17 1 11 0 140
#> [406,] 71 13 1 8 0 121
#> [407,] 71 14 1 13 1 170
#> [408,] 74 7 1 0 1 117
#> [409,] 72 10 1 8 1 153
#> [410,] 71 10 1 9 1 120
#> [411,] 75 1 0 1 0 133
#> [412,] 75 2 1 1 0 145
#> [413,] 73 10 1 9 1 146
#> [414,] 73 10 1 10 1 120
#> [415,] 74 15 1 9 1 179
#> [416,] 73 1 0 1 1 80
#> [417,] 75 9 1 7 0 140
#> [418,] 71 4 0 4 0 134
#> [419,] 72 15 1 12 1 120
#> [420,] 73 10 1 8 0 120
#> [421,] 72 1 1 1 0 168
#> [422,] 73 10 0 180 0 162
#> [423,] 72 11 0 11 1 140
#> [424,] 70 3 0 3 0 150
#> [425,] 73 5 1 3 1 112
#> [426,] 76 25 1 12 1 170
#> [427,] 73 12 1 12 1 140
#> [428,] 72 2 0 180 0 120
#> [429,] 75 1 0 180 1 140
#> [430,] 72 4 1 0 1 197
#> [431,] 71 3 1 0 0 144
#> [432,] 73 5 0 180 0 126
#> [433,] 76 3 1 0 1 120
#> [434,] 71 32 1 12 1 107
#> [435,] 72 3 0 180 0 160
#> [436,] 77 11 0 11 1 150
#> [437,] 77 4 0 4 0 185
#> [438,] 72 7 1 2 0 142
#> [439,] 73 15 0 15 1 160
#> [440,] 71 16 0 180 0 140
#> [441,] 74 3 0 3 1 128
#> [442,] 76 1 0 180 0 114
#> [443,] 76 8 1 0 1 141
#> [444,] 74 19 1 4 1 200
#> [445,] 75 23 1 14 1 110
#> [446,] 74 2 0 180 0 190
#> [447,] 72 4 0 85 1 120
#> [448,] 72 4 1 3 0 160
#> [449,] 76 17 1 0 1 200
#> [450,] 75 4 1 0 1 122
#> [451,] 75 0 0 0 1 130
#> [452,] 75 12 0 12 1 160
#> [453,] 76 13 1 8 1 148
#> [454,] 75 4 1 2 1 188
#> [455,] 74 2 0 180 0 111
#> [456,] 76 5 0 180 0 185
#> [457,] 75 9 0 180 1 140
#> [458,] 77 5 1 0 0 123
#> [459,] 73 10 1 9 0 146
#> [460,] 77 1 1 0 1 90
#> [461,] 76 12 1 11 1 120
#> [462,] 78 5 1 0 1 170
#> [463,] 73 7 1 0 0 174
#> [464,] 75 3 1 1 1 171
#> [465,] 75 6 0 180 0 150
#> [466,] 78 18 0 18 1 144
#> [467,] 77 3 0 180 0 110
#> [468,] 74 2 0 180 0 100
#> [469,] 74 15 0 180 1 172
#> [470,] 74 7 0 7 0 161
#> [471,] 76 13 1 1 1 170
#> [472,] 78 32 1 9 1 198
#> [473,] 79 6 0 180 0 170
#> [474,] 78 0 0 180 1 212
#> [475,] 78 13 1 5 0 130
#> [476,] 75 12 1 1 1 120
#> [477,] 78 15 0 180 1 270
#> [478,] 80 8 0 8 1 120
#> [479,] 74 10 1 8 0 135
#> [480,] 76 1 0 1 1 83
#> [481,] 79 4 0 80 0 145
#> [482,] 78 12 1 9 0 150
#> [483,] 78 2 1 1 0 130
#> [484,] 75 4 1 0 0 212
#> [485,] 77 2 1 0 1 143
#> [486,] 78 10 0 180 1 130
#> [487,] 76 11 1 0 0 120
#> [488,] 75 11 1 4 0 162
#> [489,] 75 3 0 3 0 0
#> [490,] 76 7 0 29 1 150
#> [491,] 78 6 1 0 1 240
#> [492,] 76 3 1 0 1 140
#> [493,] 78 11 1 1 1 140
#> [494,] 79 11 0 180 0 160
#> [495,] 81 1 0 1 0 130
#> [496,] 76 4 0 4 1 160
#> [497,] 79 4 0 4 1 125
#> [498,] 76 10 1 8 0 180
#> [499,] 77 6 0 6 1 107
#> [500,] 80 3 1 0 1 120
#> [501,] 75 2 1 1 1 204
#> [502,] 78 11 0 180 1 135
#> [503,] 76 1 0 1 1 140
#> [504,] 77 31 1 3 1 161
#> [505,] 76 1 0 1 1 90
#> [506,] 78 7 1 0 1 110
#> [507,] 77 7 0 180 1 170
#> [508,] 80 15 1 12 1 150
#> [509,] 77 9 1 4 0 141
#> [510,] 82 5 0 8 1 120
#> [511,] 78 4 0 59 1 112
#> [512,] 76 7 0 161 0 151
#> [513,] 79 10 0 10 1 120
#> [514,] 80 15 1 0 1 90
#> [515,] 81 4 1 2 1 126
#> [516,] 79 28 0 164 0 100
#> [517,] 80 6 0 173 1 160
#> [518,] 78 32 0 180 1 130
#> [519,] 79 1 0 37 1 140
#> [520,] 81 3 0 180 0 184
#> [521,] 81 2 0 175 0 172
#> [522,] 78 7 0 7 1 147
#> [523,] 80 5 1 1 1 108
#> [524,] 78 4 0 180 0 175
#> [525,] 76 1 0 166 0 131
#> [526,] 81 4 1 1 1 104
#> [527,] 78 20 1 0 1 109
#> [528,] 78 3 1 1 1 152
#> [529,] 77 10 1 8 1 130
#> [530,] 77 5 0 85 0 188
#> [531,] 80 2 1 1 0 168
#> [532,] 79 6 0 6 0 152
#> [533,] 80 6 1 0 1 119
#> [534,] 78 2 0 180 0 148
#> [535,] 79 10 0 180 1 150
#> [536,] 77 4 0 180 1 98
#> [537,] 81 1 0 108 0 129
#> [538,] 79 1 0 125 0 193
#> [539,] 84 22 1 10 0 180
#> [540,] 79 4 0 4 1 121
#> [541,] 80 6 0 6 1 110
#> [542,] 83 9 1 5 1 170
#> [543,] 82 5 0 180 0 110
#> [544,] 83 5 0 180 0 148
#> [545,] 79 7 1 6 0 130
#> [546,] 83 4 0 103 0 97
#> [547,] 81 11 1 8 0 160
#> [548,] 80 11 1 8 0 170
#> [549,] 78 23 1 10 1 145
#> [550,] 79 4 0 4 1 183
#> [551,] 78 9 1 4 1 120
#> [552,] 82 8 1 1 0 128
#> [553,] 79 1 0 180 1 170
#> [554,] 81 15 0 180 1 140
#> [555,] 84 5 1 1 1 85
#> [556,] 81 20 1 9 0 170
#> [557,] 83 8 0 8 0 115
#> [558,] 81 16 0 16 1 110
#> [559,] 80 11 1 8 0 110
#> [560,] 85 4 0 180 0 90
#> [561,] 81 2 1 1 0 198
#> [562,] 83 2 0 2 1 155
#> [563,] 82 6 0 128 1 100
#> [564,] 84 4 0 167 0 198
#> [565,] 80 3 1 1 1 230
#> [566,] 82 23 1 0 0 110
#> [567,] 84 5 0 180 1 203
#> [568,] 84 4 0 4 1 85
#> [569,] 81 1 0 1 1 150
#> [570,] 84 1 0 38 1 205
#> [571,] 83 3 0 180 0 174
#> [572,] 81 4 0 90 1 138
#> [573,] 79 9 1 8 0 150
#> [574,] 85 3 1 2 1 160
#> [575,] 84 4 0 89 1 129
#> [576,] 79 4 0 4 1 60
#> [577,] 80 6 0 71 1 189
#> [578,] 83 1 0 1 1 100
#> [579,] 82 19 0 19 0 120
#> [580,] 80 30 1 13 0 220
#> [581,] 83 9 0 180 0 198
#> [582,] 79 14 1 0 0 110
#> [583,] 81 14 1 12 1 128
#> [584,] 83 2 0 154 0 130
#> [585,] 82 0 0 2 1 100
#> [586,] 85 9 1 6 1 160
#> [587,] 83 1 0 180 0 160
#> [588,] 81 4 0 4 0 175
#> [589,] 84 15 1 13 1 110
#> [590,] 82 16 1 8 0 103
#> [591,] 86 12 0 180 1 120
#> [592,] 82 15 1 0 0 183
#> [593,] 80 2 0 88 0 135
#> [594,] 83 7 0 126 0 135
#> [595,] 86 8 0 8 1 132
#> [596,] 81 16 1 9 0 180
#> [597,] 82 9 0 180 1 134
#> [598,] 84 3 0 180 1 120
#> [599,] 81 13 0 180 0 152
#> [600,] 85 3 0 3 1 118
#> [601,] 83 4 0 4 0 130
#> [602,] 87 2 0 5 1 137
#> [603,] 86 12 1 0 1 132
#> [604,] 84 16 0 70 1 150
#> [605,] 83 19 0 43 0 150
#> [606,] 84 3 1 2 0 125
#> [607,] 83 10 1 0 1 190
#> [608,] 86 2 0 180 1 169
#> [609,] 84 3 0 3 1 121
#> [610,] 83 13 1 12 0 170
#> [611,] 84 7 1 2 0 148
#> [612,] 87 2 0 180 0 113
#> [613,] 84 9 0 92 1 110
#> [614,] 84 3 0 180 1 170
#> [615,] 86 4 0 38 1 122
#> [616,] 86 13 0 177 0 163
#> [617,] 86 6 0 6 1 117
#> [618,] 86 6 1 1 0 112
#> [619,] 88 4 0 4 0 100
#> [620,] 88 4 0 4 1 115
#> [621,] 83 9 0 65 1 150
#> [622,] 86 6 0 46 0 173
#> [623,] 88 3 0 115 0 110
#> [624,] 87 8 0 8 1 157
#> [625,] 88 4 0 4 0 86
#> [626,] 89 4 0 4 1 153
#> [627,] 87 6 0 180 1 110
#> [628,] 84 8 0 180 1 119
#> [629,] 85 8 0 8 1 136
#> [630,] 84 2 0 110 1 174
#> [631,] 87 29 0 29 1 97
#> [632,] 87 15 1 9 1 138
#> [633,] 89 10 0 46 1 170
#> [634,] 88 1 0 1 0 135
#> [635,] 87 2 0 180 0 160
#> [636,] 87 6 1 0 0 125
#> [637,] 91 10 0 145 0 135
#> [638,] 86 3 1 0 1 80
#> [639,] 88 7 0 24 0 119
#> [640,] 88 8 0 50 1 154
#> [641,] 90 11 1 10 1 186
#> [642,] 86 10 0 180 1 137
#> [643,] 86 9 1 7 0 130
#> [644,] 90 4 1 0 0 121
#> [645,] 91 1 0 1 1 74
#> [646,] 87 43 0 178 1 130
#> [647,] 89 3 1 1 1 160
#> [648,] 92 1 0 1 1 167
#> [649,] 91 3 0 33 1 137
#> [650,] 87 7 0 74 1 105
#> [651,] 89 12 1 0 1 130
#> [652,] 89 4 0 4 1 159
#> [653,] 91 0 0 0 0 0
#> [654,] 89 14 0 180 1 84
#> [655,] 90 18 0 180 0 188
#> [656,] 91 4 1 0 1 120
#> [657,] 90 19 1 11 1 129
#> [658,] 91 2 0 2 1 116
#> [659,] 93 8 0 179 1 110
#> [660,] 92 4 0 76 1 149
#> [661,] 91 1 0 180 0 158
#> [662,] 90 3 0 67 0 162
#> [663,] 96 3 0 12 1 97
#> [664,] 95 8 1 5 1 150
#> [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+ 5.0+ 2.0+ 5.0+ 180.0+ 2.0+ 180.0+ 115.0 5.0+ 180.0+
#> [11] 180.0+ 180.0+ 180.0+ 2.0+ 2.0+ 180.0+ 180.0+ 180.0+ 3.0 180.0+
#> [21] 180.0+ 180.0+ 180.0+ 155.0+ 180.0+ 180.0+ 180.0+ 180.0+ 5.0+ 180.0+
#> [31] 180.0+ 180.0+ 180.0+ 180.0+ 150.0 180.0+ 180.0+ 180.0+ 180.0+ 6.0+
#> [41] 180.0+ 180.0+ 5.0+ 161.0+ 180.0+ 180.0+ 180.0+ 180.0+ 5.0+ 180.0+
#> [51] 180.0+ 180.0+ 177.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 10.0+
#> [61] 172.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 1.0 180.0+ 180.0+ 4.0+
#> [71] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 7.0 88.0+ 4.0+
#> [81] 180.0+ 180.0+ 180.0+ 180.0+ 77.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [91] 180.0+ 85.0 180.0+ 180.0+ 166.0+ 99.0 180.0+ 16.0+ 180.0+ 152.0+
#> [101] 7.0+ 180.0+ 180.0+ 180.0+ 13.0+ 171.0+ 180.0+ 180.0+ 174.0+ 28.0
#> [111] 6.0+ 180.0+ 175.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [121] 16.0+ 15.0+ 180.0+ 180.0+ 12.0+ 134.0+ 180.0+ 8.0 180.0+ 180.0+
#> [131] 180.0+ 140.0 1.0 165.0 180.0+ 180.0+ 180.0+ 180.0+ 8.0+ 180.0+
#> [141] 180.0+ 0.5 180.0+ 180.0+ 180.0+ 171.0+ 15.0 4.0+ 147.0+ 180.0+
#> [151] 180.0+ 5.0+ 180.0+ 4.0+ 9.0+ 1.0 180.0+ 180.0+ 180.0+ 180.0+
#> [161] 180.0+ 180.0+ 180.0+ 180.0+ 64.0 180.0+ 180.0+ 9.0+ 0.5 180.0+
#> [171] 161.0+ 180.0+ 1.0 180.0+ 17.0 9.0+ 180.0+ 172.0+ 8.0 1.0+
#> [181] 180.0+ 77.0 13.0+ 8.0+ 180.0+ 180.0+ 180.0+ 170.0 169.0 7.0
#> [191] 7.0+ 6.0 180.0+ 180.0+ 180.0+ 180.0+ 3.0+ 180.0+ 30.0 180.0+
#> [201] 180.0+ 13.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [211] 180.0+ 180.0+ 180.0+ 9.0 7.0+ 180.0+ 180.0+ 84.0 180.0+ 1.0
#> [221] 1.0 180.0+ 4.0+ 3.0+ 167.0 180.0+ 17.0 180.0+ 12.0 180.0+
#> [231] 180.0+ 180.0+ 14.0+ 180.0+ 3.0+ 88.0 180.0+ 12.0 180.0+ 180.0+
#> [241] 180.0+ 0.5 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [251] 12.0 180.0+ 9.0 3.0 180.0+ 180.0+ 180.0+ 2.0+ 18.0+ 180.0+
#> [261] 180.0+ 180.0+ 2.0+ 103.0 15.0 180.0+ 5.0+ 13.0 180.0+ 166.0+
#> [271] 0.5+ 3.0+ 180.0+ 175.0+ 180.0+ 7.0+ 5.0 180.0+ 180.0+ 180.0+
#> [281] 1.0+ 18.0 11.0+ 79.0 80.0 180.0+ 4.0+ 15.0 180.0+ 180.0+
#> [291] 180.0+ 174.0+ 180.0+ 180.0+ 8.0+ 3.0 175.0 180.0+ 10.0 180.0+
#> [301] 180.0+ 6.0 180.0+ 19.0+ 180.0+ 180.0+ 11.0+ 180.0+ 0.5 18.0
#> [311] 7.0+ 180.0+ 180.0+ 180.0+ 93.0 21.0+ 180.0+ 1.0 101.0 4.0
#> [321] 180.0+ 180.0+ 180.0+ 171.0 180.0+ 174.0+ 180.0+ 0.5 180.0+ 180.0+
#> [331] 180.0+ 180.0+ 2.0 103.0 3.0+ 36.0 180.0+ 97.0 180.0+ 7.0
#> [341] 8.0+ 180.0+ 2.0+ 180.0+ 18.0 172.0+ 7.0 180.0+ 8.0+ 180.0+
#> [351] 180.0+ 19.0 1.0 60.0 76.0 132.0 10.0+ 180.0+ 180.0+ 7.0+
#> [361] 7.0+ 9.0 180.0+ 180.0+ 180.0+ 12.0 180.0+ 152.0 180.0+ 180.0+
#> [371] 180.0+ 180.0+ 180.0+ 28.0 180.0+ 16.0+ 180.0+ 180.0+ 16.0+ 180.0+
#> [381] 180.0+ 180.0+ 7.0+ 15.0 3.0+ 180.0+ 180.0+ 180.0+ 2.0 3.0+
#> [391] 180.0+ 20.0 180.0+ 0.5 180.0+ 87.0 12.0 180.0+ 4.0+ 180.0+
#> [401] 180.0+ 180.0+ 180.0+ 3.0 180.0+ 175.0 14.0+ 180.0+ 10.0+ 179.0+
#> [411] 1.0 180.0+ 180.0+ 15.0 180.0+ 1.0 180.0+ 4.0+ 180.0+ 10.0
#> [421] 1.0 180.0+ 11.0 3.0+ 5.0 180.0+ 12.0 180.0+ 180.0+ 180.0+
#> [431] 180.0+ 180.0+ 180.0+ 177.0+ 180.0+ 11.0+ 4.0+ 7.0 15.0+ 180.0+
#> [441] 3.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 85.0 180.0+ 17.0+ 4.0
#> [451] 0.5 12.0 180.0+ 46.0 180.0+ 180.0+ 180.0+ 5.0 180.0+ 1.0
#> [461] 12.0 180.0+ 7.0+ 3.0 180.0+ 18.0 180.0+ 180.0+ 180.0+ 7.0
#> [471] 180.0+ 32.0 180.0+ 180.0+ 172.0 12.0 180.0+ 8.0 180.0+ 1.0
#> [481] 80.0 180.0+ 180.0+ 4.0+ 2.0 180.0+ 11.0 152.0+ 3.0 29.0
#> [491] 180.0+ 3.0+ 180.0+ 180.0+ 1.0 4.0 4.0 10.0+ 6.0 3.0+
#> [501] 2.0+ 180.0+ 1.0 171.0 1.0 43.0 180.0+ 180.0+ 71.0 8.0
#> [511] 59.0 161.0 10.0+ 180.0+ 93.0 164.0 173.0 180.0+ 37.0 180.0+
#> [521] 175.0+ 7.0+ 5.0+ 180.0+ 166.0+ 71.0 20.0+ 3.0+ 10.0 85.0
#> [531] 10.0 6.0+ 6.0 180.0+ 180.0+ 180.0+ 108.0 125.0 180.0+ 4.0
#> [541] 6.0 9.0+ 180.0+ 180.0+ 180.0+ 103.0 180.0+ 169.0 70.0 4.0
#> [551] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 20.0 8.0+ 16.0 180.0+ 180.0+
#> [561] 180.0+ 2.0 128.0 167.0 3.0+ 62.0 180.0+ 4.0 1.0 38.0
#> [571] 180.0+ 90.0 180.0+ 180.0+ 89.0 4.0 71.0 1.0 19.0 30.0
#> [581] 180.0+ 180.0+ 180.0+ 154.0 2.0 180.0+ 180.0+ 4.0+ 180.0+ 16.0+
#> [591] 180.0+ 83.0 88.0 126.0 8.0 180.0+ 180.0+ 180.0+ 180.0+ 3.0+
#> [601] 4.0+ 5.0 180.0+ 70.0 43.0 180.0+ 180.0+ 180.0+ 3.0 13.0
#> [611] 180.0+ 180.0+ 92.0 180.0+ 38.0 177.0 6.0+ 6.0+ 4.0+ 4.0
#> [621] 65.0 46.0 115.0 8.0+ 4.0 4.0 180.0+ 180.0+ 8.0 110.0
#> [631] 29.0 180.0+ 46.0 1.0+ 180.0+ 25.0 145.0 3.0 24.0 50.0
#> [641] 11.0 180.0+ 180.0+ 4.0 1.0 178.0+ 3.0+ 1.0 33.0 74.0
#> [651] 180.0+ 4.0 0.5 180.0+ 180.0+ 4.0 180.0+ 2.0 179.0+ 76.0
#> [661] 180.0+ 67.0 12.0 8.0 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$test)
#> Warning: Multiple lambdas have been fit. Lambda will be set to 0.01 (see parameter 's').
# Score the predictions
predictions$score()
#> surv.cindex
#> 0.8069356