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.
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 |
| 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 | \([0, 1]\) | |
| exclude | untyped | - | - | |
| exmx | numeric | 250 | \((-\infty, \infty)\) | |
| fdev | numeric | 1e-05 | \([0, 1]\) | |
| foldid | untyped | NULL | - | |
| 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 | \([1, \infty)\) | |
| nfolds | integer | 10 | \([3, \infty)\) | |
| 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 | lambda.1se | \([0, \infty)\) | |
| standardize | logical | TRUE | TRUE, FALSE | - |
| standardize.response | logical | FALSE | TRUE, FALSE | - |
| thresh | numeric | 1e-07 | \([0, \infty)\) | |
| trace.it | integer | 0 | \([0, 1]\) | |
| type.gaussian | character | - | covariance, naive | - |
| type.logistic | character | Newton | Newton, modified.Newton | - |
| type.measure | character | deviance | deviance, C | - |
| type.multinomial | character | ungrouped | ungrouped, grouped | - |
| upper.limits | untyped | Inf | - | |
| stype | integer | 2 | \([1, 2]\) | |
| ctype | integer | - | \([1, 2]\) |
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.
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
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.cv_glmnet")
print(learner)
#>
#> ── <LearnerSurvCVGlmnet> (surv.cv_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', 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)
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
#> Warning: Starting in glmnet 5.1, the default Cox tie-handling method will change from 'breslow' to 'efron' (matching survival::coxph). To silence this message and lock in the v5.0 default, pass cox.ties = 'breslow' explicitly. To preview the v5.1 behavior, pass cox.ties = 'efron'.
print(learner$model)
#> $model
#>
#> Call: (if (cv) glmnet::cv.glmnet else glmnet::glmnet)(x = data, y = target, family = "cox")
#>
#> Measure: Partial Likelihood Deviance
#>
#> Lambda Index Measure SE Nonzero
#> min 0.00291 46 2.715 0.1170 6
#> 1se 0.05705 14 2.814 0.1166 5
#>
#> $x
#> age los revasc revascdays stchange sysbp
#> [1,] 32 5 1 0 1 121
#> [2,] 33 2 0 2 0 150
#> [3,] 35 2 0 180 0 121
#> [4,] 35 2 1 1 1 112
#> [5,] 38 2 0 115 0 150
#> [6,] 36 1 0 180 1 155
#> [7,] 38 12 1 8 1 120
#> [8,] 36 5 1 0 1 115
#> [9,] 33 6 1 1 1 115
#> [10,] 38 16 1 10 0 160
#> [11,] 38 12 1 11 1 92
#> [12,] 40 12 1 9 0 153
#> [13,] 42 3 1 1 1 130
#> [14,] 37 1 1 0 1 146
#> [15,] 42 2 0 180 1 100
#> [16,] 38 5 1 3 0 125
#> [17,] 42 2 0 2 0 140
#> [18,] 40 6 0 180 1 138
#> [19,] 40 11 1 10 1 120
#> [20,] 42 2 0 180 0 100
#> [21,] 43 3 1 0 1 100
#> [22,] 40 1 1 0 1 145
#> [23,] 43 4 1 0 1 130
#> [24,] 42 4 0 180 0 162
#> [25,] 40 3 1 1 0 170
#> [26,] 42 12 1 10 1 170
#> [27,] 43 2 1 1 1 116
#> [28,] 41 10 1 8 0 150
#> [29,] 45 6 0 180 1 170
#> [30,] 41 5 1 4 1 141
#> [31,] 44 2 1 1 1 150
#> [32,] 43 2 0 180 1 140
#> [33,] 45 2 0 180 1 140
#> [34,] 47 4 1 3 0 118
#> [35,] 45 3 0 150 0 130
#> [36,] 44 3 1 0 1 180
#> [37,] 46 7 1 2 0 166
#> [38,] 43 29 0 180 1 180
#> [39,] 45 4 1 0 0 124
#> [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,] 45 5 0 180 1 190
#> [47,] 46 5 1 3 0 130
#> [48,] 46 4 0 180 1 121
#> [49,] 44 2 0 180 0 142
#> [50,] 46 15 0 180 1 120
#> [51,] 45 9 1 0 1 145
#> [52,] 47 3 1 1 1 120
#> [53,] 47 5 1 3 1 130
#> [54,] 47 9 1 6 0 170
#> [55,] 46 3 1 0 1 119
#> [56,] 49 4 0 180 0 117
#> [57,] 47 10 0 10 1 140
#> [58,] 50 1 1 0 1 129
#> [59,] 47 7 0 180 0 145
#> [60,] 50 6 1 2 1 140
#> [61,] 49 7 1 7 1 110
#> [62,] 46 9 1 9 1 122
#> [63,] 50 7 0 180 1 110
#> [64,] 51 1 0 1 1 145
#> [65,] 49 15 1 11 1 160
#> [66,] 49 23 0 179 1 112
#> [67,] 46 6 1 0 1 156
#> [68,] 52 2 0 180 1 170
#> [69,] 50 7 1 0 1 92
#> [70,] 50 4 0 4 1 100
#> [71,] 51 3 1 2 0 113
#> [72,] 50 1 1 0 0 150
#> [73,] 50 9 0 180 0 130
#> [74,] 49 7 1 4 1 90
#> [75,] 47 8 0 180 0 160
#> [76,] 47 6 0 180 1 162
#> [77,] 51 8 0 180 1 140
#> [78,] 52 2 0 180 0 155
#> [79,] 46 3 0 180 1 120
#> [80,] 46 1 1 1 0 145
#> [81,] 50 4 1 1 0 150
#> [82,] 48 7 1 0 1 110
#> [83,] 48 17 1 10 0 111
#> [84,] 52 4 1 4 0 152
#> [85,] 49 15 0 180 1 160
#> [86,] 53 5 0 180 1 140
#> [87,] 54 17 1 12 1 102
#> [88,] 53 5 0 77 0 159
#> [89,] 54 6 1 3 0 129
#> [90,] 50 2 0 5 1 106
#> [91,] 50 10 1 6 0 122
#> [92,] 50 14 1 13 0 170
#> [93,] 48 3 1 2 0 150
#> [94,] 49 5 1 2 1 150
#> [95,] 53 4 0 4 0 140
#> [96,] 52 14 1 7 1 200
#> [97,] 48 11 1 10 0 120
#> [98,] 49 16 0 16 0 125
#> [99,] 52 7 1 2 0 154
#> [100,] 54 9 1 1 0 130
#> [101,] 52 4 0 180 1 180
#> [102,] 51 13 1 11 0 145
#> [103,] 50 5 1 4 1 150
#> [104,] 54 4 1 0 1 121
#> [105,] 52 4 0 180 0 183
#> [106,] 50 3 0 174 1 153
#> [107,] 49 6 1 0 1 130
#> [108,] 49 1 0 1 1 110
#> [109,] 50 7 1 1 0 156
#> [110,] 53 9 0 9 1 95
#> [111,] 56 4 1 1 1 130
#> [112,] 52 5 0 175 1 117
#> [113,] 55 2 0 2 0 145
#> [114,] 54 1 0 180 0 162
#> [115,] 54 7 1 0 1 100
#> [116,] 56 3 0 180 1 193
#> [117,] 56 2 0 180 0 132
#> [118,] 55 5 1 4 1 120
#> [119,] 52 8 0 180 0 119
#> [120,] 53 18 1 9 1 150
#> [121,] 54 3 0 180 1 180
#> [122,] 55 6 0 180 0 170
#> [123,] 53 10 1 9 0 172
#> [124,] 52 16 1 14 0 170
#> [125,] 53 15 0 15 1 90
#> [126,] 55 6 0 180 1 100
#> [127,] 55 6 1 5 1 138
#> [128,] 54 12 1 0 1 190
#> [129,] 55 2 0 134 1 140
#> [130,] 54 3 0 180 0 128
#> [131,] 56 3 0 8 1 139
#> [132,] 57 3 0 3 0 120
#> [133,] 54 2 1 1 0 135
#> [134,] 52 9 1 3 0 170
#> [135,] 54 2 1 1 1 176
#> [136,] 57 5 1 3 1 138
#> [137,] 57 1 0 180 1 156
#> [138,] 57 1 0 1 1 100
#> [139,] 52 2 0 180 0 140
#> [140,] 55 11 1 7 0 104
#> [141,] 52 15 1 14 0 130
#> [142,] 58 8 0 8 1 130
#> [143,] 54 5 0 180 1 108
#> [144,] 55 3 1 1 1 156
#> [145,] 53 21 1 13 1 130
#> [146,] 59 3 1 1 0 172
#> [147,] 58 6 1 0 1 90
#> [148,] 53 15 1 10 1 130
#> [149,] 54 17 1 8 1 227
#> [150,] 55 13 0 166 1 140
#> [151,] 56 5 0 5 1 150
#> [152,] 53 4 0 147 1 145
#> [153,] 53 7 1 0 1 120
#> [154,] 57 11 1 10 1 129
#> [155,] 55 3 1 2 0 140
#> [156,] 55 5 0 5 1 131
#> [157,] 54 7 1 0 1 141
#> [158,] 56 4 0 4 0 164
#> [159,] 55 5 1 0 0 140
#> [160,] 56 7 1 5 1 120
#> [161,] 55 2 0 2 0 106
#> [162,] 59 9 1 1 1 125
#> [163,] 57 1 0 180 0 148
#> [164,] 60 11 1 9 0 106
#> [165,] 58 4 1 0 1 160
#> [166,] 57 5 0 180 1 130
#> [167,] 59 6 1 0 1 140
#> [168,] 59 5 0 180 1 155
#> [169,] 59 4 1 0 1 152
#> [170,] 58 26 1 0 1 189
#> [171,] 61 9 0 9 1 160
#> [172,] 58 4 1 3 0 120
#> [173,] 59 2 1 1 0 140
#> [174,] 58 8 0 161 1 140
#> [175,] 58 14 1 6 0 190
#> [176,] 61 4 1 3 0 151
#> [177,] 61 9 1 8 0 150
#> [178,] 61 3 1 2 1 102
#> [179,] 57 13 1 10 0 110
#> [180,] 57 2 1 0 1 116
#> [181,] 58 10 0 10 1 150
#> [182,] 57 4 1 3 0 138
#> [183,] 61 3 0 17 0 143
#> [184,] 57 3 1 2 0 120
#> [185,] 58 19 1 13 1 140
#> [186,] 56 13 1 6 1 158
#> [187,] 56 18 1 11 1 165
#> [188,] 55 4 1 3 1 160
#> [189,] 58 11 0 172 1 135
#> [190,] 60 12 1 0 1 114
#> [191,] 55 9 1 7 1 135
#> [192,] 61 4 1 0 1 115
#> [193,] 61 13 1 12 1 130
#> [194,] 59 11 1 8 1 190
#> [195,] 57 1 0 1 0 126
#> [196,] 59 5 1 2 0 182
#> [197,] 58 5 1 1 1 135
#> [198,] 59 10 0 180 0 160
#> [199,] 61 8 0 77 0 120
#> [200,] 61 13 0 13 0 210
#> [201,] 62 10 1 0 1 153
#> [202,] 57 3 1 0 0 100
#> [203,] 61 18 0 170 0 140
#> [204,] 61 7 0 7 1 150
#> [205,] 61 6 0 6 0 134
#> [206,] 59 13 1 2 0 198
#> [207,] 57 12 1 9 1 120
#> [208,] 62 4 1 0 0 160
#> [209,] 60 17 1 8 1 140
#> [210,] 58 3 1 0 1 146
#> [211,] 62 4 1 3 0 173
#> [212,] 58 2 0 30 0 202
#> [213,] 59 1 0 180 0 155
#> [214,] 63 6 0 28 1 120
#> [215,] 61 13 0 13 0 120
#> [216,] 61 5 0 5 1 110
#> [217,] 61 5 0 5 1 160
#> [218,] 58 11 1 9 0 179
#> [219,] 62 17 1 10 1 180
#> [220,] 62 1 1 0 1 172
#> [221,] 58 7 0 180 1 150
#> [222,] 63 3 1 1 0 180
#> [223,] 63 4 1 3 0 222
#> [224,] 62 3 0 180 1 105
#> [225,] 63 4 1 1 0 155
#> [226,] 59 8 0 180 1 140
#> [227,] 62 7 0 7 0 150
#> [228,] 59 1 0 22 1 162
#> [229,] 60 7 0 7 0 140
#> [230,] 59 5 1 1 0 148
#> [231,] 60 7 1 1 1 90
#> [232,] 65 13 0 180 1 100
#> [233,] 63 1 0 1 0 162
#> [234,] 62 6 0 180 0 170
#> [235,] 61 15 1 13 0 170
#> [236,] 60 3 0 3 0 168
#> [237,] 62 6 0 6 0 120
#> [238,] 63 12 1 10 0 200
#> [239,] 61 6 1 1 1 117
#> [240,] 64 12 1 11 0 160
#> [241,] 66 1 1 0 1 120
#> [242,] 64 6 1 0 1 140
#> [243,] 63 10 1 0 1 148
#> [244,] 63 14 1 9 0 123
#> [245,] 65 36 1 11 0 140
#> [246,] 63 4 1 3 0 162
#> [247,] 63 12 1 9 0 114
#> [248,] 63 7 0 180 0 120
#> [249,] 66 5 1 0 1 110
#> [250,] 64 0 0 0 1 90
#> [251,] 64 21 1 10 0 190
#> [252,] 61 12 1 11 0 154
#> [253,] 64 9 0 180 0 150
#> [254,] 61 4 0 180 1 113
#> [255,] 63 12 0 12 1 150
#> [256,] 62 3 1 1 1 199
#> [257,] 65 3 1 0 1 80
#> [258,] 63 5 1 4 0 170
#> [259,] 63 2 1 1 0 180
#> [260,] 62 13 1 11 0 180
#> [261,] 64 2 0 2 0 201
#> [262,] 66 18 1 5 0 142
#> [263,] 62 9 0 180 0 145
#> [264,] 61 14 1 5 0 140
#> [265,] 61 15 1 10 0 130
#> [266,] 63 9 1 8 1 160
#> [267,] 63 3 1 2 0 120
#> [268,] 63 2 1 0 0 140
#> [269,] 65 8 1 0 1 140
#> [270,] 67 6 0 180 1 170
#> [271,] 65 15 1 11 1 160
#> [272,] 68 5 1 4 1 150
#> [273,] 64 13 1 12 1 150
#> [274,] 64 6 1 0 1 125
#> [275,] 65 3 0 3 0 105
#> [276,] 64 0 0 0 1 148
#> [277,] 66 3 1 0 1 135
#> [278,] 65 2 1 1 1 170
#> [279,] 68 1 0 180 1 166
#> [280,] 64 10 1 9 1 110
#> [281,] 63 7 1 0 0 162
#> [282,] 67 8 1 1 1 130
#> [283,] 68 5 0 5 1 90
#> [284,] 63 10 0 16 1 160
#> [285,] 66 14 0 180 0 130
#> [286,] 68 18 0 180 1 260
#> [287,] 63 8 1 1 1 162
#> [288,] 63 1 1 0 1 155
#> [289,] 63 10 0 18 1 130
#> [290,] 67 11 0 11 0 150
#> [291,] 68 14 0 79 0 172
#> [292,] 66 11 1 0 0 100
#> [293,] 65 4 1 2 1 145
#> [294,] 66 15 1 13 1 160
#> [295,] 63 2 0 180 0 150
#> [296,] 69 21 1 10 0 180
#> [297,] 63 8 0 180 1 120
#> [298,] 68 14 1 13 1 140
#> [299,] 65 8 1 0 1 90
#> [300,] 66 3 0 3 1 138
#> [301,] 69 1 1 0 0 170
#> [302,] 67 1 0 180 1 160
#> [303,] 68 10 1 10 1 150
#> [304,] 65 1 1 0 0 133
#> [305,] 67 7 1 4 1 130
#> [306,] 63 2 1 0 0 99
#> [307,] 67 2 0 180 0 184
#> [308,] 65 6 0 6 0 80
#> [309,] 67 12 1 12 0 160
#> [310,] 64 4 0 179 0 160
#> [311,] 66 4 0 180 1 130
#> [312,] 64 4 0 180 1 140
#> [313,] 67 2 0 18 0 131
#> [314,] 66 4 0 180 0 177
#> [315,] 68 4 1 0 1 160
#> [316,] 69 4 1 3 1 150
#> [317,] 65 13 1 12 1 130
#> [318,] 69 17 1 10 0 140
#> [319,] 69 8 0 93 0 140
#> [320,] 65 1 0 1 1 120
#> [321,] 65 6 0 101 1 115
#> [322,] 71 3 0 5 0 112
#> [323,] 71 20 1 0 1 160
#> [324,] 67 2 0 180 0 128
#> [325,] 66 9 1 3 1 151
#> [326,] 66 1 1 1 1 165
#> [327,] 70 4 1 0 1 180
#> [328,] 70 14 0 171 0 166
#> [329,] 66 4 0 180 0 130
#> [330,] 67 6 1 4 0 130
#> [331,] 69 0 0 0 1 148
#> [332,] 65 2 0 180 0 130
#> [333,] 68 7 1 0 1 150
#> [334,] 69 3 1 2 0 151
#> [335,] 67 14 1 13 0 130
#> [336,] 65 14 1 13 1 150
#> [337,] 71 7 0 7 0 230
#> [338,] 71 6 0 45 1 158
#> [339,] 69 5 0 5 1 142
#> [340,] 71 3 0 103 0 133
#> [341,] 69 3 0 3 1 130
#> [342,] 67 1 0 36 1 104
#> [343,] 67 5 0 5 0 130
#> [344,] 69 6 1 4 1 174
#> [345,] 72 3 1 0 1 132
#> [346,] 72 7 0 7 1 110
#> [347,] 69 8 1 7 1 108
#> [348,] 67 3 0 180 0 110
#> [349,] 69 19 0 180 0 130
#> [350,] 67 14 0 172 1 140
#> [351,] 69 11 1 0 1 120
#> [352,] 66 2 0 180 0 130
#> [353,] 67 7 1 4 0 122
#> [354,] 68 2 0 7 1 130
#> [355,] 69 8 1 2 0 121
#> [356,] 67 13 1 9 0 130
#> [357,] 70 9 0 180 1 142
#> [358,] 72 5 1 4 0 170
#> [359,] 68 3 0 19 0 135
#> [360,] 67 12 1 8 0 120
#> [361,] 67 1 0 1 1 60
#> [362,] 67 4 0 60 1 136
#> [363,] 67 8 1 0 1 130
#> [364,] 72 13 1 11 1 195
#> [365,] 68 10 1 8 1 160
#> [366,] 70 35 1 0 1 105
#> [367,] 68 7 1 2 0 135
#> [368,] 73 20 1 0 1 170
#> [369,] 71 6 0 9 0 120
#> [370,] 70 11 0 180 1 210
#> [371,] 72 12 1 10 0 170
#> [372,] 67 8 0 180 1 170
#> [373,] 67 5 1 0 1 147
#> [374,] 67 9 0 180 0 158
#> [375,] 72 2 0 2 1 100
#> [376,] 67 4 1 1 0 134
#> [377,] 72 6 1 5 0 115
#> [378,] 71 1 0 173 1 188
#> [379,] 69 3 0 180 0 220
#> [380,] 71 3 1 2 0 150
#> [381,] 68 4 1 3 0 210
#> [382,] 72 5 0 28 0 120
#> [383,] 73 6 0 180 1 117
#> [384,] 69 16 1 10 1 140
#> [385,] 69 8 1 1 0 164
#> [386,] 68 7 0 180 1 130
#> [387,] 70 4 0 180 0 180
#> [388,] 71 2 1 0 1 180
#> [389,] 73 7 0 7 1 140
#> [390,] 70 13 1 9 0 100
#> [391,] 72 6 0 180 1 130
#> [392,] 73 0 0 180 1 161
#> [393,] 73 4 0 180 1 154
#> [394,] 69 2 1 0 1 110
#> [395,] 71 15 1 11 0 165
#> [396,] 68 9 0 180 1 120
#> [397,] 71 20 1 10 0 140
#> [398,] 74 0 1 0 1 90
#> [399,] 73 3 1 0 1 136
#> [400,] 71 17 1 11 0 160
#> [401,] 71 8 1 7 0 149
#> [402,] 71 3 1 2 1 190
#> [403,] 73 10 1 8 0 106
#> [404,] 73 4 0 58 1 160
#> [405,] 73 6 0 180 0 110
#> [406,] 72 8 1 0 1 140
#> [407,] 74 3 0 3 1 150
#> [408,] 73 17 1 11 0 140
#> [409,] 71 13 1 8 0 121
#> [410,] 69 2 1 1 1 80
#> [411,] 74 7 1 0 1 117
#> [412,] 72 10 1 8 1 153
#> [413,] 72 15 1 13 0 156
#> [414,] 70 8 0 8 0 120
#> [415,] 71 10 1 9 1 120
#> [416,] 75 2 1 1 0 145
#> [417,] 73 10 1 9 1 146
#> [418,] 72 10 1 9 1 160
#> [419,] 73 10 1 10 1 120
#> [420,] 74 15 1 9 1 179
#> [421,] 75 13 1 1 1 130
#> [422,] 71 4 0 4 0 134
#> [423,] 72 15 1 12 1 120
#> [424,] 72 1 1 1 0 168
#> [425,] 72 7 0 57 1 145
#> [426,] 72 11 0 11 1 140
#> [427,] 73 5 1 3 1 112
#> [428,] 76 25 1 12 1 170
#> [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,] 73 4 0 180 0 124
#> [434,] 72 5 0 180 0 154
#> [435,] 72 3 0 180 0 160
#> [436,] 77 11 0 11 1 150
#> [437,] 77 4 0 4 0 185
#> [438,] 75 3 1 1 0 180
#> [439,] 73 15 0 15 1 160
#> [440,] 71 16 0 180 0 140
#> [441,] 73 10 1 10 0 124
#> [442,] 74 3 0 3 1 128
#> [443,] 76 1 0 180 0 114
#> [444,] 74 2 1 1 0 140
#> [445,] 76 8 1 0 1 141
#> [446,] 73 6 0 6 1 114
#> [447,] 75 23 1 14 1 110
#> [448,] 74 2 0 180 0 190
#> [449,] 72 4 1 3 0 160
#> [450,] 76 17 1 0 1 200
#> [451,] 73 4 1 3 1 125
#> [452,] 76 13 1 10 0 110
#> [453,] 75 4 1 0 1 122
#> [454,] 75 7 0 7 0 190
#> [455,] 75 0 0 0 1 130
#> [456,] 73 13 1 11 0 195
#> [457,] 75 12 0 12 1 160
#> [458,] 74 8 1 0 1 105
#> [459,] 76 13 1 8 1 148
#> [460,] 75 4 1 2 1 188
#> [461,] 74 6 0 180 0 160
#> [462,] 76 4 0 4 1 155
#> [463,] 75 1 0 1 1 125
#> [464,] 74 2 0 180 0 111
#> [465,] 73 0 0 180 0 156
#> [466,] 78 12 1 11 1 160
#> [467,] 76 44 1 10 0 105
#> [468,] 76 5 0 180 0 185
#> [469,] 74 10 1 0 1 135
#> [470,] 76 5 1 0 1 167
#> [471,] 75 9 0 180 1 140
#> [472,] 73 33 1 12 1 175
#> [473,] 77 5 1 0 0 123
#> [474,] 77 12 1 9 1 100
#> [475,] 73 10 1 9 0 146
#> [476,] 77 12 0 180 0 130
#> [477,] 77 1 1 0 1 90
#> [478,] 78 5 1 0 1 170
#> [479,] 73 7 1 0 0 174
#> [480,] 74 6 0 79 1 140
#> [481,] 75 3 1 1 1 171
#> [482,] 74 9 1 8 0 200
#> [483,] 75 6 0 180 0 150
#> [484,] 74 2 1 0 1 130
#> [485,] 77 3 0 180 0 110
#> [486,] 73 11 1 2 1 110
#> [487,] 74 2 0 180 0 100
#> [488,] 78 7 0 7 1 133
#> [489,] 74 15 0 180 1 172
#> [490,] 76 13 1 1 1 170
#> [491,] 80 10 1 6 1 147
#> [492,] 78 13 1 5 0 130
#> [493,] 75 5 0 119 1 150
#> [494,] 78 15 0 180 1 270
#> [495,] 80 8 0 8 1 120
#> [496,] 75 13 1 6 0 150
#> [497,] 74 10 1 8 0 135
#> [498,] 76 1 0 1 1 83
#> [499,] 75 4 1 0 0 212
#> [500,] 78 10 0 180 1 130
#> [501,] 75 11 1 4 0 162
#> [502,] 75 3 0 3 0 0
#> [503,] 76 7 0 29 1 150
#> [504,] 77 24 0 24 1 160
#> [505,] 79 8 0 32 1 120
#> [506,] 80 9 0 23 1 128
#> [507,] 78 6 1 0 1 240
#> [508,] 76 3 1 0 1 140
#> [509,] 78 11 1 1 1 140
#> [510,] 81 1 0 1 0 130
#> [511,] 76 4 0 4 1 160
#> [512,] 76 10 1 8 0 180
#> [513,] 76 12 1 10 1 127
#> [514,] 77 6 0 6 1 107
#> [515,] 78 11 0 180 1 135
#> [516,] 76 1 0 1 1 140
#> [517,] 77 31 1 3 1 161
#> [518,] 78 7 1 0 1 110
#> [519,] 79 3 0 3 0 120
#> [520,] 77 7 0 180 1 170
#> [521,] 77 6 0 6 1 144
#> [522,] 79 4 1 0 1 120
#> [523,] 81 1 0 180 0 120
#> [524,] 80 15 1 12 1 150
#> [525,] 77 9 1 4 0 141
#> [526,] 82 5 0 8 1 120
#> [527,] 78 4 0 59 1 112
#> [528,] 80 17 1 12 0 100
#> [529,] 76 7 0 161 0 151
#> [530,] 79 10 0 10 1 120
#> [531,] 81 4 1 2 1 126
#> [532,] 80 9 0 118 1 186
#> [533,] 80 6 0 173 1 160
#> [534,] 79 1 0 37 1 140
#> [535,] 81 2 0 175 0 172
#> [536,] 78 7 0 7 1 147
#> [537,] 80 5 1 1 1 108
#> [538,] 78 4 0 180 0 175
#> [539,] 78 26 1 5 0 194
#> [540,] 76 1 0 166 0 131
#> [541,] 81 4 1 1 1 104
#> [542,] 78 3 1 1 1 152
#> [543,] 77 10 1 8 1 130
#> [544,] 80 2 1 1 0 168
#> [545,] 79 6 0 6 0 152
#> [546,] 80 6 1 0 1 119
#> [547,] 82 1 1 0 1 82
#> [548,] 77 4 0 180 1 98
#> [549,] 78 12 0 180 0 134
#> [550,] 79 1 0 125 0 193
#> [551,] 82 21 1 2 0 155
#> [552,] 84 22 1 10 0 180
#> [553,] 83 9 1 5 1 170
#> [554,] 79 7 1 6 0 130
#> [555,] 78 23 1 10 1 145
#> [556,] 79 4 0 4 1 183
#> [557,] 78 9 1 4 1 120
#> [558,] 82 8 1 1 0 128
#> [559,] 79 1 0 180 1 170
#> [560,] 81 15 0 180 1 140
#> [561,] 80 7 1 0 1 146
#> [562,] 84 5 1 1 1 85
#> [563,] 81 20 1 9 0 170
#> [564,] 83 8 0 8 0 115
#> [565,] 80 6 1 0 1 150
#> [566,] 80 11 1 8 0 110
#> [567,] 81 8 0 180 0 146
#> [568,] 79 7 0 177 0 197
#> [569,] 82 6 0 128 1 100
#> [570,] 84 4 0 167 0 198
#> [571,] 80 3 1 1 1 230
#> [572,] 82 23 1 0 0 110
#> [573,] 84 5 0 180 1 203
#> [574,] 84 4 0 4 1 85
#> [575,] 81 1 0 1 1 150
#> [576,] 84 1 0 38 1 205
#> [577,] 83 3 0 180 0 174
#> [578,] 81 4 0 90 1 138
#> [579,] 80 13 1 8 1 140
#> [580,] 80 2 1 0 1 130
#> [581,] 79 4 0 4 1 60
#> [582,] 83 1 0 1 1 100
#> [583,] 82 19 0 19 0 120
#> [584,] 83 9 0 180 0 198
#> [585,] 83 3 0 114 0 98
#> [586,] 83 2 0 154 0 130
#> [587,] 81 1 0 1 1 145
#> [588,] 82 5 1 0 1 146
#> [589,] 81 4 0 4 0 160
#> [590,] 86 12 0 180 1 120
#> [591,] 83 12 1 2 1 170
#> [592,] 81 19 1 14 0 120
#> [593,] 82 3 1 2 0 130
#> [594,] 82 15 1 0 0 183
#> [595,] 80 2 0 88 0 135
#> [596,] 81 16 1 9 0 180
#> [597,] 82 9 0 180 1 134
#> [598,] 84 3 0 180 1 120
#> [599,] 85 3 0 3 1 118
#> [600,] 81 2 1 0 1 118
#> [601,] 83 9 0 180 1 149
#> [602,] 82 1 0 180 1 193
#> [603,] 83 4 0 4 0 130
#> [604,] 87 2 0 5 1 137
#> [605,] 86 12 1 0 1 132
#> [606,] 82 14 1 11 1 103
#> [607,] 86 6 1 0 1 140
#> [608,] 84 16 0 70 1 150
#> [609,] 83 10 1 0 1 190
#> [610,] 86 2 0 180 1 169
#> [611,] 88 14 1 3 1 130
#> [612,] 84 3 0 3 1 121
#> [613,] 87 2 0 180 0 113
#> [614,] 84 3 0 180 1 170
#> [615,] 86 4 0 38 1 122
#> [616,] 84 13 0 62 1 100
#> [617,] 86 6 1 1 0 112
#> [618,] 83 20 1 3 1 150
#> [619,] 88 4 0 4 1 115
#> [620,] 85 22 0 22 1 184
#> [621,] 83 9 0 65 1 150
#> [622,] 86 9 1 7 1 142
#> [623,] 87 2 0 180 1 130
#> [624,] 86 6 0 46 0 173
#> [625,] 88 2 0 180 1 68
#> [626,] 83 3 0 3 1 130
#> [627,] 88 4 0 4 0 86
#> [628,] 89 5 0 119 1 140
#> [629,] 87 6 0 180 1 110
#> [630,] 87 1 0 1 0 170
#> [631,] 84 8 0 180 1 119
#> [632,] 85 8 0 8 1 136
#> [633,] 84 2 0 110 1 174
#> [634,] 87 15 1 9 1 138
#> [635,] 84 0 0 180 1 136
#> [636,] 89 10 0 46 1 170
#> [637,] 90 14 0 14 1 100
#> [638,] 87 2 0 180 0 160
#> [639,] 87 6 1 0 0 125
#> [640,] 86 3 1 0 1 80
#> [641,] 88 7 0 24 0 119
#> [642,] 88 8 0 50 1 154
#> [643,] 87 6 0 126 1 168
#> [644,] 91 1 0 1 1 74
#> [645,] 87 43 0 178 1 130
#> [646,] 87 5 0 36 1 150
#> [647,] 90 5 1 0 1 125
#> [648,] 88 3 1 2 0 159
#> [649,] 89 3 1 1 1 160
#> [650,] 89 12 1 0 1 130
#> [651,] 89 2 0 168 0 118
#> [652,] 91 5 0 169 1 176
#> [653,] 89 52 0 52 1 130
#> [654,] 89 4 0 4 1 159
#> [655,] 91 4 1 0 1 120
#> [656,] 94 6 0 50 0 78
#> [657,] 90 1 0 1 1 118
#> [658,] 93 8 0 179 1 110
#> [659,] 94 8 0 8 1 142
#> [660,] 91 1 0 180 0 158
#> [661,] 90 16 0 16 1 106
#> [662,] 96 3 0 12 1 97
#> [663,] 95 8 1 5 1 150
#> [664,] 94 3 0 26 1 144
#> [665,] 91 12 0 53 1 212
#> [666,] 91 7 0 7 0 135
#> [667,] 93 0 1 0 1 122
#> [668,] 92 5 0 69 0 139
#> [669,] 96 3 1 0 1 104
#> [670,] 96 15 1 0 1 140
#>
#> $y
#> [1] 5.0+ 2.0+ 180.0+ 2.0+ 115.0 180.0+ 12.0 5.0+ 180.0+ 180.0+
#> [11] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 5.0+ 2.0+ 180.0+ 180.0+ 180.0+
#> [21] 3.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 2.0+ 180.0+ 180.0+ 5.0+
#> [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+ 5.0+ 180.0+ 180.0+ 180.0+
#> [51] 177.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 10.0+ 172.0+ 180.0+ 180.0+
#> [61] 7.0 180.0+ 180.0+ 1.0 179.0+ 179.0+ 180.0+ 180.0+ 180.0+ 4.0+
#> [71] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [81] 180.0+ 7.0 88.0+ 4.0+ 180.0+ 180.0+ 180.0+ 77.0 180.0+ 5.0
#> [91] 180.0+ 180.0+ 180.0+ 180.0+ 4.0+ 85.0 180.0+ 16.0+ 7.0+ 180.0+
#> [101] 180.0+ 13.0+ 171.0+ 180.0+ 180.0+ 174.0+ 6.0+ 1.0 180.0+ 9.0+
#> [111] 180.0+ 175.0+ 2.0 180.0+ 7.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [121] 180.0+ 180.0+ 180.0+ 16.0 15.0+ 180.0+ 180.0+ 12.0+ 134.0+ 180.0+
#> [131] 8.0 3.0+ 180.0+ 180.0+ 180.0+ 140.0 180.0+ 1.0 180.0+ 180.0+
#> [141] 180.0+ 8.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 171.0+ 166.0+
#> [151] 5.0+ 147.0+ 180.0+ 180.0+ 180.0+ 5.0+ 180.0+ 4.0+ 180.0+ 180.0+
#> [161] 2.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 64.0 180.0+ 180.0+ 180.0+
#> [171] 9.0+ 180.0+ 180.0+ 161.0+ 171.0+ 180.0+ 180.0+ 3.0 180.0+ 180.0+
#> [181] 10.0+ 180.0+ 17.0 3.0+ 19.0 180.0+ 180.0+ 180.0+ 172.0+ 172.0+
#> [191] 24.0 180.0+ 180.0+ 180.0+ 1.0+ 180.0+ 180.0+ 180.0+ 77.0 13.0+
#> [201] 180.0+ 180.0+ 170.0 7.0 6.0 180.0+ 180.0+ 180.0+ 180.0+ 3.0+
#> [211] 180.0+ 30.0 180.0+ 28.0 13.0+ 5.0 5.0+ 180.0+ 180.0+ 1.0
#> [221] 180.0+ 180.0+ 180.0+ 180.0+ 4.0+ 180.0+ 7.0+ 22.0 7.0+ 180.0+
#> [231] 180.0+ 180.0+ 1.0 180.0+ 180.0+ 3.0+ 6.0+ 180.0+ 180.0+ 12.0
#> [241] 180.0+ 180.0+ 180.0+ 14.0+ 36.0 180.0+ 12.0 180.0+ 180.0+ 0.5
#> [251] 180.0+ 12.0+ 180.0+ 180.0+ 12.0 180.0+ 3.0 180.0+ 180.0+ 180.0+
#> [261] 2.0+ 18.0+ 180.0+ 180.0+ 180.0+ 180.0+ 3.0+ 2.0+ 15.0 180.0+
#> [271] 180.0+ 5.0+ 13.0 180.0+ 3.0 0.5+ 3.0+ 175.0+ 180.0+ 180.0+
#> [281] 7.0+ 8.0 5.0 16.0 180.0+ 180.0+ 180.0+ 1.0+ 18.0 11.0+
#> [291] 79.0 180.0+ 4.0+ 180.0+ 180.0+ 174.0+ 180.0+ 180.0+ 8.0+ 3.0
#> [301] 175.0 180.0+ 10.0 180.0+ 180.0+ 180.0+ 180.0+ 6.0 12.0 179.0+
#> [311] 180.0+ 180.0+ 18.0 180.0+ 180.0+ 152.0+ 180.0+ 180.0+ 93.0 1.0
#> [321] 101.0 5.0 180.0+ 180.0+ 180.0+ 1.0 180.0+ 171.0 180.0+ 6.0
#> [331] 0.5 180.0+ 180.0+ 180.0+ 180.0+ 14.0+ 7.0+ 45.0 5.0+ 103.0
#> [341] 3.0+ 36.0 5.0+ 97.0 180.0+ 7.0 8.0+ 180.0+ 180.0+ 172.0+
#> [351] 180.0+ 180.0+ 7.0 7.0 8.0+ 13.0+ 180.0+ 180.0+ 19.0 180.0+
#> [361] 1.0 60.0 180.0+ 132.0 10.0+ 180.0+ 7.0+ 124.0 9.0 180.0+
#> [371] 12.0 180.0+ 180.0+ 180.0+ 2.0 76.0 180.0+ 173.0+ 180.0+ 180.0+
#> [381] 180.0+ 28.0 180.0+ 16.0+ 180.0+ 180.0+ 180.0+ 180.0+ 7.0+ 13.0+
#> [391] 180.0+ 180.0+ 180.0+ 2.0 180.0+ 180.0+ 20.0 0.5 180.0+ 180.0+
#> [401] 8.0 3.0 87.0 58.0 180.0+ 180.0+ 3.0 180.0+ 175.0 2.0
#> [411] 180.0+ 10.0+ 180.0+ 8.0+ 179.0+ 180.0+ 180.0+ 159.0 15.0 180.0+
#> [421] 13.0 4.0+ 180.0+ 1.0 57.0 11.0 5.0 180.0+ 180.0+ 180.0+
#> [431] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 11.0+ 4.0+ 180.0+ 15.0+ 180.0+
#> [441] 10.0 3.0 180.0+ 180.0+ 180.0+ 6.0 180.0+ 180.0+ 180.0+ 17.0+
#> [451] 180.0+ 174.0+ 4.0 7.0 0.5 180.0+ 12.0 180.0+ 180.0+ 46.0
#> [461] 180.0+ 4.0 1.0 180.0+ 180.0+ 12.0 180.0+ 180.0+ 180.0+ 180.0+
#> [471] 180.0+ 33.0 5.0 180.0+ 180.0+ 180.0+ 1.0 180.0+ 7.0+ 79.0
#> [481] 3.0 168.0+ 180.0+ 176.0+ 180.0+ 11.0 180.0+ 7.0 180.0+ 180.0+
#> [491] 10.0 172.0 119.0 180.0+ 8.0 180.0+ 180.0+ 1.0 4.0+ 180.0+
#> [501] 152.0+ 3.0 29.0 24.0 32.0 23.0 180.0+ 3.0+ 180.0+ 1.0
#> [511] 4.0 10.0+ 180.0+ 6.0 180.0+ 1.0 171.0 43.0 3.0 180.0+
#> [521] 6.0 138.0 180.0+ 180.0+ 71.0 8.0 59.0 17.0 161.0 10.0+
#> [531] 93.0 118.0 173.0 37.0 175.0+ 7.0+ 5.0+ 180.0+ 171.0+ 166.0+
#> [541] 71.0 3.0+ 10.0 10.0 6.0+ 6.0 1.0 180.0+ 180.0+ 125.0
#> [551] 180.0+ 180.0+ 9.0+ 180.0+ 70.0 4.0 180.0+ 180.0+ 180.0+ 180.0+
#> [561] 7.0+ 180.0+ 20.0 8.0+ 180.0+ 180.0+ 180.0+ 177.0+ 128.0 167.0
#> [571] 3.0+ 62.0 180.0+ 4.0 1.0 38.0 180.0+ 90.0 180.0+ 180.0+
#> [581] 4.0 1.0 19.0 180.0+ 114.0 154.0 1.0 5.0+ 4.0+ 180.0+
#> [591] 77.0 180.0+ 3.0 83.0 88.0 180.0+ 180.0+ 180.0+ 3.0+ 180.0+
#> [601] 180.0+ 180.0+ 4.0+ 5.0 180.0+ 174.0 6.0 70.0 180.0+ 180.0+
#> [611] 14.0 3.0 180.0+ 180.0+ 38.0 62.0 6.0+ 20.0 4.0 22.0
#> [621] 65.0 11.0 180.0+ 46.0 180.0+ 3.0+ 4.0 119.0 180.0+ 1.0+
#> [631] 180.0+ 8.0 110.0 180.0+ 180.0+ 46.0 14.0 180.0+ 25.0 3.0
#> [641] 24.0 50.0 126.0 1.0 178.0+ 36.0 89.0 75.0 3.0+ 180.0+
#> [651] 168.0 169.0 52.0 4.0 4.0 50.0 1.0+ 179.0+ 8.0+ 180.0+
#> [661] 16.0 12.0 8.0 26.0 53.0 7.0+ 0.5 69.0 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
#> 0.8244272