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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 = mlr3::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 = mlr3::tsk("grace")

# Create train and test set
ids = mlr3::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 0.00327    44   2.722 0.07738       6
#> 1se 0.04849    15   2.794 0.07655       4
#> 
#> $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   2      0        180        0   121
#>   [5,]  35   2      1          1        1   112
#>   [6,]  37   9      0        180        1   151
#>   [7,]  38   2      0        115        0   150
#>   [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,]  42   3      1          1        1   130
#>  [14,]  37   1      1          0        1   146
#>  [15,]  40   2      1          1        1   148
#>  [16,]  42   2      0        180        1   100
#>  [17,]  38   5      1          3        0   125
#>  [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,]  43   4      1          0        1   130
#>  [23,]  42   4      0        180        0   162
#>  [24,]  40   3      1          1        0   170
#>  [25,]  42  12      1         10        1   170
#>  [26,]  43   2      1          1        1   116
#>  [27,]  42   2      0        180        1   124
#>  [28,]  44   5      1          1        0   170
#>  [29,]  45   3      0        180        1   154
#>  [30,]  45   9      1          7        0   110
#>  [31,]  41   5      1          4        1   141
#>  [32,]  43   2      0        180        1   140
#>  [33,]  45   2      0        180        1   140
#>  [34,]  46  15      0        180        0   120
#>  [35,]  46   2      1          1        0   126
#>  [36,]  47   4      1          3        0   118
#>  [37,]  48  15      0        180        1   160
#>  [38,]  44   3      1          0        1   180
#>  [39,]  46   7      1          2        0   166
#>  [40,]  43  29      0        180        1   180
#>  [41,]  46  13      1         10        0   100
#>  [42,]  47   4      1          3        1   160
#>  [43,]  43   3      1          0        1   124
#>  [44,]  45   8      1          0        1   117
#>  [45,]  49   5      0         73        1   136
#>  [46,]  45   5      0          5        0   141
#>  [47,]  46   6      1          0        1   100
#>  [48,]  44   4      1          0        1   114
#>  [49,]  47   2      0        180        0   108
#>  [50,]  44   9      1          8        1   135
#>  [51,]  45   5      0        180        1   190
#>  [52,]  46   5      1          3        0   130
#>  [53,]  46   4      0        180        1   121
#>  [54,]  44   2      0        180        0   142
#>  [55,]  46  15      0        180        1   120
#>  [56,]  45   9      1          0        1   145
#>  [57,]  47   3      1          1        1   120
#>  [58,]  48  12      1         11        0   200
#>  [59,]  47   5      1          3        1   130
#>  [60,]  46   3      1          0        1   119
#>  [61,]  47  10      0         10        1   140
#>  [62,]  47   7      0        180        0   145
#>  [63,]  50   4      1          1        0   125
#>  [64,]  49   7      1          7        1   110
#>  [65,]  50   7      0        180        1   110
#>  [66,]  49   2      0          2        0   105
#>  [67,]  51   1      0          1        1   145
#>  [68,]  49  15      1         11        1   160
#>  [69,]  49  23      0        179        1   112
#>  [70,]  52   2      0        180        1   170
#>  [71,]  50   7      1          0        1    92
#>  [72,]  50   4      0          4        1   100
#>  [73,]  51   3      1          2        0   113
#>  [74,]  50   1      1          0        0   150
#>  [75,]  50   9      0        180        0   130
#>  [76,]  49   7      1          4        1    90
#>  [77,]  51   8      0        180        1   140
#>  [78,]  46   3      0        180        1   120
#>  [79,]  50   4      1          1        0   150
#>  [80,]  48  17      1         10        0   111
#>  [81,]  47   2      1          1        0   110
#>  [82,]  52   4      1          4        0   152
#>  [83,]  49   9      1          3        0   102
#>  [84,]  53   5      0        180        1   140
#>  [85,]  54  17      1         12        1   102
#>  [86,]  53   5      0         77        0   159
#>  [87,]  53   7      1          0        0   199
#>  [88,]  51   3      1          1        0   140
#>  [89,]  50  14      1         13        0   170
#>  [90,]  53   8      1          7        0   160
#>  [91,]  48   3      1          2        0   150
#>  [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,]  53   4      1          0        1   156
#>  [98,]  51  13      0         99        1   160
#>  [99,]  55   3      1          1        0   150
#> [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,]  52   4      0        180        1   180
#> [105,]  50   5      1          4        1   150
#> [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,]  52   5      0        175        1   117
#> [112,]  55   1      0        180        0   127
#> [113,]  55   2      0          2        0   145
#> [114,]  54   1      0        180        0   162
#> [115,]  56   2      0        180        0   132
#> [116,]  53  18      1          9        1   150
#> [117,]  54   3      0        180        1   180
#> [118,]  52  16      0         16        0   152
#> [119,]  53  10      1          9        0   172
#> [120,]  52  16      1         14        0   170
#> [121,]  55   6      0        180        1   100
#> [122,]  55   6      1          5        1   138
#> [123,]  54   3      0        180        0   128
#> [124,]  56   3      0          8        1   139
#> [125,]  55   1      0          2        0   130
#> [126,]  54   7      1          2        0   129
#> [127,]  54   2      1          1        0   135
#> [128,]  52   9      1          3        0   170
#> [129,]  57   5      1          3        1   138
#> [130,]  57   1      0          1        1   100
#> [131,]  56   4      1          0        1   140
#> [132,]  52   2      0        180        0   140
#> [133,]  52  15      1         14        0   130
#> [134,]  53   3      1          0        1   200
#> [135,]  57  10      0        180        1   170
#> [136,]  58   8      0          8        1   130
#> [137,]  54   5      0        180        1   108
#> [138,]  55   3      1          1        1   156
#> [139,]  59   3      1          1        0   172
#> [140,]  57   4      0        180        1   119
#> [141,]  58   6      1          0        1    90
#> [142,]  53  15      1         10        1   130
#> [143,]  55  13      0        166        1   140
#> [144,]  57   4      1          2        1   185
#> [145,]  53   7      1          0        1   120
#> [146,]  57  11      1         10        1   129
#> [147,]  55   3      1          2        0   140
#> [148,]  54   7      1          0        1   141
#> [149,]  59  15      1         10        0   140
#> [150,]  55   5      1          0        0   140
#> [151,]  56   7      1          5        1   120
#> [152,]  57   1      0        180        0   148
#> [153,]  60  11      1          9        0   106
#> [154,]  59   3      0        180        0   120
#> [155,]  58   4      1          0        1   160
#> [156,]  57   2      0          2        1   120
#> [157,]  57   5      0        180        1   130
#> [158,]  58  11      1          9        1   124
#> [159,]  57  10      1          9        0   103
#> [160,]  59   5      0        180        1   155
#> [161,]  61   9      0          9        1   160
#> [162,]  58   4      1          3        0   120
#> [163,]  59   2      1          1        0   140
#> [164,]  58   8      0        161        1   140
#> [165,]  58  14      1          6        0   190
#> [166,]  61   3      1          2        1   102
#> [167,]  58   1      0          1        1   100
#> [168,]  61  20      1         13        0   130
#> [169,]  57   4      1          3        0   138
#> [170,]  58  19      1         13        1   140
#> [171,]  56  13      1          6        1   158
#> [172,]  59   9      1          0        1    80
#> [173,]  55   4      1          3        1   160
#> [174,]  58  11      0        172        1   135
#> [175,]  60  12      1          0        1   114
#> [176,]  55   9      1          7        1   135
#> [177,]  56   8      1          8        0   120
#> [178,]  61  13      1         12        1   130
#> [179,]  59  11      1          8        1   190
#> [180,]  57   1      0          1        0   126
#> [181,]  58   5      1          1        1   135
#> [182,]  61  13      0         13        0   210
#> [183,]  58   8      1          5        0   152
#> [184,]  62  10      1          0        1   153
#> [185,]  62   7      1          2        1   180
#> [186,]  57   3      1          0        0   100
#> [187,]  61  18      0        170        0   140
#> [188,]  61  28      1          7        0   133
#> [189,]  58   8      1          3        1   150
#> [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,]  60  17      1          8        1   140
#> [196,]  62   4      1          3        0   173
#> [197,]  58   2      0         30        0   202
#> [198,]  59   1      0        180        0   155
#> [199,]  59  16      1          9        1   133
#> [200,]  61  13      0         13        0   120
#> [201,]  61   5      0          5        1   110
#> [202,]  57  18      1          9        1    93
#> [203,]  61   5      0          5        1   160
#> [204,]  58  11      1          9        0   179
#> [205,]  61   7      0        180        0   135
#> [206,]  62   3      0        180        1   105
#> [207,]  63   4      0        180        1   190
#> [208,]  63  15      1         10        1   126
#> [209,]  64   4      0        180        0   130
#> [210,]  60  18      1         13        0   132
#> [211,]  59   8      0        180        1   140
#> [212,]  61   9      1          9        1   150
#> [213,]  62   7      0          7        0   150
#> [214,]  58   2      0        180        0   127
#> [215,]  59   4      0        180        0   196
#> [216,]  60   7      0          7        0   140
#> [217,]  63   1      0          1        0   130
#> [218,]  62   6      0        180        0   170
#> [219,]  61  15      1         13        0   170
#> [220,]  59   4      0          4        0   149
#> [221,]  64  10      1          9        0   160
#> [222,]  62   6      0          6        0   120
#> [223,]  63  12      1         10        0   200
#> [224,]  59  10      0        180        1   130
#> [225,]  60   8      0         17        1   130
#> [226,]  61   6      1          1        1   117
#> [227,]  64  12      1         11        0   160
#> [228,]  66   1      1          0        1   120
#> [229,]  63  10      1          0        1   148
#> [230,]  63  14      1          9        0   123
#> [231,]  65  36      1         11        0   140
#> [232,]  66   3      1          1        0   127
#> [233,]  61  10      1          2        1   194
#> [234,]  64  32      1          9        1   160
#> [235,]  63  12      1          9        0   114
#> [236,]  63   7      0        180        0   120
#> [237,]  66   5      1          0        1   110
#> [238,]  60   6      0        180        0   130
#> [239,]  64  21      1         10        0   190
#> [240,]  61  12      1         11        0   154
#> [241,]  64   9      0        180        0   150
#> [242,]  61   4      0        180        1   113
#> [243,]  63  16      1          7        1   110
#> [244,]  64   7      0        180        1   120
#> [245,]  66   6      1          1        1   130
#> [246,]  62   3      1          1        1   199
#> [247,]  65   6      0          9        0   112
#> [248,]  63   5      1          4        0   170
#> [249,]  63   2      1          1        0   180
#> [250,]  64   2      0          2        0   201
#> [251,]  66  18      1          5        0   142
#> [252,]  66  16      1         11        1   169
#> [253,]  61  15      1         10        0   130
#> [254,]  63   9      1          8        1   160
#> [255,]  63   3      1          2        0   120
#> [256,]  63   2      1          0        0   140
#> [257,]  64  19      1          8        1   160
#> [258,]  65   8      1          0        1   140
#> [259,]  67   6      0        180        1   170
#> [260,]  65  15      1         11        1   160
#> [261,]  68   5      1          4        1   150
#> [262,]  64  13      1         12        1   150
#> [263,]  64   6      1          0        1   125
#> [264,]  66  13      1          0        0   118
#> [265,]  64  14      1         13        1   150
#> [266,]  65   3      0          3        0   105
#> [267,]  64   0      0          0        1   148
#> [268,]  67   4      1          3        0   130
#> [269,]  66   3      1          0        1   135
#> [270,]  66   6      1          0        1   140
#> [271,]  65   2      1          1        1   170
#> [272,]  68   1      0        180        1   166
#> [273,]  63   7      1          0        0   162
#> [274,]  67   8      1          1        1   130
#> [275,]  63  10      0         16        1   160
#> [276,]  66  14      0        180        0   130
#> [277,]  64   1      0          1        1   120
#> [278,]  65  17      1         14        1   100
#> [279,]  63   8      1          1        1   162
#> [280,]  65  18      1          3        0   120
#> [281,]  63   1      1          0        1   155
#> [282,]  63  10      0         18        1   130
#> [283,]  67  11      0         11        0   150
#> [284,]  68  11      0        180        0   160
#> [285,]  68  14      0         79        0   172
#> [286,]  66  12      1         10        1   150
#> [287,]  65  15      1         12        1   150
#> [288,]  66  11      1          0        0   100
#> [289,]  65   4      1          2        1   145
#> [290,]  69  12      0         15        1   140
#> [291,]  66  15      1         13        1   160
#> [292,]  63   2      0        180        0   150
#> [293,]  65  11      1          6        0   130
#> [294,]  69   6      0        180        1   100
#> [295,]  66   9      1          8        0   130
#> [296,]  63   8      0        180        1   120
#> [297,]  68  14      1         13        1   140
#> [298,]  65   8      1          0        1    90
#> [299,]  66   3      0          3        1   138
#> [300,]  69   1      1          0        0   170
#> [301,]  67   1      0        180        1   160
#> [302,]  68  10      1         10        1   150
#> [303,]  65   1      1          0        0   133
#> [304,]  67   7      1          4        1   130
#> [305,]  63   2      1          0        0    99
#> [306,]  67   2      0        180        0   184
#> [307,]  66  19      1         12        1   150
#> [308,]  67  12      1         12        0   160
#> [309,]  65   4      1          1        0   130
#> [310,]  64   4      0        179        0   160
#> [311,]  66   4      0        180        1   130
#> [312,]  70  15      1         12        1   132
#> [313,]  64   4      0        180        1   140
#> [314,]  64   0      1          0        1   118
#> [315,]  67   2      0         18        0   131
#> [316,]  66   7      1          5        1   131
#> [317,]  69   4      1          3        1   150
#> [318,]  65  13      1         12        1   130
#> [319,]  69   8      0         93        0   140
#> [320,]  66   6      0        180        0   140
#> [321,]  68  18      1          0        1   160
#> [322,]  71   3      0          5        0   112
#> [323,]  70   7      1          0        1   190
#> [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,]  69   8      0        180        1   153
#> [329,]  70  14      0        171        0   166
#> [330,]  66   4      0        180        0   130
#> [331,]  67  10      1          9        0   200
#> [332,]  65   2      0        180        0   130
#> [333,]  68   7      1          0        1   150
#> [334,]  69   3      1          2        0   151
#> [335,]  65  14      1         13        1   150
#> [336,]  71   7      0          7        0   230
#> [337,]  69   5      0          5        1   142
#> [338,]  71   3      0        103        0   133
#> [339,]  69   3      0          3        1   130
#> [340,]  70  22      1         13        0   103
#> [341,]  67   1      0         36        1   104
#> [342,]  67   5      0          5        0   130
#> [343,]  69   8      1          5        1   195
#> [344,]  69   6      1          4        1   174
#> [345,]  72   3      1          0        1   132
#> [346,]  69   8      1          7        1   108
#> [347,]  67   3      0        180        0   110
#> [348,]  66   2      1          1        0   123
#> [349,]  69  19      0        180        0   130
#> [350,]  68  18      0         18        1   100
#> [351,]  69  11      1          0        1   120
#> [352,]  66   2      0        180        0   130
#> [353,]  67   7      1          4        0   122
#> [354,]  69   4      1          3        0   132
#> [355,]  69   8      1          2        0   121
#> [356,]  67  13      1          9        0   130
#> [357,]  70   3      0        123        0   130
#> [358,]  70   9      0        180        1   142
#> [359,]  67  22      1          1        1   140
#> [360,]  68   3      0         19        0   135
#> [361,]  67  12      1          8        0   120
#> [362,]  67   1      0          1        1    60
#> [363,]  69   5      0         76        0   120
#> [364,]  67   8      1          0        1   130
#> [365,]  72  13      1         11        1   195
#> [366,]  68  10      1          8        1   160
#> [367,]  66  24      1         13        0   130
#> [368,]  72  30      1          0        1   145
#> [369,]  70   7      0          7        0   102
#> [370,]  71   6      0          9        0   120
#> [371,]  69  10      1          6        1   120
#> [372,]  70  11      0        180        1   210
#> [373,]  72  12      1         10        0   170
#> [374,]  67   9      0        180        0   158
#> [375,]  70   5      0        180        0   150
#> [376,]  67   4      1          1        0   134
#> [377,]  70   3      0        180        0   121
#> [378,]  69   3      0        180        0   220
#> [379,]  68   4      1          3        0   210
#> [380,]  72   5      0         28        0   120
#> [381,]  71   5      0        180        0   191
#> [382,]  73   6      0        180        1   117
#> [383,]  69   8      1          1        0   164
#> [384,]  68   7      0        180        1   130
#> [385,]  72  16      1          1        1   130
#> [386,]  70   4      0        180        0   180
#> [387,]  69   1      1          0        0   155
#> [388,]  73   6      1          0        1   270
#> [389,]  72   8      1          1        1   150
#> [390,]  71   2      1          0        1   180
#> [391,]  73   7      0          7        1   140
#> [392,]  70   3      0          3        1   159
#> [393,]  70  13      1          9        0   100
#> [394,]  73   0      0        180        1   161
#> [395,]  74   8      1          0        1    85
#> [396,]  69   2      1          0        1   110
#> [397,]  71  15      1         11        0   165
#> [398,]  68   9      0        180        1   120
#> [399,]  71  20      1         10        0   140
#> [400,]  73   3      1          0        1   136
#> [401,]  71   8      1          7        0   149
#> [402,]  73  10      1          8        0   106
#> [403,]  69  12      1          1        1   149
#> [404,]  70  26      1         11        1   120
#> [405,]  74   4      0          4        0   120
#> [406,]  73   4      0         58        1   160
#> [407,]  73   6      0        180        0   110
#> [408,]  72  15      1          0        1   150
#> [409,]  72   8      1          0        1   140
#> [410,]  74   3      0          3        1   150
#> [411,]  73  17      1         11        0   140
#> [412,]  71  14      1         13        1   170
#> [413,]  72  10      1          8        1   153
#> [414,]  69   7      0        180        1   144
#> [415,]  72  15      1         13        0   156
#> [416,]  70   8      0          8        0   120
#> [417,]  71  10      1          9        1   120
#> [418,]  73  10      1          9        1   146
#> [419,]  73  10      1         10        1   120
#> [420,]  74  15      1          9        1   179
#> [421,]  73   1      0          1        1    80
#> [422,]  71  11      1          8        0   110
#> [423,]  73  10      1          8        0   120
#> [424,]  70   7      1          4        0   184
#> [425,]  72   1      1          1        0   168
#> [426,]  72   7      0         57        1   145
#> [427,]  73  10      0        180        0   162
#> [428,]  72  11      0         11        1   140
#> [429,]  70   3      0          3        0   150
#> [430,]  73   5      1          3        1   112
#> [431,]  76  25      1         12        1   170
#> [432,]  73  12      1         12        1   140
#> [433,]  72   2      0        180        0   120
#> [434,]  72   4      1          0        1   197
#> [435,]  71   3      1          0        0   144
#> [436,]  74  34      1          8        1   233
#> [437,]  76   3      1          0        1   120
#> [438,]  71  32      1         12        1   107
#> [439,]  72   5      0        180        0   154
#> [440,]  76   5      0          5        1   130
#> [441,]  77   4      0          4        0   185
#> [442,]  75   3      1          1        0   180
#> [443,]  72   7      1          2        0   142
#> [444,]  71  16      0        180        0   140
#> [445,]  73  10      1         10        0   124
#> [446,]  74   7      0        180        1   150
#> [447,]  74   2      1          1        0   140
#> [448,]  74  19      1          4        1   200
#> [449,]  73   6      0          6        1   114
#> [450,]  75  23      1         14        1   110
#> [451,]  74   2      0        180        0   190
#> [452,]  72   4      0         85        1   120
#> [453,]  76  17      1          0        1   200
#> [454,]  73   4      1          3        1   125
#> [455,]  76  13      1         10        0   110
#> [456,]  75   4      1          0        1   122
#> [457,]  75   7      0          7        0   190
#> [458,]  75   0      0          0        1   130
#> [459,]  73  13      1         11        0   195
#> [460,]  75  12      0         12        1   160
#> [461,]  74   8      1          0        1   105
#> [462,]  74   6      0        180        0   160
#> [463,]  74   2      0        180        0   111
#> [464,]  73   1      0         52        1   105
#> [465,]  73   0      0        180        0   156
#> [466,]  72   5      0        180        0   120
#> [467,]  76  44      1         10        0   105
#> [468,]  76   5      0        180        0   185
#> [469,]  74  10      1          0        1   135
#> [470,]  73  33      1         12        1   175
#> [471,]  77   5      1          0        0   123
#> [472,]  76  12      1         11        1   120
#> [473,]  78   5      1          0        1   170
#> [474,]  73   7      1          0        0   174
#> [475,]  74   6      0         79        1   140
#> [476,]  75   3      1          1        1   171
#> [477,]  79  10      1          8        0   190
#> [478,]  74   2      1          0        1   130
#> [479,]  78  18      0         18        1   144
#> [480,]  76  29      0         47        0    90
#> [481,]  73   8      1          1        1   162
#> [482,]  73  11      1          2        1   110
#> [483,]  74  15      0        180        1   172
#> [484,]  74   7      0          7        0   161
#> [485,]  76  13      1          1        1   170
#> [486,]  78  32      1          9        1   198
#> [487,]  79   6      0        180        0   170
#> [488,]  80  10      1          6        1   147
#> [489,]  78   0      0        180        1   212
#> [490,]  78  13      1          5        0   130
#> [491,]  75   5      0        119        1   150
#> [492,]  75  12      1          1        1   120
#> [493,]  80   8      0          8        1   120
#> [494,]  75  13      1          6        0   150
#> [495,]  74  10      1          8        0   135
#> [496,]  76   1      0          1        1    83
#> [497,]  79   4      0         80        0   145
#> [498,]  77   2      1          0        1   143
#> [499,]  78  10      0        180        1   130
#> [500,]  76  11      1          0        0   120
#> [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,]  78  11      1          1        1   140
#> [509,]  78  14      1          0        1   140
#> [510,]  81   1      0          1        0   130
#> [511,]  76  10      1          8        0   180
#> [512,]  77   6      0          6        1   107
#> [513,]  80   3      1          0        1   120
#> [514,]  78  11      0        180        1   135
#> [515,]  76   1      0          1        1   140
#> [516,]  76   1      0          1        1    90
#> [517,]  79   3      0          3        0   120
#> [518,]  81   1      0        180        0   120
#> [519,]  80  15      1         12        1   150
#> [520,]  82   5      0          8        1   120
#> [521,]  76   7      0        161        0   151
#> [522,]  79  10      0         10        1   120
#> [523,]  80  15      1          0        1    90
#> [524,]  79  28      0        164        0   100
#> [525,]  80   9      0        118        1   186
#> [526,]  80   6      0        173        1   160
#> [527,]  78  32      0        180        1   130
#> [528,]  81   3      0        180        0   184
#> [529,]  81   2      0        175        0   172
#> [530,]  78   7      0          7        1   147
#> [531,]  77  13      1          0        1   190
#> [532,]  78  15      0         15        0   165
#> [533,]  78   4      0        180        0   175
#> [534,]  78  26      1          5        0   194
#> [535,]  81   4      1          1        1   104
#> [536,]  78   3      1          1        1   152
#> [537,]  77  10      1          8        1   130
#> [538,]  82   3      1          1        1   144
#> [539,]  80   2      1          1        0   168
#> [540,]  80   6      1          0        1   119
#> [541,]  78   2      0        180        0   148
#> [542,]  82   1      1          0        1    82
#> [543,]  79  10      0        180        1   150
#> [544,]  81   1      0        108        0   129
#> [545,]  78  12      0        180        0   134
#> [546,]  79   1      0        125        0   193
#> [547,]  82  21      1          2        0   155
#> [548,]  84  22      1         10        0   180
#> [549,]  79   4      0          4        1   121
#> [550,]  83   9      1          5        1   170
#> [551,]  83   5      0        180        0   148
#> [552,]  79   7      1          6        0   130
#> [553,]  81   5      0        177        0    41
#> [554,]  80  11      1          8        0   170
#> [555,]  78  23      1         10        1   145
#> [556,]  79   4      0          4        1   183
#> [557,]  82   8      1          1        0   128
#> [558,]  79   1      0        180        1   170
#> [559,]  84   5      1          1        1    85
#> [560,]  81  20      1          9        0   170
#> [561,]  81  16      0         16        1   110
#> [562,]  80   6      1          0        1   150
#> [563,]  80  11      1          8        0   110
#> [564,]  81   8      0        180        0   146
#> [565,]  79   7      0        177        0   197
#> [566,]  79   0      1          0        1    96
#> [567,]  81   2      1          1        0   198
#> [568,]  83   2      0          2        1   155
#> [569,]  82   6      0        128        1   100
#> [570,]  80   3      1          1        1   230
#> [571,]  84   5      0        180        1   203
#> [572,]  84   4      0          4        1    85
#> [573,]  79   9      1          8        0   150
#> [574,]  80  13      1          8        1   140
#> [575,]  84   4      0         89        1   129
#> [576,]  79   4      0          4        1    60
#> [577,]  80   6      0         71        1   189
#> [578,]  82  19      0         19        0   120
#> [579,]  80  30      1         13        0   220
#> [580,]  83   9      0        180        0   198
#> [581,]  81  14      1         12        1   128
#> [582,]  83   2      0        154        0   130
#> [583,]  82   0      0          2        1   100
#> [584,]  85   9      1          6        1   160
#> [585,]  83   1      0        180        0   160
#> [586,]  81   4      0          4        0   175
#> [587,]  84  15      1         13        1   110
#> [588,]  81  12      0         12        1   163
#> [589,]  82  16      1          8        0   103
#> [590,]  82   5      1          0        1   146
#> [591,]  81   4      0          4        0   160
#> [592,]  86  12      0        180        1   120
#> [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,]  84   6      0        165        0   145
#> [598,]  81  13      0        180        0   152
#> [599,]  85   3      0          3        1   118
#> [600,]  81   2      1          0        1   118
#> [601,]  81   4      0        180        0   160
#> [602,]  83   9      0        180        1   149
#> [603,]  82   1      0        180        1   193
#> [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,]  83  19      0         43        0   150
#> [609,]  84   3      1          2        0   125
#> [610,]  83  10      1          0        1   190
#> [611,]  86   2      0        180        1   169
#> [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,]  84   9      0         92        1   110
#> [618,]  84   3      0        180        1   170
#> [619,]  82   4      0          4        0   130
#> [620,]  86   6      0          6        1   117
#> [621,]  84  13      0         62        1   100
#> [622,]  83  20      1          3        1   150
#> [623,]  85  22      0         22        1   184
#> [624,]  83   9      0         65        1   150
#> [625,]  87   2      0        180        1   130
#> [626,]  86   6      0         46        0   173
#> [627,]  88   3      0        115        0   110
#> [628,]  88   2      0        180        1    68
#> [629,]  89   4      0          4        1   153
#> [630,]  89   5      0        119        1   140
#> [631,]  87   6      0        180        1   110
#> [632,]  87   1      0          1        0   170
#> [633,]  84   8      0        180        1   119
#> [634,]  85   8      0          8        1   136
#> [635,]  84   2      0        110        1   174
#> [636,]  87  29      0         29        1    97
#> [637,]  87  15      1          9        1   138
#> [638,]  90  14      0         14        1   100
#> [639,]  88   1      0          1        0   135
#> [640,]  86   4      0        180        1   145
#> [641,]  91   8      0          8        0   100
#> [642,]  87   6      1          0        0   125
#> [643,]  86   3      1          0        1    80
#> [644,]  88   8      0         50        1   154
#> [645,]  90  11      1         10        1   186
#> [646,]  87   6      0        126        1   168
#> [647,]  86  10      0        180        1   137
#> [648,]  86   9      1          7        0   130
#> [649,]  90   4      1          0        0   121
#> [650,]  90   5      1          0        1   125
#> [651,]  88   3      1          2        0   159
#> [652,]  88   5      0        158        0   100
#> [653,]  87   7      0         74        1   105
#> [654,]  89  12      1          0        1   130
#> [655,]  89   2      0        168        0   118
#> [656,]  91   5      0        169        1   176
#> [657,]  89   4      0          4        1   159
#> [658,]  91   0      0          0        0     0
#> [659,]  89  14      0        180        1    84
#> [660,]  90  18      0        180        0   188
#> [661,]  91   4      1          0        1   120
#> [662,]  90   1      0          1        1   118
#> [663,]  91   2      0          2        1   116
#> [664,]  94   8      0          8        1   142
#> [665,]  92   4      0         76        1   149
#> [666,]  90  16      0         16        1   106
#> [667,]  95   8      1          5        1   150
#> [668,]  92   2      0          2        0   112
#> [669,]  93   4      0        180        1   135
#> [670,]  96  15      1          0        1   140
#> 
#> $y
#>   [1] 180.0+   5.0+   5.0+ 180.0+   2.0+ 180.0+ 115.0  180.0+  12.0    5.0+
#>  [11] 180.0+ 180.0+ 180.0+ 180.0+   2.0+ 180.0+   5.0+ 180.0+   3.0  180.0+
#>  [21] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0+ 180.0+ 155.0+ 180.0+ 180.0+
#>  [31]   5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [41] 180.0+ 180.0+ 180.0+ 180.0+  73.0    5.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [51] 180.0+   5.0+ 180.0+ 180.0+ 180.0+ 177.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [61]  10.0+ 180.0+ 180.0+   7.0  180.0+   2.0    1.0  179.0+ 179.0+ 180.0+
#>  [71] 180.0+   4.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+  88.0+
#>  [81] 180.0+   4.0+ 180.0+ 180.0+ 180.0+  77.0  180.0+ 180.0+ 180.0+ 180.0+
#>  [91] 180.0+ 180.0+ 180.0+  85.0  180.0+ 180.0+ 166.0+  99.0  180.0+   7.0+
#> [101]   6.0+ 180.0+ 180.0+ 180.0+ 171.0+ 174.0+   6.0+   1.0  180.0+   9.0+
#> [111] 175.0+ 180.0+   2.0  180.0+ 180.0+ 180.0+ 180.0+  16.0+ 180.0+  16.0 
#> [121] 180.0+ 180.0+ 180.0+   8.0    2.0  180.0+ 180.0+ 180.0+ 140.0    1.0 
#> [131] 165.0  180.0+ 180.0+ 180.0+ 180.0+   8.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [141] 180.0+ 180.0+ 166.0+   4.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [151] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0  180.0+ 180.0+ 180.0+ 180.0+
#> [161]   9.0+ 180.0+ 180.0+ 161.0+ 171.0+   3.0    1.0  180.0+ 180.0+  19.0 
#> [171] 180.0+   9.0+ 180.0+ 172.0+ 172.0+  24.0    8.0  180.0+ 180.0+   1.0+
#> [181] 180.0+  13.0+   8.0+ 180.0+ 180.0+ 180.0+ 170.0   94.0  180.0+   7.0 
#> [191]   7.0+   6.0  180.0+ 180.0+ 180.0+ 180.0+  30.0  180.0+ 180.0+  13.0+
#> [201]   5.0   18.0    5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [211] 180.0+ 180.0+   7.0+ 180.0+ 180.0+   7.0+   1.0  180.0+ 180.0+   4.0+
#> [221] 167.0    6.0+ 180.0+ 180.0+  17.0  180.0+  12.0  180.0+ 180.0+  14.0+
#> [231]  36.0    3.0+  88.0  180.0+  12.0  180.0+ 180.0+ 180.0+ 180.0+  12.0+
#> [241] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   9.0  180.0+ 180.0+   2.0+
#> [251]  18.0+ 180.0+ 180.0+ 180.0+   3.0+   2.0+ 103.0   15.0  180.0+ 180.0+
#> [261]   5.0+  13.0  180.0+ 166.0+  14.0+   3.0    0.5+ 180.0+   3.0+ 180.0+
#> [271] 175.0+ 180.0+   7.0+   8.0   16.0  180.0+   1.0  180.0+ 180.0+ 123.0+
#> [281]   1.0+  18.0   11.0+ 180.0+  79.0   80.0   15.0+ 180.0+   4.0+  15.0 
#> [291] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   8.0+   3.0  175.0 
#> [301] 180.0+  10.0  180.0+ 180.0+ 180.0+ 180.0+  19.0+  12.0  180.0+ 179.0+
#> [311] 180.0+ 180.0+ 180.0+   0.5   18.0    7.0+ 152.0+ 180.0+  93.0  180.0+
#> [321]  18.0+   5.0    7.0+ 180.0+ 180.0+   1.0  180.0+ 180.0+ 171.0  180.0+
#> [331] 174.0+ 180.0+ 180.0+ 180.0+  14.0+   7.0+   5.0+ 103.0    3.0+ 180.0+
#> [341]  36.0    5.0+ 180.0+  97.0  180.0+   8.0+ 180.0+   2.0+ 180.0+  18.0 
#> [351] 180.0+ 180.0+   7.0  180.0+   8.0+  13.0+ 123.0  180.0+  51.0   19.0 
#> [361] 180.0+   1.0   76.0  180.0+ 132.0   10.0+ 180.0+ 162.0    7.0+   9.0 
#> [371] 180.0+ 180.0+  12.0  180.0+ 180.0+  76.0  180.0+ 180.0+ 180.0+  28.0 
#> [381] 180.0+ 180.0+ 180.0+ 180.0+  16.0+ 180.0+ 180.0+   6.0  180.0+ 180.0+
#> [391]   7.0+   3.0+  13.0+ 180.0+ 180.0+   2.0  180.0+ 180.0+  20.0  180.0+
#> [401]   8.0   87.0   12.0  180.0+   4.0+  58.0  180.0+ 180.0+ 180.0+   3.0 
#> [411] 180.0+  14.0+  10.0+ 180.0+ 180.0+   8.0+ 179.0+ 180.0+  15.0  180.0+
#> [421]   1.0  180.0+  10.0  104.0+   1.0   57.0  180.0+  11.0    3.0+   5.0 
#> [431] 180.0+  12.0  180.0+ 180.0+ 180.0+  34.0  180.0+ 177.0+ 180.0+   5.0 
#> [441]   4.0+ 180.0+   7.0  180.0+  10.0  180.0+ 180.0+ 180.0+   6.0  180.0+
#> [451] 180.0+  85.0   17.0+ 180.0+ 174.0+   4.0    7.0    0.5  180.0+  12.0 
#> [461] 180.0+ 180.0+ 180.0+  52.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+  33.0 
#> [471]   5.0   12.0  180.0+   7.0+  79.0    3.0  180.0+ 176.0+  18.0   47.0 
#> [481] 180.0+  11.0  180.0+   7.0  180.0+  32.0  180.0+  10.0  180.0+ 172.0 
#> [491] 119.0   12.0    8.0  180.0+ 180.0+   1.0   80.0    2.0  180.0+  11.0 
#> [501] 152.0+   3.0   29.0   24.0   32.0   23.0  180.0+ 180.0+ 180.0+   1.0 
#> [511]  10.0+   6.0    3.0+ 180.0+   1.0    1.0    3.0  180.0+ 180.0+   8.0 
#> [521] 161.0   10.0+ 180.0+ 164.0  118.0  173.0  180.0+ 180.0+ 175.0+   7.0+
#> [531]  22.0   15.0+ 180.0+ 171.0+  71.0    3.0+  10.0  180.0+  10.0    6.0 
#> [541] 180.0+   1.0  180.0+ 108.0  180.0+ 125.0  180.0+ 180.0+   4.0    9.0+
#> [551] 180.0+ 180.0+ 177.0+ 169.0   70.0    4.0  180.0+ 180.0+ 180.0+  20.0 
#> [561]  16.0  180.0+ 180.0+ 180.0+ 177.0+   0.5  180.0+   2.0  128.0    3.0+
#> [571] 180.0+   4.0  180.0+ 180.0+  89.0    4.0   71.0   19.0   30.0  180.0+
#> [581] 180.0+ 154.0    2.0  180.0+ 180.0+   4.0+ 180.0+  12.0   16.0+   5.0+
#> [591]   4.0+ 180.0+  88.0  126.0    8.0  180.0+ 165.0  180.0+   3.0+ 180.0+
#> [601] 180.0+ 180.0+ 180.0+   5.0  180.0+ 174.0    6.0   43.0  180.0+ 180.0+
#> [611] 180.0+  14.0    3.0   13.0  180.0+ 180.0+  92.0  180.0+   4.0    6.0+
#> [621]  62.0   20.0   22.0   65.0  180.0+  46.0  115.0  180.0+   4.0  119.0 
#> [631] 180.0+   1.0+ 180.0+   8.0  110.0   29.0  180.0+  14.0    1.0+ 180.0+
#> [641]   8.0   25.0    3.0   50.0   11.0  126.0  180.0+ 180.0+   4.0   89.0 
#> [651]  75.0  158.0   74.0  180.0+ 168.0  169.0    4.0    0.5  180.0+ 180.0+
#> [661]   4.0    1.0+   2.0    8.0+  76.0   16.0    8.0    2.0  180.0+  15.0+
#> 
#> $weights
#> NULL
#> 
#> $offset
#> NULL
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> surv.cindex 
#>   0.8406061