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Generalized linear models with elastic net regularization. Calls glmnet::cv.glmnet() from package glmnet.

Initial parameter values

  • family is set to "cox" and cannot be changed.

Prediction types

This learner returns three prediction types:

  1. lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated using glmnet::predict.cv.glmnet().

  2. crank: same as lp.

  3. distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated using glmnet::survfit.cv.glmnet(). Parameters stype and ctype relate to how lp predictions are transformed into survival predictions and are described in survival::survfit.coxph(). By default the Breslow estimator is used for computing the baseline hazard.

Dictionary

This Learner can be instantiated via lrn():

lrn("surv.cv_glmnet")

Meta Information

  • Task type: “surv”

  • Predict Types: “crank”, “distr”, “lp”

  • Feature Types: “logical”, “integer”, “numeric”

  • Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet

Parameters

IdTypeDefaultLevelsRange
alignmentcharacterlambdalambda, fraction-
alphanumeric1\([0, 1]\)
bignumeric9.9e+35\((-\infty, \infty)\)
devmaxnumeric0.999\([0, 1]\)
dfmaxinteger-\([0, \infty)\)
epsnumeric1e-06\([0, 1]\)
epsnrnumeric1e-08\([0, 1]\)
excludeuntyped--
exmxnumeric250\((-\infty, \infty)\)
fdevnumeric1e-05\([0, 1]\)
foldiduntypedNULL-
gammauntyped--
groupedlogicalTRUETRUE, FALSE-
interceptlogicalTRUETRUE, FALSE-
keeplogicalFALSETRUE, FALSE-
lambdauntyped--
lambda.min.rationumeric-\([0, 1]\)
lower.limitsuntyped-Inf-
maxitinteger100000\([1, \infty)\)
mnlaminteger5\([1, \infty)\)
mxitinteger100\([1, \infty)\)
mxitnrinteger25\([1, \infty)\)
nfoldsinteger10\([3, \infty)\)
nlambdainteger100\([1, \infty)\)
use_pred_offsetlogicalTRUETRUE, FALSE-
parallellogicalFALSETRUE, FALSE-
penalty.factoruntyped--
pmaxinteger-\([0, \infty)\)
pminnumeric1e-09\([0, 1]\)
precnumeric1e-10\((-\infty, \infty)\)
predict.gammanumericgamma.1se\((-\infty, \infty)\)
relaxlogicalFALSETRUE, FALSE-
snumericlambda.1se\([0, \infty)\)
standardizelogicalTRUETRUE, FALSE-
standardize.responselogicalFALSETRUE, FALSE-
threshnumeric1e-07\([0, \infty)\)
trace.itinteger0\([0, 1]\)
type.gaussiancharacter-covariance, naive-
type.logisticcharacterNewtonNewton, modified.Newton-
type.measurecharacterdeviancedeviance, C-
type.multinomialcharacterungroupedungrouped, grouped-
upper.limitsuntypedInf-
stypeinteger2\([1, 2]\)
ctypeinteger-\([1, 2]\)

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

Author

be-marc

Super classes

mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvCVGlmnet

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


Method selected_features()

Returns the set of selected features as reported by glmnet::predict.glmnet() with type set to "nonzero".

Usage

LearnerSurvCVGlmnet$selected_features(lambda = NULL)

Arguments

lambda

(numeric(1))
Custom lambda, defaults to the active lambda depending on parameter set.

Returns

(character()) of feature names.


Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerSurvCVGlmnet$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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'

# 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") 
#> 
#> Measure: Partial Likelihood Deviance 
#> 
#>      Lambda Index Measure     SE Nonzero
#> min 0.00333    45   2.839 0.1484       6
#> 1se 0.07181    12   2.973 0.1494       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,]  35  10      1          9        0   106
#>   [5,]  35   2      1          1        1   112
#>   [6,]  38  13      1          0        1   161
#>   [7,]  36   1      0        180        1   155
#>   [8,]  35   0      0        180        1   119
#>   [9,]  38  12      1          8        1   120
#>  [10,]  36   5      1          0        1   115
#>  [11,]  33   6      1          1        1   115
#>  [12,]  38  16      1         10        0   160
#>  [13,]  40  12      1          9        0   153
#>  [14,]  42   3      1          1        1   130
#>  [15,]  37   1      1          0        1   146
#>  [16,]  40   2      1          1        1   148
#>  [17,]  42   2      0        180        1   100
#>  [18,]  40   6      0        180        1   138
#>  [19,]  43   3      1          0        1   100
#>  [20,]  41   2      1          1        0   166
#>  [21,]  40   1      1          0        1   145
#>  [22,]  42  15      1         13        1   125
#>  [23,]  40   3      1          1        0   170
#>  [24,]  42  12      1         10        1   170
#>  [25,]  43   2      1          1        1   116
#>  [26,]  42   2      0        180        1   124
#>  [27,]  44   5      1          1        0   170
#>  [28,]  41  10      1          8        0   150
#>  [29,]  44   3      0        180        0   141
#>  [30,]  41  13      1          1        0   140
#>  [31,]  45   6      0        180        1   170
#>  [32,]  41   5      1          4        1   141
#>  [33,]  43   2      0        180        1   140
#>  [34,]  45   2      0        180        1   140
#>  [35,]  46  15      0        180        0   120
#>  [36,]  46   2      1          1        0   126
#>  [37,]  47   4      1          3        0   118
#>  [38,]  48  15      0        180        1   160
#>  [39,]  45   3      0        150        0   130
#>  [40,]  44   3      1          0        1   180
#>  [41,]  46   7      1          2        0   166
#>  [42,]  43  29      0        180        1   180
#>  [43,]  45   4      1          0        0   124
#>  [44,]  43  10      0        180        0   185
#>  [45,]  46  13      1         10        0   100
#>  [46,]  44   0      1          0        1    96
#>  [47,]  47   4      1          3        1   160
#>  [48,]  43   3      1          0        1   124
#>  [49,]  49   5      0         73        1   136
#>  [50,]  45   5      0          5        0   141
#>  [51,]  46   2      1          1        1   122
#>  [52,]  46   6      1          0        1   100
#>  [53,]  44   4      1          0        1   114
#>  [54,]  47   2      0        180        0   108
#>  [55,]  44   9      1          8        1   135
#>  [56,]  45   5      0        180        1   190
#>  [57,]  46   5      1          3        0   130
#>  [58,]  46  15      0        180        1   120
#>  [59,]  45   9      1          0        1   145
#>  [60,]  47   3      1          1        1   120
#>  [61,]  48   3      0        180        0   154
#>  [62,]  47   5      1          3        1   130
#>  [63,]  47   9      1          6        0   170
#>  [64,]  49   4      0        180        0   117
#>  [65,]  47  10      0         10        1   140
#>  [66,]  50   1      1          0        1   129
#>  [67,]  48   2      1          0        0   184
#>  [68,]  47   7      0        180        0   145
#>  [69,]  46   9      1          9        1   122
#>  [70,]  50   7      0        180        1   110
#>  [71,]  49   2      0          2        0   105
#>  [72,]  47   2      0        180        0   150
#>  [73,]  52   2      0        180        1   170
#>  [74,]  51   3      1          2        0   113
#>  [75,]  50   1      1          0        0   150
#>  [76,]  49   7      1          4        1    90
#>  [77,]  52   2      0        180        0   155
#>  [78,]  46   1      1          1        0   145
#>  [79,]  48   7      1          0        1   110
#>  [80,]  48  17      1         10        0   111
#>  [81,]  47   2      1          1        0   110
#>  [82,]  52   4      1          4        0   152
#>  [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,]  54   6      1          3        0   129
#>  [88,]  50   2      0          5        1   106
#>  [89,]  50  10      1          6        0   122
#>  [90,]  50  14      1         13        0   170
#>  [91,]  53   8      1          7        0   160
#>  [92,]  51  25      1          1        0   202
#>  [93,]  49   5      1          2        1   150
#>  [94,]  52  14      1          7        1   200
#>  [95,]  48   6      0        180        0   160
#>  [96,]  48  11      1         10        0   120
#>  [97,]  49  16      0         16        0   125
#>  [98,]  55   3      1          1        0   150
#>  [99,]  54  23      1         10        0   131
#> [100,]  52   7      1          2        0   154
#> [101,]  55   6      1          2        1   114
#> [102,]  54   9      1          1        0   130
#> [103,]  55   4      1          2        0   150
#> [104,]  51  13      1         11        0   145
#> [105,]  50   5      1          4        1   150
#> [106,]  54   4      1          0        1   121
#> [107,]  52   4      0        180        0   183
#> [108,]  50   3      0        174        1   153
#> [109,]  50   7      1          1        0   156
#> [110,]  53   8      1          0        1   130
#> [111,]  56   4      1          1        1   130
#> [112,]  52   5      0        175        1   117
#> [113,]  55   1      0        180        0   127
#> [114,]  55   2      0          2        0   145
#> [115,]  54   1      0        180        0   162
#> [116,]  56   3      0        180        1   193
#> [117,]  55   5      1          4        1   120
#> [118,]  52   8      0        180        0   119
#> [119,]  53  18      1          9        1   150
#> [120,]  54   3      0        180        1   180
#> [121,]  52  16      0         16        0   152
#> [122,]  53  10      1          9        0   172
#> [123,]  52  16      1         14        0   170
#> [124,]  53  15      0         15        1    90
#> [125,]  53   4      0        180        1   150
#> [126,]  55   6      0        180        1   100
#> [127,]  54  12      1          0        1   190
#> [128,]  55   2      0        134        1   140
#> [129,]  56   3      0          8        1   139
#> [130,]  55   1      0          2        0   130
#> [131,]  57   3      0          3        0   120
#> [132,]  54   7      1          2        0   129
#> [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,]  56  14      1         11        0   130
#> [142,]  53   3      1          0        1   200
#> [143,]  57  10      0        180        1   170
#> [144,]  58   8      0          8        1   130
#> [145,]  54   5      0        180        1   108
#> [146,]  55   3      1          1        1   156
#> [147,]  57   0      0          0        1   150
#> [148,]  53  21      1         13        1   130
#> [149,]  57   4      0        180        1   119
#> [150,]  58   6      1          0        1    90
#> [151,]  53  15      1         10        1   130
#> [152,]  55   9      1          2        1   147
#> [153,]  55  13      0        166        1   140
#> [154,]  56   5      0          5        1   150
#> [155,]  54  23      1          8        0   120
#> [156,]  57   4      1          2        1   185
#> [157,]  55   3      1          2        0   140
#> [158,]  55   5      0          5        1   131
#> [159,]  54   7      1          0        1   141
#> [160,]  58   1      1          1        1   200
#> [161,]  55   5      1          0        0   140
#> [162,]  56   7      1          5        1   120
#> [163,]  57   1      0        180        0   148
#> [164,]  60  11      1          9        0   106
#> [165,]  60   5      1          1        0   138
#> [166,]  57   5      0        180        1   130
#> [167,]  55   5      1          0        1   160
#> [168,]  59   6      1          0        1   140
#> [169,]  59   5      0        180        1   155
#> [170,]  59   4      1          0        1   152
#> [171,]  58  26      1          0        1   189
#> [172,]  61   9      0          9        1   160
#> [173,]  60   0      1          0        1    80
#> [174,]  59   2      1          1        0   140
#> [175,]  58   8      0        161        1   140
#> [176,]  58  14      1          6        0   190
#> [177,]  61   3      1          2        1   102
#> [178,]  61  20      1         13        0   130
#> [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,]  57  11      0        180        1   150
#> [184,]  61   3      0         17        0   143
#> [185,]  56  14      0         45        0   130
#> [186,]  58  19      1         13        1   140
#> [187,]  56  13      1          6        1   158
#> [188,]  59   9      1          0        1    80
#> [189,]  55   4      1          3        1   160
#> [190,]  60  12      1          0        1   114
#> [191,]  55   9      1          7        1   135
#> [192,]  56   8      1          8        0   120
#> [193,]  61  13      1         12        1   130
#> [194,]  57   1      0          1        0   126
#> [195,]  57  15      1         13        1   110
#> [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,]  62   7      1          2        1   180
#> [203,]  57   3      1          0        0   100
#> [204,]  58   8      1          3        1   150
#> [205,]  60   7      0          7        0   147
#> [206,]  61   6      0          6        0   134
#> [207,]  59  13      1          2        0   198
#> [208,]  57  12      1          9        1   120
#> [209,]  58   3      1          0        1   146
#> [210,]  62   4      1          3        0   173
#> [211,]  58   2      0         30        0   202
#> [212,]  59   1      0        180        0   155
#> [213,]  61  13      0         13        0   120
#> [214,]  61   5      0          5        1   110
#> [215,]  58  11      1          9        0   179
#> [216,]  57   2      1          1        0   159
#> [217,]  62  17      1         10        1   180
#> [218,]  58   7      0        180        1   150
#> [219,]  63   3      1          1        0   180
#> [220,]  63   4      1          3        0   222
#> [221,]  62   3      0        180        1   105
#> [222,]  63   4      0        180        1   190
#> [223,]  63  15      1         10        1   126
#> [224,]  64   4      0        180        0   130
#> [225,]  63   4      1          1        0   155
#> [226,]  60  18      1         13        0   132
#> [227,]  59   8      0        180        1   140
#> [228,]  61   9      1          9        1   150
#> [229,]  58   9      1          9        0   110
#> [230,]  62   7      0          7        0   150
#> [231,]  59   4      0        180        0   196
#> [232,]  60   7      1          5        1   141
#> [233,]  59   5      1          1        0   148
#> [234,]  60   7      1          1        1    90
#> [235,]  63   1      0          1        0   162
#> [236,]  63   1      0          1        0   130
#> [237,]  62   6      0        180        0   170
#> [238,]  61  15      1         13        0   170
#> [239,]  60   3      0          3        0   168
#> [240,]  64  10      1          9        0   160
#> [241,]  59  10      0        180        1   130
#> [242,]  60   8      0         17        1   130
#> [243,]  61   6      1          1        1   117
#> [244,]  64  12      1         11        0   160
#> [245,]  66   1      1          0        1   120
#> [246,]  64   6      1          0        1   140
#> [247,]  63  10      1          0        1   148
#> [248,]  63  14      1          9        0   123
#> [249,]  65  36      1         11        0   140
#> [250,]  63   4      1          3        0   162
#> [251,]  66   3      1          1        0   127
#> [252,]  64  32      1          9        1   160
#> [253,]  63   7      0        180        0   120
#> [254,]  66   5      1          0        1   110
#> [255,]  64   0      0          0        1    90
#> [256,]  60   6      0        180        0   130
#> [257,]  61  12      1         11        0   154
#> [258,]  61   4      0        180        1   113
#> [259,]  65   3      0        180        1   190
#> [260,]  66   6      1          1        1   130
#> [261,]  62   3      1          1        1   199
#> [262,]  65   6      0          9        0   112
#> [263,]  65   3      1          0        1    80
#> [264,]  63   5      1          4        0   170
#> [265,]  62  13      1         11        0   180
#> [266,]  64   2      0          2        0   201
#> [267,]  66  18      1          5        0   142
#> [268,]  66  16      1         11        1   169
#> [269,]  62   9      0        180        0   145
#> [270,]  61  15      1         10        0   130
#> [271,]  63   9      1          8        1   160
#> [272,]  63   3      1          2        0   120
#> [273,]  63   2      1          0        0   140
#> [274,]  65   8      1          0        1   140
#> [275,]  67   6      0        180        1   170
#> [276,]  65  15      1         11        1   160
#> [277,]  68   5      1          4        1   150
#> [278,]  64  13      1         12        1   150
#> [279,]  64   6      1          0        1   125
#> [280,]  66   7      1          0        1   115
#> [281,]  65   3      0          3        0   105
#> [282,]  67   4      1          3        0   130
#> [283,]  66   3      1          0        1   135
#> [284,]  66   6      1          0        1   140
#> [285,]  65   2      1          1        1   170
#> [286,]  68   1      0        180        1   166
#> [287,]  64  10      1          9        1   110
#> [288,]  67   8      1          1        1   130
#> [289,]  66  14      0        180        0   130
#> [290,]  64   1      0          1        1   120
#> [291,]  68  18      0        180        1   260
#> [292,]  63   8      1          1        1   162
#> [293,]  65  18      1          3        0   120
#> [294,]  63   1      1          0        1   155
#> [295,]  67  11      0         11        0   150
#> [296,]  66  12      1         10        1   150
#> [297,]  65  15      1         12        1   150
#> [298,]  65   4      1          2        1   145
#> [299,]  69  12      0         15        1   140
#> [300,]  66  15      1         13        1   160
#> [301,]  63   2      0        180        0   150
#> [302,]  65  11      1          6        0   130
#> [303,]  66   9      1          8        0   130
#> [304,]  63   8      0        180        1   120
#> [305,]  65   8      1          0        1    90
#> [306,]  69   1      1          0        0   170
#> [307,]  67   1      0        180        1   160
#> [308,]  68  10      1         10        1   150
#> [309,]  67   7      1          4        1   130
#> [310,]  63   2      1          0        0    99
#> [311,]  67   2      0        180        0   184
#> [312,]  65   6      0          6        0    80
#> [313,]  66  19      1         12        1   150
#> [314,]  67  12      1         12        0   160
#> [315,]  69   6      0         99        1   140
#> [316,]  65   4      1          1        0   130
#> [317,]  64   4      0        179        0   160
#> [318,]  70  15      1         12        1   132
#> [319,]  64  11      0         11        0   125
#> [320,]  64   4      0        180        1   140
#> [321,]  64   0      1          0        1   118
#> [322,]  66   7      1          5        1   131
#> [323,]  66   4      0        180        0   177
#> [324,]  68   4      1          0        1   160
#> [325,]  69   4      1          3        1   150
#> [326,]  65  13      1         12        1   130
#> [327,]  69  17      1         10        0   140
#> [328,]  69   8      0         93        0   140
#> [329,]  66   6      0        180        0   140
#> [330,]  65   1      0          1        1   120
#> [331,]  68  18      1          0        1   160
#> [332,]  68   4      0          4        1   190
#> [333,]  70   7      1          0        1   190
#> [334,]  68   7      0        150        0   210
#> [335,]  66   1      1          1        1   165
#> [336,]  70   4      1          0        1   180
#> [337,]  69   8      0        180        1   153
#> [338,]  70  14      0        171        0   166
#> [339,]  66   4      0        180        0   130
#> [340,]  67  10      1          9        0   200
#> [341,]  67   6      1          4        0   130
#> [342,]  68  18      1         14        1   170
#> [343,]  69   0      0          0        1   148
#> [344,]  65   2      0        180        0   130
#> [345,]  68   7      1          0        1   150
#> [346,]  67  14      1         13        0   130
#> [347,]  65  14      1         13        1   150
#> [348,]  66   2      0          2        1   228
#> [349,]  71   6      0         45        1   158
#> [350,]  69   3      0          3        1   130
#> [351,]  67   5      0          5        0   130
#> [352,]  68   6      0        180        0   145
#> [353,]  67   3      0        180        0   110
#> [354,]  66   2      1          1        0   123
#> [355,]  69  19      0        180        0   130
#> [356,]  68  18      0         18        1   100
#> [357,]  69  11      1          0        1   120
#> [358,]  69   4      1          3        0   132
#> [359,]  69   8      1          2        0   121
#> [360,]  67  13      1          9        0   130
#> [361,]  70   3      0        123        0   130
#> [362,]  68   3      0         19        0   135
#> [363,]  67  12      1          8        0   120
#> [364,]  69   1      0          1        1   110
#> [365,]  67   1      0          1        1    60
#> [366,]  67   4      0         60        1   136
#> [367,]  72  13      1         11        1   195
#> [368,]  68  10      1          8        1   160
#> [369,]  66  24      1         13        0   130
#> [370,]  72  30      1          0        1   145
#> [371,]  70   7      0          7        0   102
#> [372,]  71   6      0          9        0   120
#> [373,]  70  11      0        180        1   210
#> [374,]  72  19      1          8        0   120
#> [375,]  72  12      1         10        0   170
#> [376,]  67   8      0        180        1   170
#> [377,]  67   5      1          0        1   147
#> [378,]  73  13      0        152        1   130
#> [379,]  72   2      0          2        1   100
#> [380,]  72   6      1          5        0   115
#> [381,]  71   1      0        173        1   188
#> [382,]  70   3      0        180        0   121
#> [383,]  68   4      1          3        0   210
#> [384,]  72   5      0         28        0   120
#> [385,]  69  16      1         10        1   140
#> [386,]  68   7      0        180        1   130
#> [387,]  72  16      1          1        1   130
#> [388,]  69   1      1          0        0   155
#> [389,]  73   6      1          0        1   270
#> [390,]  72   8      1          1        1   150
#> [391,]  71   2      1          0        1   180
#> [392,]  70   3      0          3        1   159
#> [393,]  72   6      0        180        1   130
#> [394,]  73   0      0        180        1   161
#> [395,]  74   8      1          0        1    85
#> [396,]  73   4      0        180        1   154
#> [397,]  69   2      1          0        1   110
#> [398,]  71  15      1         11        0   165
#> [399,]  74  20      0         20        1   180
#> [400,]  68   9      0        180        1   120
#> [401,]  71  20      1         10        0   140
#> [402,]  74   0      1          0        1    90
#> [403,]  71  17      1         11        0   160
#> [404,]  71   8      1          7        0   149
#> [405,]  71   3      1          2        1   190
#> [406,]  69  12      1          1        1   149
#> [407,]  70  26      1         11        1   120
#> [408,]  72   5      1          3        1   160
#> [409,]  73   6      0        180        0   110
#> [410,]  72  15      1          0        1   150
#> [411,]  74   3      0          3        1   150
#> [412,]  73  17      1         11        0   140
#> [413,]  71  13      1          8        0   121
#> [414,]  71  14      1         13        1   170
#> [415,]  74   7      1          0        1   117
#> [416,]  72  10      1          8        1   153
#> [417,]  72  15      1         13        0   156
#> [418,]  70   8      0          8        0   120
#> [419,]  71  10      1          9        1   120
#> [420,]  75   1      0          1        0   133
#> [421,]  75   2      1          1        0   145
#> [422,]  72  10      1          9        1   160
#> [423,]  74  15      1          9        1   179
#> [424,]  75  13      1          1        1   130
#> [425,]  71  11      1          8        0   110
#> [426,]  73  10      1          8        0   120
#> [427,]  70   7      1          4        0   184
#> [428,]  72   1      1          1        0   168
#> [429,]  72   7      0         57        1   145
#> [430,]  70   3      0          3        0   150
#> [431,]  73   5      1          3        1   112
#> [432,]  73  12      1         12        1   140
#> [433,]  75   1      0        180        1   140
#> [434,]  72   4      1          0        1   197
#> [435,]  71   3      1          0        0   144
#> [436,]  73   5      0        180        0   126
#> [437,]  73   4      0        180        0   124
#> [438,]  74  34      1          8        1   233
#> [439,]  76   3      1          0        1   120
#> [440,]  72   5      0        180        0   154
#> [441,]  72   3      0        180        0   160
#> [442,]  76   5      0          5        1   130
#> [443,]  71  16      0        180        0   140
#> [444,]  73  10      1         10        0   124
#> [445,]  74   7      0        180        1   150
#> [446,]  74   3      0          3        1   128
#> [447,]  76   1      0        180        0   114
#> [448,]  76   8      1          0        1   141
#> [449,]  74  19      1          4        1   200
#> [450,]  73   6      0          6        1   114
#> [451,]  75  23      1         14        1   110
#> [452,]  76  17      1          0        1   200
#> [453,]  73   4      1          3        1   125
#> [454,]  76  13      1         10        0   110
#> [455,]  75   4      1          0        1   122
#> [456,]  75   7      0          7        0   190
#> [457,]  75   0      0          0        1   130
#> [458,]  73  13      1         11        0   195
#> [459,]  74   8      1          0        1   105
#> [460,]  76  13      1          8        1   148
#> [461,]  75   4      1          2        1   188
#> [462,]  76   4      0          4        1   155
#> [463,]  75   1      0          1        1   125
#> [464,]  74   2      0        180        0   111
#> [465,]  73   1      0         52        1   105
#> [466,]  73   0      0        180        0   156
#> [467,]  72   5      0        180        0   120
#> [468,]  78  12      1         11        1   160
#> [469,]  76   5      0        180        0   185
#> [470,]  74  10      1          0        1   135
#> [471,]  76   5      1          0        1   167
#> [472,]  74   8      1          8        1   170
#> [473,]  77   5      1          0        0   123
#> [474,]  73  10      1          9        0   146
#> [475,]  77  12      0        180        0   130
#> [476,]  77   1      1          0        1    90
#> [477,]  76  12      1         11        1   120
#> [478,]  78   5      1          0        1   170
#> [479,]  75   3      1          1        1   171
#> [480,]  75   6      0        180        0   150
#> [481,]  79  10      1          8        0   190
#> [482,]  74   2      1          0        1   130
#> [483,]  78  18      0         18        1   144
#> [484,]  77   3      0        180        0   110
#> [485,]  76  29      0         47        0    90
#> [486,]  73  11      1          2        1   110
#> [487,]  78   8      1          6        1   110
#> [488,]  76  13      1          1        1   170
#> [489,]  78  32      1          9        1   198
#> [490,]  79   6      0        180        0   170
#> [491,]  80  10      1          6        1   147
#> [492,]  78   0      0        180        1   212
#> [493,]  78  13      1          5        0   130
#> [494,]  75   5      0        119        1   150
#> [495,]  75  12      1          1        1   120
#> [496,]  78  15      0        180        1   270
#> [497,]  75  13      1          6        0   150
#> [498,]  74  10      1          8        0   135
#> [499,]  76   1      0          1        1    83
#> [500,]  78  12      1          9        0   150
#> [501,]  78   2      1          1        0   130
#> [502,]  77   2      1          0        1   143
#> [503,]  78  10      0        180        1   130
#> [504,]  75  11      1          4        0   162
#> [505,]  76   7      0         29        1   150
#> [506,]  77  24      0         24        1   160
#> [507,]  79   8      0         32        1   120
#> [508,]  80   6      0          6        1   150
#> [509,]  78   6      1          0        1   240
#> [510,]  76   3      1          0        1   140
#> [511,]  79  11      0        180        0   160
#> [512,]  78  14      1          0        1   140
#> [513,]  78  11      1          8        1   118
#> [514,]  79   4      0          4        1   125
#> [515,]  76  10      1          8        0   180
#> [516,]  77   6      0          6        1   107
#> [517,]  80   3      1          0        1   120
#> [518,]  78  11      0        180        1   135
#> [519,]  76   1      0          1        1   140
#> [520,]  78   7      1          0        1   110
#> [521,]  79   4      1          0        1   120
#> [522,]  81   1      0        180        0   120
#> [523,]  80  15      1         12        1   150
#> [524,]  77   9      1          4        0   141
#> [525,]  82   5      0          8        1   120
#> [526,]  80  40      1          0        1   138
#> [527,]  78   4      0         59        1   112
#> [528,]  76   7      0        161        0   151
#> [529,]  79  10      0         10        1   120
#> [530,]  80  15      1          0        1    90
#> [531,]  81   4      1          2        1   126
#> [532,]  79  28      0        164        0   100
#> [533,]  80   9      0        118        1   186
#> [534,]  80   6      0        173        1   160
#> [535,]  78  32      0        180        1   130
#> [536,]  81   3      0        180        0   184
#> [537,]  78   7      0          7        1   147
#> [538,]  77  13      1          0        1   190
#> [539,]  78  15      0         15        0   165
#> [540,]  78   4      0        180        0   175
#> [541,]  78  26      1          5        0   194
#> [542,]  76   1      0        166        0   131
#> [543,]  81   4      1          1        1   104
#> [544,]  80   1      0          1        0   100
#> [545,]  78   3      1          1        1   152
#> [546,]  77  10      1          8        1   130
#> [547,]  77   5      0         85        0   188
#> [548,]  79   6      0          6        0   152
#> [549,]  80   6      1          0        1   119
#> [550,]  78   2      0        180        0   148
#> [551,]  77   4      0        180        1    98
#> [552,]  81   1      0        108        0   129
#> [553,]  78  12      0        180        0   134
#> [554,]  84  22      1         10        0   180
#> [555,]  79   4      0          4        1   121
#> [556,]  80   6      0          6        1   110
#> [557,]  83   4      0        103        0    97
#> [558,]  81  11      1          8        0   160
#> [559,]  81   5      0        177        0    41
#> [560,]  80  11      1          8        0   170
#> [561,]  78  23      1         10        1   145
#> [562,]  78   9      1          4        1   120
#> [563,]  82   8      1          1        0   128
#> [564,]  79   1      0        180        1   170
#> [565,]  81  15      0        180        1   140
#> [566,]  80   7      1          0        1   146
#> [567,]  84   5      1          1        1    85
#> [568,]  81  20      1          9        0   170
#> [569,]  83   8      0          8        0   115
#> [570,]  81  16      0         16        1   110
#> [571,]  80   6      1          0        1   150
#> [572,]  80  11      1          8        0   110
#> [573,]  80   8      1          7        0   160
#> [574,]  79   0      1          0        1    96
#> [575,]  85   4      0        180        0    90
#> [576,]  81   2      1          1        0   198
#> [577,]  83   2      0          2        1   155
#> [578,]  82   6      0        128        1   100
#> [579,]  84   4      0        167        0   198
#> [580,]  82  23      1          0        0   110
#> [581,]  81   1      0          1        1   150
#> [582,]  84   1      0         38        1   205
#> [583,]  83   3      0        180        0   174
#> [584,]  81   4      0         90        1   138
#> [585,]  79   9      1          8        0   150
#> [586,]  85   3      1          2        1   160
#> [587,]  84   4      0         89        1   129
#> [588,]  80   2      1          0        1   130
#> [589,]  80  30      1         13        0   220
#> [590,]  83   3      0        114        0    98
#> [591,]  85   9      1          6        1   160
#> [592,]  81   4      0          4        0   175
#> [593,]  84  15      1         13        1   110
#> [594,]  82  16      1          8        0   103
#> [595,]  81   4      0          4        0   160
#> [596,]  86  12      0        180        1   120
#> [597,]  83  12      1          2        1   170
#> [598,]  82   3      1          2        0   130
#> [599,]  82  15      1          0        0   183
#> [600,]  80   2      0         88        0   135
#> [601,]  83   7      0        126        0   135
#> [602,]  86   8      0          8        1   132
#> [603,]  81  16      1          9        0   180
#> [604,]  84   6      0        165        0   145
#> [605,]  81  13      0        180        0   152
#> [606,]  81   2      1          0        1   118
#> [607,]  81   4      0        180        0   160
#> [608,]  82   1      0        180        1   193
#> [609,]  82  14      1         11        1   103
#> [610,]  86   6      1          0        1   140
#> [611,]  83  10      1          0        1   190
#> [612,]  88  14      1          3        1   130
#> [613,]  84   3      0          3        1   121
#> [614,]  83  13      1         12        0   170
#> [615,]  84   7      1          2        0   148
#> [616,]  87   2      0        180        0   113
#> [617,]  82   4      0          4        0   130
#> [618,]  86  13      0        177        0   163
#> [619,]  86   6      0          6        1   117
#> [620,]  86   6      1          1        0   112
#> [621,]  88   4      0          4        0   100
#> [622,]  83  20      1          3        1   150
#> [623,]  85  22      0         22        1   184
#> [624,]  86   9      1          7        1   142
#> [625,]  86   6      0         46        0   173
#> [626,]  88   3      0        115        0   110
#> [627,]  83   3      0          3        1   130
#> [628,]  87   8      0          8        1   157
#> [629,]  86  15      1          8        1   109
#> [630,]  89   4      0          4        1   153
#> [631,]  89   5      0        119        1   140
#> [632,]  87   6      0        180        1   110
#> [633,]  87   1      0          1        0   170
#> [634,]  84   2      0        110        1   174
#> [635,]  87  29      0         29        1    97
#> [636,]  89  10      0         46        1   170
#> [637,]  90  14      0         14        1   100
#> [638,]  86   4      0        180        1   145
#> [639,]  91   8      0          8        0   100
#> [640,]  87   2      0        180        0   160
#> [641,]  91  10      0        145        0   135
#> [642,]  86   3      1          0        1    80
#> [643,]  88   7      0         24        0   119
#> [644,]  88   8      0         50        1   154
#> [645,]  90  11      1         10        1   186
#> [646,]  86   9      1          7        0   130
#> [647,]  90   4      1          0        0   121
#> [648,]  91   1      0          1        1    74
#> [649,]  87  43      0        178        1   130
#> [650,]  88   3      1          2        0   159
#> [651,]  92   1      0          1        1   167
#> [652,]  88   5      0        158        0   100
#> [653,]  89  12      1          0        1   130
#> [654,]  89   2      0        168        0   118
#> [655,]  89   4      0          4        1   159
#> [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,]  96   3      0         12        1    97
#> [664,]  95   8      1          5        1   150
#> [665,]  94   3      0         26        1   144
#> [666,]  93   0      1          0        1   122
#> [667,]  92   5      0         69        0   139
#> [668,]  92   2      0          2        0   112
#> [669,]  93   4      0        180        1   135
#> [670,]  96   3      1          0        1   104
#> 
#> $y
#>   [1] 180.0+   5.0+   5.0+ 180.0+   2.0+ 180.0+ 180.0+ 180.0+  12.0    5.0+
#>  [11] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0+ 180.0+ 180.0+   3.0  180.0+
#>  [21] 180.0+ 180.0+ 180.0+ 180.0+   2.0+ 180.0+ 155.0+ 180.0+ 180.0+ 180.0+
#>  [31] 180.0+   5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 150.0  180.0+
#>  [41] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+  73.0    5.0+
#>  [51] 161.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   5.0+ 180.0+ 177.0+ 180.0+
#>  [61] 180.0+ 180.0+ 180.0+ 180.0+  10.0+ 172.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [71]   2.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   7.0   88.0+
#>  [81] 180.0+   4.0+ 180.0+ 180.0+  77.0  180.0+ 180.0+   5.0  180.0+ 180.0+
#>  [91] 180.0+ 180.0+ 180.0+  85.0  180.0+ 180.0+  16.0+ 180.0+ 152.0+   7.0+
#> [101]   6.0+ 180.0+ 180.0+  13.0+ 171.0+ 180.0+ 180.0+ 174.0+ 180.0+ 180.0+
#> [111] 180.0+ 175.0+ 180.0+   2.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [121]  16.0+ 180.0+  16.0   15.0+ 180.0+ 180.0+  12.0+ 134.0+   8.0    2.0 
#> [131]   3.0+ 180.0+ 180.0+ 180.0+ 180.0+ 140.0  180.0+   1.0  180.0+ 180.0+
#> [141] 180.0+ 180.0+ 180.0+   8.0+ 180.0+ 180.0+   0.5  180.0+ 180.0+ 180.0+
#> [151] 180.0+  15.0  166.0+   5.0+ 180.0+   4.0+ 180.0+   5.0+ 180.0+   1.0 
#> [161] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+  64.0  180.0+ 180.0+
#> [171] 180.0+   9.0+   0.5  180.0+ 161.0+ 171.0+   3.0  180.0+ 180.0+ 180.0+
#> [181]  10.0+ 180.0+ 180.0+  17.0   45.0   19.0  180.0+   9.0+ 180.0+ 172.0+
#> [191]  24.0    8.0  180.0+   1.0+  15.0  180.0+ 180.0+ 180.0+  77.0   13.0+
#> [201] 180.0+ 180.0+ 180.0+ 180.0+   7.0+   6.0  180.0+ 180.0+   3.0+ 180.0+
#> [211]  30.0  180.0+  13.0+   5.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [221] 180.0+ 180.0+ 180.0+ 180.0+   4.0+ 180.0+ 180.0+ 180.0+   9.0    7.0+
#> [231] 180.0+  84.0  180.0+ 180.0+   1.0    1.0  180.0+ 180.0+   3.0+ 167.0 
#> [241] 180.0+  17.0  180.0+  12.0  180.0+ 180.0+ 180.0+  14.0+  36.0  180.0+
#> [251]   3.0+ 180.0+ 180.0+ 180.0+   0.5  180.0+  12.0+ 180.0+ 180.0+ 180.0+
#> [261] 180.0+   9.0    3.0  180.0+ 180.0+   2.0+  18.0+ 180.0+ 180.0+ 180.0+
#> [271] 180.0+   3.0+   2.0+  15.0  180.0+ 180.0+   5.0+  13.0  180.0+ 179.0+
#> [281]   3.0  180.0+   3.0+ 180.0+ 175.0+ 180.0+ 180.0+   8.0  180.0+   1.0 
#> [291] 180.0+ 180.0+ 123.0+   1.0+  11.0+  80.0   15.0+   4.0+  15.0  180.0+
#> [301] 180.0+ 180.0+ 180.0+ 180.0+   8.0+ 175.0  180.0+  10.0  180.0+ 180.0+
#> [311] 180.0+   6.0   19.0+  12.0   99.0  180.0+ 179.0+ 180.0+  11.0+ 180.0+
#> [321]   0.5    7.0+ 180.0+ 180.0+ 152.0+ 180.0+ 180.0+  93.0  180.0+   1.0 
#> [331]  18.0+   4.0    7.0+ 150.0    1.0  180.0+ 180.0+ 171.0  180.0+ 174.0+
#> [341]   6.0  180.0+   0.5  180.0+ 180.0+ 180.0+  14.0+   2.0   45.0    3.0+
#> [351]   5.0+ 180.0+ 180.0+   2.0+ 180.0+  18.0  180.0+ 180.0+   8.0+  13.0+
#> [361] 123.0   19.0  180.0+   1.0    1.0   60.0  132.0   10.0+ 180.0+ 162.0 
#> [371]   7.0+   9.0  180.0+ 180.0+  12.0  180.0+ 180.0+ 152.0    2.0  180.0+
#> [381] 173.0+ 180.0+ 180.0+  28.0   16.0+ 180.0+  16.0+ 180.0+   6.0  180.0+
#> [391] 180.0+   3.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0  180.0+  20.0  180.0+
#> [401]  20.0    0.5  180.0+   8.0    3.0   12.0  180.0+ 180.0+ 180.0+ 180.0+
#> [411]   3.0  180.0+ 175.0   14.0+ 180.0+  10.0+ 180.0+   8.0+ 179.0+   1.0 
#> [421] 180.0+ 159.0  180.0+  13.0  180.0+  10.0  104.0+   1.0   57.0    3.0+
#> [431]   5.0   12.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+  34.0  180.0+ 180.0+
#> [441] 180.0+   5.0  180.0+  10.0  180.0+   3.0  180.0+ 180.0+ 180.0+   6.0 
#> [451] 180.0+  17.0+ 180.0+ 174.0+   4.0    7.0    0.5  180.0+ 180.0+ 180.0+
#> [461]  46.0    4.0    1.0  180.0+  52.0  180.0+ 180.0+  12.0  180.0+ 180.0+
#> [471] 180.0+   8.0    5.0  180.0+ 180.0+   1.0   12.0  180.0+   3.0  180.0+
#> [481] 180.0+ 176.0+  18.0  180.0+  47.0   11.0    8.0+ 180.0+  32.0  180.0+
#> [491]  10.0  180.0+ 172.0  119.0   12.0  180.0+ 180.0+ 180.0+   1.0  180.0+
#> [501] 180.0+   2.0  180.0+ 152.0+  29.0   24.0   32.0    6.0  180.0+   3.0+
#> [511] 180.0+ 180.0+  11.0    4.0   10.0+   6.0    3.0+ 180.0+   1.0   43.0 
#> [521] 138.0  180.0+ 180.0+  71.0    8.0   40.0   59.0  161.0   10.0+ 180.0+
#> [531]  93.0  164.0  118.0  173.0  180.0+ 180.0+   7.0+  22.0   15.0+ 180.0+
#> [541] 171.0+ 166.0+  71.0    1.0    3.0+  10.0   85.0    6.0+   6.0  180.0+
#> [551] 180.0+ 108.0  180.0+ 180.0+   4.0    6.0  103.0  180.0+ 177.0+ 169.0 
#> [561]  70.0  180.0+ 180.0+ 180.0+ 180.0+   7.0+ 180.0+  20.0    8.0+  16.0 
#> [571] 180.0+ 180.0+ 180.0+   0.5  180.0+ 180.0+   2.0  128.0  167.0   62.0 
#> [581]   1.0   38.0  180.0+  90.0  180.0+ 180.0+  89.0  180.0+  30.0  114.0 
#> [591] 180.0+   4.0+ 180.0+  16.0+   4.0+ 180.0+  77.0    3.0   83.0   88.0 
#> [601] 126.0    8.0  180.0+ 165.0  180.0+ 180.0+ 180.0+ 180.0+ 174.0    6.0 
#> [611] 180.0+  14.0    3.0   13.0  180.0+ 180.0+   4.0  177.0    6.0+   6.0+
#> [621]   4.0+  20.0   22.0   11.0   46.0  115.0    3.0+   8.0+ 180.0+   4.0 
#> [631] 119.0  180.0+   1.0+ 110.0   29.0   46.0   14.0  180.0+   8.0  180.0+
#> [641] 145.0    3.0   24.0   50.0   11.0  180.0+   4.0    1.0  178.0+  75.0 
#> [651]   1.0  158.0  180.0+ 168.0    4.0  180.0+ 180.0+  50.0    1.0+   2.0 
#> [661] 179.0+   8.0+  12.0    8.0   26.0    0.5   69.0    2.0  180.0+   3.0 
#> 
#> $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 
#>   0.8534318