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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 0.00359    44   2.753 0.2001       6
#> 1se 0.08484    10   2.930 0.1918       3
#> 
#> $x
#>        age los revasc revascdays stchange sysbp
#>   [1,]  28   9      0        180        1   107
#>   [2,]  35   5      1          2        0   172
#>   [3,]  35  10      1          9        0   106
#>   [4,]  35   2      0        180        0   121
#>   [5,]  37   9      0        180        1   151
#>   [6,]  38   2      0        115        0   150
#>   [7,]  36   1      0        180        1   155
#>   [8,]  38  12      1          8        1   120
#>   [9,]  36   5      1          0        1   115
#>  [10,]  33   6      1          1        1   115
#>  [11,]  38  16      1         10        0   160
#>  [12,]  38  12      1         11        1    92
#>  [13,]  40  12      1          9        0   153
#>  [14,]  42   3      1          1        1   130
#>  [15,]  40   2      1          1        1   148
#>  [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,]  41   2      1          1        0   166
#>  [23,]  40   1      1          0        1   145
#>  [24,]  42  15      1         13        1   125
#>  [25,]  40   3      1          1        0   170
#>  [26,]  42  12      1         10        1   170
#>  [27,]  43   2      1          1        1   116
#>  [28,]  42   2      0        180        1   124
#>  [29,]  41  10      1          8        0   150
#>  [30,]  44   3      0        180        0   141
#>  [31,]  41  13      1          1        0   140
#>  [32,]  45   9      1          7        0   110
#>  [33,]  45   6      0        180        1   170
#>  [34,]  41   5      1          4        1   141
#>  [35,]  44   2      1          1        1   150
#>  [36,]  43   2      0        180        1   140
#>  [37,]  46   2      1          1        0   126
#>  [38,]  47   4      1          3        0   118
#>  [39,]  48  15      0        180        1   160
#>  [40,]  45   3      0        150        0   130
#>  [41,]  44   3      1          0        1   180
#>  [42,]  43  29      0        180        1   180
#>  [43,]  43  10      0        180        0   185
#>  [44,]  47   6      1          0        1   116
#>  [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   6      1          0        1   100
#>  [52,]  47   2      0        180        0   108
#>  [53,]  45   5      0        180        1   190
#>  [54,]  46   5      1          3        0   130
#>  [55,]  44   2      0        180        0   142
#>  [56,]  47   3      1          1        1   120
#>  [57,]  48   3      0        180        0   154
#>  [58,]  48  12      1         11        0   200
#>  [59,]  47   5      1          3        1   130
#>  [60,]  47   9      1          6        0   170
#>  [61,]  49   4      0        180        0   117
#>  [62,]  47  10      0         10        1   140
#>  [63,]  48   2      1          0        0   184
#>  [64,]  47   7      0        180        0   145
#>  [65,]  50   4      1          1        0   125
#>  [66,]  50   6      1          2        1   140
#>  [67,]  49   7      1          7        1   110
#>  [68,]  46   3      1          1        1   140
#>  [69,]  46   9      1          9        1   122
#>  [70,]  50   7      0        180        1   110
#>  [71,]  49   2      0          2        0   105
#>  [72,]  51   1      0          1        1   145
#>  [73,]  49  15      1         11        1   160
#>  [74,]  47   2      0        180        0   150
#>  [75,]  49  23      0        179        1   112
#>  [76,]  46   6      1          0        1   156
#>  [77,]  50   4      0          4        1   100
#>  [78,]  50   1      1          0        0   150
#>  [79,]  50   9      0        180        0   130
#>  [80,]  49   7      1          4        1    90
#>  [81,]  47   8      0        180        0   160
#>  [82,]  51   8      0        180        1   140
#>  [83,]  52   2      0        180        0   155
#>  [84,]  46   1      1          1        0   145
#>  [85,]  50   4      1          1        0   150
#>  [86,]  53   8      0         36        1   160
#>  [87,]  52   4      1          4        0   152
#>  [88,]  49   9      1          3        0   102
#>  [89,]  49  15      0        180        1   160
#>  [90,]  53   5      0        180        1   140
#>  [91,]  54  17      1         12        1   102
#>  [92,]  53   7      1          0        0   199
#>  [93,]  54   6      1          3        0   129
#>  [94,]  51   3      1          1        0   140
#>  [95,]  50   2      0          5        1   106
#>  [96,]  50  10      1          6        0   122
#>  [97,]  48   3      1          2        0   150
#>  [98,]  51  25      1          1        0   202
#>  [99,]  53   4      0          4        0   140
#> [100,]  52  14      1          7        1   200
#> [101,]  48   6      0        180        0   160
#> [102,]  48  11      1         10        0   120
#> [103,]  53   4      1          0        1   156
#> [104,]  51  13      0         99        1   160
#> [105,]  49  16      0         16        0   125
#> [106,]  55   3      1          1        0   150
#> [107,]  52   7      1          2        0   154
#> [108,]  54   9      1          1        0   130
#> [109,]  51  13      1         11        0   145
#> [110,]  52   4      0        180        0   183
#> [111,]  50   3      0        174        1   153
#> [112,]  55  28      1         13        1   160
#> [113,]  49   6      1          0        1   130
#> [114,]  49   1      0          1        1   110
#> [115,]  53   9      0          9        1    95
#> [116,]  53   8      1          0        1   130
#> [117,]  50   7      1          0        1   127
#> [118,]  56   4      1          1        1   130
#> [119,]  52   5      0        175        1   117
#> [120,]  55   1      0        180        0   127
#> [121,]  54   7      1          0        1   100
#> [122,]  56   3      0        180        1   193
#> [123,]  52   8      0        180        0   119
#> [124,]  54   3      0        180        1   180
#> [125,]  53  10      1          9        0   172
#> [126,]  52  16      1         14        0   170
#> [127,]  53  15      0         15        1    90
#> [128,]  53   4      0        180        1   150
#> [129,]  55   6      0        180        1   100
#> [130,]  54  12      1          0        1   190
#> [131,]  55   2      0        134        1   140
#> [132,]  56   3      0          8        1   139
#> [133,]  57   3      0          3        0   120
#> [134,]  54   7      1          2        0   129
#> [135,]  54   2      1          1        0   135
#> [136,]  52   9      1          3        0   170
#> [137,]  54   2      1          1        1   176
#> [138,]  57   5      1          3        1   138
#> [139,]  57   1      0        180        1   156
#> [140,]  57   1      0          1        1   100
#> [141,]  52  15      1         14        0   130
#> [142,]  56  14      1         11        0   130
#> [143,]  53   3      1          0        1   200
#> [144,]  57  10      0        180        1   170
#> [145,]  58   8      0          8        1   130
#> [146,]  54   5      0        180        1   108
#> [147,]  55   3      1          1        1   156
#> [148,]  57   0      0          0        1   150
#> [149,]  58   6      1          0        1    90
#> [150,]  54  17      1          8        1   227
#> [151,]  55   9      1          2        1   147
#> [152,]  55  13      0        166        1   140
#> [153,]  56   5      0          5        1   150
#> [154,]  54  23      1          8        0   120
#> [155,]  57   4      1          2        1   185
#> [156,]  53   4      0        147        1   145
#> [157,]  53   7      1          0        1   120
#> [158,]  55   3      1          2        0   140
#> [159,]  54   7      1          0        1   141
#> [160,]  56   4      0          4        0   164
#> [161,]  59  15      1         10        0   140
#> [162,]  55   5      1          0        0   140
#> [163,]  56   7      1          5        1   120
#> [164,]  55   2      0          2        0   106
#> [165,]  59   9      1          1        1   125
#> [166,]  57   1      0        180        0   148
#> [167,]  60  11      1          9        0   106
#> [168,]  59   3      0        180        0   120
#> [169,]  58   4      1          0        1   160
#> [170,]  57   2      0          2        1   120
#> [171,]  60   5      1          1        0   138
#> [172,]  58  11      1          9        1   124
#> [173,]  55   5      1          0        1   160
#> [174,]  57  10      1          9        0   103
#> [175,]  58  26      1          0        1   189
#> [176,]  61   9      0          9        1   160
#> [177,]  60   0      1          0        1    80
#> [178,]  59   2      1          1        0   140
#> [179,]  61   4      1          3        0   151
#> [180,]  61   9      1          8        0   150
#> [181,]  61   3      1          2        1   102
#> [182,]  58   1      0          1        1   100
#> [183,]  61  20      1         13        0   130
#> [184,]  58  10      0         10        1   150
#> [185,]  57   4      1          3        0   138
#> [186,]  56  14      0         45        0   130
#> [187,]  57   3      1          2        0   120
#> [188,]  56  13      1          6        1   158
#> [189,]  56  18      1         11        1   165
#> [190,]  59   9      1          0        1    80
#> [191,]  55   4      1          3        1   160
#> [192,]  58  11      0        172        1   135
#> [193,]  60  12      1          0        1   114
#> [194,]  55   9      1          7        1   135
#> [195,]  61   4      1          0        1   115
#> [196,]  56   8      1          8        0   120
#> [197,]  59  11      1          8        1   190
#> [198,]  57   1      0          1        0   126
#> [199,]  57  15      1         13        1   110
#> [200,]  59   5      1          2        0   182
#> [201,]  61   8      0         77        0   120
#> [202,]  58   8      1          5        0   152
#> [203,]  57   3      1          0        0   100
#> [204,]  61  18      0        170        0   140
#> [205,]  58   8      1          3        1   150
#> [206,]  57  12      1          9        1   120
#> [207,]  62   4      1          0        0   160
#> [208,]  60  17      1          8        1   140
#> [209,]  58   3      1          0        1   146
#> [210,]  62   4      1          3        0   173
#> [211,]  59  16      1          9        1   133
#> [212,]  63   6      0         28        1   120
#> [213,]  61  13      0         13        0   120
#> [214,]  57  18      1          9        1    93
#> [215,]  61   5      0          5        1   160
#> [216,]  58  11      1          9        0   179
#> [217,]  62  17      1         10        1   180
#> [218,]  62   1      1          0        1   172
#> [219,]  58   7      0        180        1   150
#> [220,]  63   3      1          1        0   180
#> [221,]  63   1      0        180        1   130
#> [222,]  61   7      0        180        0   135
#> [223,]  62   3      0        180        1   105
#> [224,]  63  15      1         10        1   126
#> [225,]  63   4      1          1        0   155
#> [226,]  60  18      1         13        0   132
#> [227,]  58   2      0        180        0   127
#> [228,]  59   4      0        180        0   196
#> [229,]  60   7      0          7        0   140
#> [230,]  60   7      1          1        1    90
#> [231,]  63   1      0          1        0   162
#> [232,]  63   1      0          1        0   130
#> [233,]  62   6      0        180        0   170
#> [234,]  61  15      1         13        0   170
#> [235,]  59   4      0          4        0   149
#> [236,]  62   6      0          6        0   120
#> [237,]  63  12      1         10        0   200
#> [238,]  59  10      0        180        1   130
#> [239,]  60   8      0         17        1   130
#> [240,]  61   6      1          1        1   117
#> [241,]  64  12      1         11        0   160
#> [242,]  66   1      1          0        1   120
#> [243,]  64   6      1          0        1   140
#> [244,]  63  14      1          9        0   123
#> [245,]  65  36      1         11        0   140
#> [246,]  63   7      0        180        0   120
#> [247,]  65  10      1          8        1   120
#> [248,]  64  21      1         10        0   190
#> [249,]  61   4      0        180        1   113
#> [250,]  65   3      0        180        1   190
#> [251,]  64   7      0        180        1   120
#> [252,]  66   6      1          1        1   130
#> [253,]  63  12      0         12        1   150
#> [254,]  65   6      0          9        0   112
#> [255,]  63   5      1          4        0   170
#> [256,]  67  11      0         11        1   100
#> [257,]  66  18      1          5        0   142
#> [258,]  61  15      1         10        0   130
#> [259,]  63   9      1          8        1   160
#> [260,]  64  19      1          8        1   160
#> [261,]  65   8      1          0        1   140
#> [262,]  68   5      1          4        1   150
#> [263,]  64  13      1         12        1   150
#> [264,]  66   7      1          0        1   115
#> [265,]  64  14      1         13        1   150
#> [266,]  65   3      0          3        0   105
#> [267,]  67   4      1          3        0   130
#> [268,]  68   1      0        180        1   166
#> [269,]  64  10      1          9        1   110
#> [270,]  63   7      1          0        0   162
#> [271,]  67   8      1          1        1   130
#> [272,]  63  10      0         16        1   160
#> [273,]  66  14      0        180        0   130
#> [274,]  64   1      0          1        1   120
#> [275,]  65  17      1         14        1   100
#> [276,]  65  18      1          3        0   120
#> [277,]  63  10      0         18        1   130
#> [278,]  67  11      0         11        0   150
#> [279,]  68  11      0        180        0   160
#> [280,]  66  12      1         10        1   150
#> [281,]  69  12      0         15        1   140
#> [282,]  69  21      1         10        0   180
#> [283,]  69   6      0        180        1   100
#> [284,]  63   8      0        180        1   120
#> [285,]  68  14      1         13        1   140
#> [286,]  65   8      1          0        1    90
#> [287,]  69   1      1          0        0   170
#> [288,]  67   1      0        180        1   160
#> [289,]  65   1      1          0        0   133
#> [290,]  67   7      1          4        1   130
#> [291,]  67   2      0        180        0   184
#> [292,]  65  10      1          1        1   148
#> [293,]  66  19      1         12        1   150
#> [294,]  67  12      1         12        0   160
#> [295,]  65   4      1          1        0   130
#> [296,]  64   4      0        179        0   160
#> [297,]  66   4      0        180        1   130
#> [298,]  64  11      0         11        0   125
#> [299,]  64   4      0        180        1   140
#> [300,]  64   0      1          0        1   118
#> [301,]  66   4      0        180        0   177
#> [302,]  68   4      1          0        1   160
#> [303,]  69   4      1          3        1   150
#> [304,]  65  13      1         12        1   130
#> [305,]  64  21      0         21        1   155
#> [306,]  68   4      0          4        1   190
#> [307,]  70   7      1          0        1   190
#> [308,]  68   7      0        150        0   210
#> [309,]  71  20      1          0        1   160
#> [310,]  66   9      1          3        1   151
#> [311,]  66   1      1          1        1   165
#> [312,]  69   8      0        180        1   153
#> [313,]  70  14      0        171        0   166
#> [314,]  66   4      0        180        0   130
#> [315,]  67  10      1          9        0   200
#> [316,]  67   6      1          4        0   130
#> [317,]  69   0      0          0        1   148
#> [318,]  65   2      0        180        0   130
#> [319,]  68   7      1          0        1   150
#> [320,]  69   3      1          2        0   151
#> [321,]  67  14      1         13        0   130
#> [322,]  65  14      1         13        1   150
#> [323,]  69   8      0        180        1   180
#> [324,]  66   2      0          2        1   228
#> [325,]  71   6      0         45        1   158
#> [326,]  69   5      0          5        1   142
#> [327,]  69   3      0          3        1   130
#> [328,]  67   1      0         36        1   104
#> [329,]  67   5      0          5        0   130
#> [330,]  68   6      0        180        0   145
#> [331,]  69   6      1          4        1   174
#> [332,]  72   3      1          0        1   132
#> [333,]  69   8      1          7        1   108
#> [334,]  67   3      0        180        0   110
#> [335,]  66   2      1          1        0   123
#> [336,]  68  18      0         18        1   100
#> [337,]  67  14      0        172        1   140
#> [338,]  67   7      1          4        0   122
#> [339,]  69   4      1          3        0   132
#> [340,]  68   2      0          7        1   130
#> [341,]  70   3      0        123        0   130
#> [342,]  70   9      0        180        1   142
#> [343,]  68   3      0         19        0   135
#> [344,]  67  12      1          8        0   120
#> [345,]  69   1      0          1        1   110
#> [346,]  67   1      0          1        1    60
#> [347,]  69   5      0         76        0   120
#> [348,]  67   8      1          0        1   130
#> [349,]  72  13      1         11        1   195
#> [350,]  68  10      1          8        1   160
#> [351,]  66  24      1         13        0   130
#> [352,]  70  35      1          0        1   105
#> [353,]  72  30      1          0        1   145
#> [354,]  70   7      0          7        0   102
#> [355,]  73  20      1          0        1   170
#> [356,]  71   6      0          9        0   120
#> [357,]  70  11      0        180        1   210
#> [358,]  72  12      1         10        0   170
#> [359,]  67   8      0        180        1   170
#> [360,]  67   9      0        180        0   158
#> [361,]  70   5      0        180        0   150
#> [362,]  67   4      1          1        0   134
#> [363,]  72   6      1          5        0   115
#> [364,]  71   1      0        173        1   188
#> [365,]  68  23      0        180        1   220
#> [366,]  70   3      0        180        0   121
#> [367,]  71   3      1          2        0   150
#> [368,]  72   5      0         28        0   120
#> [369,]  72  16      1          1        1   130
#> [370,]  73   6      1          0        1   270
#> [371,]  72   8      1          1        1   150
#> [372,]  71   2      1          0        1   180
#> [373,]  73   7      0          7        1   140
#> [374,]  68  15      1         13        1   130
#> [375,]  70  13      1          9        0   100
#> [376,]  72   6      0        180        1   130
#> [377,]  73   0      0        180        1   161
#> [378,]  74   8      1          0        1    85
#> [379,]  69   2      1          0        1   110
#> [380,]  71   3      1          1        0   150
#> [381,]  71  15      1         11        0   165
#> [382,]  74  20      0         20        1   180
#> [383,]  68   9      0        180        1   120
#> [384,]  71  20      1         10        0   140
#> [385,]  73   3      1          0        1   136
#> [386,]  70   5      1          0        1   190
#> [387,]  71  17      1         11        0   160
#> [388,]  71   8      1          7        0   149
#> [389,]  71   3      1          2        1   190
#> [390,]  70  26      1         11        1   120
#> [391,]  74   4      0          4        0   120
#> [392,]  72   5      1          3        1   160
#> [393,]  70   3      0        180        1   154
#> [394,]  73   6      0        180        0   110
#> [395,]  72   8      1          0        1   140
#> [396,]  74   3      0          3        1   150
#> [397,]  73  17      1         11        0   140
#> [398,]  69   2      1          1        1    80
#> [399,]  71  14      1         13        1   170
#> [400,]  74   7      1          0        1   117
#> [401,]  72  10      1          8        1   153
#> [402,]  69   7      0        180        1   144
#> [403,]  72  15      1         13        0   156
#> [404,]  71  10      1          9        1   120
#> [405,]  73  10      1         10        1   120
#> [406,]  74  15      1          9        1   179
#> [407,]  71   2      0         10        1   112
#> [408,]  73   1      0          1        1    80
#> [409,]  75   9      1          7        0   140
#> [410,]  75  13      1          1        1   130
#> [411,]  71  11      1          8        0   110
#> [412,]  72  15      1         12        1   120
#> [413,]  70   7      1          4        0   184
#> [414,]  72   1      1          1        0   168
#> [415,]  73  10      0        180        0   162
#> [416,]  70   3      0          3        0   150
#> [417,]  73   5      1          3        1   112
#> [418,]  75   1      0        180        1   140
#> [419,]  73   5      0        180        0   126
#> [420,]  76   3      1          0        1   120
#> [421,]  71  32      1         12        1   107
#> [422,]  72   5      0        180        0   154
#> [423,]  72   3      0        180        0   160
#> [424,]  76   5      0          5        1   130
#> [425,]  77  11      0         11        1   150
#> [426,]  77   4      0          4        0   185
#> [427,]  75   3      1          1        0   180
#> [428,]  72   7      1          2        0   142
#> [429,]  73  15      0         15        1   160
#> [430,]  71  16      0        180        0   140
#> [431,]  73  10      1         10        0   124
#> [432,]  74   7      0        180        1   150
#> [433,]  76   8      1          0        1   141
#> [434,]  75  23      1         14        1   110
#> [435,]  74   2      0        180        0   190
#> [436,]  72   4      1          3        0   160
#> [437,]  73   4      1          3        1   125
#> [438,]  75   7      0          7        0   190
#> [439,]  75   0      0          0        1   130
#> [440,]  73  13      1         11        0   195
#> [441,]  75  12      0         12        1   160
#> [442,]  74   8      1          0        1   105
#> [443,]  76  13      1          8        1   148
#> [444,]  75   4      1          2        1   188
#> [445,]  74   6      0        180        0   160
#> [446,]  75   1      0          1        1   125
#> [447,]  74   2      0        180        0   111
#> [448,]  73   1      0         52        1   105
#> [449,]  73   0      0        180        0   156
#> [450,]  72   5      0        180        0   120
#> [451,]  78  12      1         11        1   160
#> [452,]  76  44      1         10        0   105
#> [453,]  76   5      0        180        0   185
#> [454,]  74  10      1          0        1   135
#> [455,]  76   5      1          0        1   167
#> [456,]  75   9      0        180        1   140
#> [457,]  73  33      1         12        1   175
#> [458,]  77   5      1          0        0   123
#> [459,]  73  10      1          9        0   146
#> [460,]  76  12      1         11        1   120
#> [461,]  78   5      1          0        1   170
#> [462,]  73   7      1          0        0   174
#> [463,]  74   6      0         79        1   140
#> [464,]  75   3      1          1        1   171
#> [465,]  79  10      1          8        0   190
#> [466,]  78  18      0         18        1   144
#> [467,]  77   3      0        180        0   110
#> [468,]  73   8      1          1        1   162
#> [469,]  73  11      1          2        1   110
#> [470,]  74   2      0        180        0   100
#> [471,]  78   7      0          7        1   133
#> [472,]  74  15      0        180        1   172
#> [473,]  74   7      0          7        0   161
#> [474,]  79   6      0        180        0   170
#> [475,]  80  10      1          6        1   147
#> [476,]  78   0      0        180        1   212
#> [477,]  75   5      0        119        1   150
#> [478,]  75  12      1          1        1   120
#> [479,]  78  15      0        180        1   270
#> [480,]  80   8      0          8        1   120
#> [481,]  79   4      0         80        0   145
#> [482,]  78  12      1          9        0   150
#> [483,]  78   2      1          1        0   130
#> [484,]  75   4      1          0        0   212
#> [485,]  77   2      1          0        1   143
#> [486,]  78  10      0        180        1   130
#> [487,]  75  11      1          4        0   162
#> [488,]  75   3      0          3        0     0
#> [489,]  76   7      0         29        1   150
#> [490,]  77  24      0         24        1   160
#> [491,]  79   8      0         32        1   120
#> [492,]  80   9      0         23        1   128
#> [493,]  80   6      0          6        1   150
#> [494,]  76   3      1          0        1   140
#> [495,]  79  11      0        180        0   160
#> [496,]  79   2      1          0        1   121
#> [497,]  78  14      1          0        1   140
#> [498,]  81   1      0          1        0   130
#> [499,]  78  11      1          8        1   118
#> [500,]  76   4      0          4        1   160
#> [501,]  79   4      0          4        1   125
#> [502,]  76  12      1         10        1   127
#> [503,]  77   6      0          6        1   107
#> [504,]  80   3      1          0        1   120
#> [505,]  75   2      1          1        1   204
#> [506,]  78  11      0        180        1   135
#> [507,]  76   1      0          1        1   140
#> [508,]  77  31      1          3        1   161
#> [509,]  76   1      0          1        1    90
#> [510,]  78   7      1          0        1   110
#> [511,]  79   3      0          3        0   120
#> [512,]  77   7      0        180        1   170
#> [513,]  77   6      0          6        1   144
#> [514,]  79   4      1          0        1   120
#> [515,]  81   1      0        180        0   120
#> [516,]  80  15      1         12        1   150
#> [517,]  82   5      0          8        1   120
#> [518,]  80  40      1          0        1   138
#> [519,]  78   4      0         59        1   112
#> [520,]  80  17      1         12        0   100
#> [521,]  76   7      0        161        0   151
#> [522,]  81   4      1          2        1   126
#> [523,]  79  28      0        164        0   100
#> [524,]  79   1      0         37        1   140
#> [525,]  81   3      0        180        0   184
#> [526,]  81   2      0        175        0   172
#> [527,]  80   5      1          1        1   108
#> [528,]  78   4      0        180        0   175
#> [529,]  78  26      1          5        0   194
#> [530,]  81   4      1          1        1   104
#> [531,]  78  20      1          0        1   109
#> [532,]  80   1      0          1        0   100
#> [533,]  80   2      1          1        0   168
#> [534,]  79   6      0          6        0   152
#> [535,]  78   2      0        180        0   148
#> [536,]  82   1      1          0        1    82
#> [537,]  79  10      0        180        1   150
#> [538,]  78  12      0        180        0   134
#> [539,]  82  21      1          2        0   155
#> [540,]  79   4      0          4        1   121
#> [541,]  83   9      1          5        1   170
#> [542,]  81  11      1          8        0   160
#> [543,]  81   5      0        177        0    41
#> [544,]  80  11      1          8        0   170
#> [545,]  79   4      0          4        1   183
#> [546,]  78   9      1          4        1   120
#> [547,]  82   8      1          1        0   128
#> [548,]  79   1      0        180        1   170
#> [549,]  81  15      0        180        1   140
#> [550,]  80   7      1          0        1   146
#> [551,]  84   5      1          1        1    85
#> [552,]  83   8      0          8        0   115
#> [553,]  80  11      1          8        0   110
#> [554,]  81   8      0        180        0   146
#> [555,]  80   8      1          7        0   160
#> [556,]  79   7      0        177        0   197
#> [557,]  79   0      1          0        1    96
#> [558,]  85   4      0        180        0    90
#> [559,]  81   2      1          1        0   198
#> [560,]  82   6      0        128        1   100
#> [561,]  84   4      0        167        0   198
#> [562,]  80   3      1          1        1   230
#> [563,]  82  23      1          0        0   110
#> [564,]  84   4      0          4        1    85
#> [565,]  81   1      0          1        1   150
#> [566,]  84   1      0         38        1   205
#> [567,]  81   4      0         90        1   138
#> [568,]  85   3      1          2        1   160
#> [569,]  80  13      1          8        1   140
#> [570,]  84   4      0         89        1   129
#> [571,]  80   2      1          0        1   130
#> [572,]  79   4      0          4        1    60
#> [573,]  80   6      0         71        1   189
#> [574,]  83   1      0          1        1   100
#> [575,]  82  19      0         19        0   120
#> [576,]  80  30      1         13        0   220
#> [577,]  79  14      1          0        0   110
#> [578,]  81  14      1         12        1   128
#> [579,]  83   2      0        154        0   130
#> [580,]  82   0      0          2        1   100
#> [581,]  85   9      1          6        1   160
#> [582,]  83   1      0        180        0   160
#> [583,]  81  12      0         12        1   163
#> [584,]  82   5      1          0        1   146
#> [585,]  86  12      0        180        1   120
#> [586,]  83  12      1          2        1   170
#> [587,]  81  19      1         14        0   120
#> [588,]  82  15      1          0        0   183
#> [589,]  83   7      0        126        0   135
#> [590,]  86   8      0          8        1   132
#> [591,]  81  16      1          9        0   180
#> [592,]  86   3      0          3        1   140
#> [593,]  84   3      0        180        1   120
#> [594,]  81  13      0        180        0   152
#> [595,]  85   3      0          3        1   118
#> [596,]  81   2      1          0        1   118
#> [597,]  81   4      0        180        0   160
#> [598,]  83   9      0        180        1   149
#> [599,]  82   1      0        180        1   193
#> [600,]  87   2      0          5        1   137
#> [601,]  86   6      1          0        1   140
#> [602,]  84  16      0         70        1   150
#> [603,]  84   3      1          2        0   125
#> [604,]  83  10      1          0        1   190
#> [605,]  86   2      0        180        1   169
#> [606,]  84   3      0          3        1   121
#> [607,]  84   7      1          2        0   148
#> [608,]  87   2      0        180        0   113
#> [609,]  84   9      0         92        1   110
#> [610,]  84   3      0        180        1   170
#> [611,]  86   4      0         38        1   122
#> [612,]  82   4      0          4        0   130
#> [613,]  86  13      0        177        0   163
#> [614,]  86   6      0          6        1   117
#> [615,]  84  13      0         62        1   100
#> [616,]  83  20      1          3        1   150
#> [617,]  88   4      0          4        1   115
#> [618,]  85  22      0         22        1   184
#> [619,]  83   9      0         65        1   150
#> [620,]  87   2      0        180        1   130
#> [621,]  86   6      0         46        0   173
#> [622,]  88   3      0        115        0   110
#> [623,]  88   2      0        180        1    68
#> [624,]  86  15      1          8        1   109
#> [625,]  88   4      0          4        0    86
#> [626,]  89   4      0          4        1   153
#> [627,]  89   5      0        119        1   140
#> [628,]  87   6      0        180        1   110
#> [629,]  87   1      0          1        0   170
#> [630,]  85   8      0          8        1   136
#> [631,]  84   2      0        110        1   174
#> [632,]  87  15      1          9        1   138
#> [633,]  90  14      0         14        1   100
#> [634,]  88   1      0          1        0   135
#> [635,]  91   8      0          8        0   100
#> [636,]  87   2      0        180        0   160
#> [637,]  87   6      1          0        0   125
#> [638,]  88   7      0         24        0   119
#> [639,]  90  11      1         10        1   186
#> [640,]  87   6      0        126        1   168
#> [641,]  86  10      0        180        1   137
#> [642,]  90   4      1          0        0   121
#> [643,]  91   1      0          1        1    74
#> [644,]  87   5      0         36        1   150
#> [645,]  90   5      1          0        1   125
#> [646,]  89   3      1          1        1   160
#> [647,]  92   1      0          1        1   167
#> [648,]  88   5      0        158        0   100
#> [649,]  87   7      0         74        1   105
#> [650,]  89  12      1          0        1   130
#> [651,]  91   5      0        169        1   176
#> [652,]  89   4      0          4        1   159
#> [653,]  91   0      0          0        0     0
#> [654,]  89  14      0        180        1    84
#> [655,]  90  19      1         11        1   129
#> [656,]  94   6      0         50        0    78
#> [657,]  91   2      0          2        1   116
#> [658,]  92   4      0         76        1   149
#> [659,]  90  16      0         16        1   106
#> [660,]  90   3      0         67        0   162
#> [661,]  96   3      0         12        1    97
#> [662,]  95   8      1          5        1   150
#> [663,]  94   3      0         26        1   144
#> [664,]  91  12      0         53        1   212
#> [665,]  91   7      0          7        0   135
#> [666,]  93   0      1          0        1   122
#> [667,]  92   5      0         69        0   139
#> [668,]  93   4      0        180        1   135
#> [669,]  96   3      1          0        1   104
#> [670,]  96  15      1          0        1   140
#> 
#> $y
#>   [1] 180.0+   5.0+ 180.0+ 180.0+ 180.0+ 115.0  180.0+  12.0    5.0+ 180.0+
#>  [11] 180.0+ 180.0+ 180.0+ 180.0+   2.0+   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+ 180.0+
#>  [31] 180.0+ 180.0+ 180.0+   5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 150.0 
#>  [41] 180.0+ 180.0+ 180.0+   6.0+ 180.0+ 180.0+ 180.0+ 180.0+  73.0    5.0+
#>  [51] 180.0+ 180.0+ 180.0+   5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#>  [61] 180.0+  10.0+ 180.0+ 180.0+ 180.0+ 180.0+   7.0  180.0+ 180.0+ 180.0+
#>  [71]   2.0    1.0  179.0+ 180.0+ 179.0+ 180.0+   4.0+ 180.0+ 180.0+ 180.0+
#>  [81] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+  36.0    4.0+ 180.0+ 180.0+ 180.0+
#>  [91] 180.0+ 180.0+ 180.0+ 180.0+   5.0  180.0+ 180.0+ 180.0+   4.0+  85.0 
#> [101] 180.0+ 180.0+ 166.0+  99.0   16.0+ 180.0+   7.0+ 180.0+  13.0+ 180.0+
#> [111] 174.0+  28.0    6.0+   1.0    9.0+ 180.0+ 180.0+ 180.0+ 175.0+ 180.0+
#> [121]   7.0+ 180.0+ 180.0+ 180.0+ 180.0+  16.0   15.0+ 180.0+ 180.0+  12.0+
#> [131] 134.0+   8.0    3.0+ 180.0+ 180.0+ 180.0+ 180.0+ 140.0  180.0+   1.0 
#> [141] 180.0+ 180.0+ 180.0+ 180.0+   8.0+ 180.0+ 180.0+   0.5  180.0+ 171.0+
#> [151]  15.0  166.0+   5.0+ 180.0+   4.0+ 147.0+ 180.0+ 180.0+ 180.0+   4.0+
#> [161] 180.0+ 180.0+ 180.0+   2.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   2.0 
#> [171] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   9.0+   0.5  180.0+ 180.0+ 180.0+
#> [181]   3.0    1.0  180.0+  10.0+ 180.0+  45.0    3.0+ 180.0+ 180.0+   9.0+
#> [191] 180.0+ 172.0+ 172.0+  24.0  180.0+   8.0  180.0+   1.0+  15.0  180.0+
#> [201]  77.0    8.0+ 180.0+ 170.0  180.0+ 180.0+ 180.0+ 180.0+   3.0+ 180.0+
#> [211] 180.0+  28.0   13.0+  18.0    5.0+ 180.0+ 180.0+   1.0  180.0+ 180.0+
#> [221] 180.0+ 180.0+ 180.0+ 180.0+   4.0+ 180.0+ 180.0+ 180.0+   7.0+ 180.0+
#> [231]   1.0    1.0  180.0+ 180.0+   4.0+   6.0+ 180.0+ 180.0+  17.0  180.0+
#> [241]  12.0  180.0+ 180.0+  14.0+  36.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [251] 180.0+ 180.0+  12.0    9.0  180.0+  11.0+  18.0+ 180.0+ 180.0+ 103.0 
#> [261]  15.0    5.0+  13.0  179.0+  14.0+   3.0  180.0+ 180.0+ 180.0+   7.0+
#> [271]   8.0   16.0  180.0+   1.0  180.0+ 123.0+  18.0   11.0+ 180.0+  80.0 
#> [281]  15.0  174.0+ 180.0+ 180.0+ 180.0+   8.0+ 175.0  180.0+ 180.0+ 180.0+
#> [291] 180.0+ 180.0+  19.0+  12.0  180.0+ 179.0+ 180.0+  11.0+ 180.0+   0.5 
#> [301] 180.0+ 180.0+ 152.0+ 180.0+  21.0+   4.0    7.0+ 150.0  180.0+ 180.0+
#> [311]   1.0  180.0+ 171.0  180.0+ 174.0+   6.0    0.5  180.0+ 180.0+ 180.0+
#> [321] 180.0+  14.0+ 180.0+   2.0   45.0    5.0+   3.0+  36.0    5.0+ 180.0+
#> [331]  97.0  180.0+   8.0+ 180.0+   2.0+  18.0  172.0+   7.0  180.0+   7.0 
#> [341] 123.0  180.0+  19.0  180.0+   1.0    1.0   76.0  180.0+ 132.0   10.0+
#> [351] 180.0+ 180.0+ 162.0    7.0+ 124.0    9.0  180.0+  12.0  180.0+ 180.0+
#> [361] 180.0+  76.0  180.0+ 173.0+ 180.0+ 180.0+ 180.0+  28.0   16.0+   6.0 
#> [371] 180.0+ 180.0+   7.0+  15.0   13.0+ 180.0+ 180.0+ 180.0+   2.0    3.0+
#> [381] 180.0+  20.0  180.0+  20.0  180.0+ 180.0+ 180.0+   8.0    3.0  180.0+
#> [391]   4.0+ 180.0+ 180.0+ 180.0+ 180.0+   3.0  180.0+   2.0   14.0+ 180.0+
#> [401]  10.0+ 180.0+ 180.0+ 179.0+  15.0  180.0+  10.0    1.0  180.0+  13.0 
#> [411] 180.0+ 180.0+ 104.0+   1.0  180.0+   3.0+   5.0  180.0+ 180.0+ 180.0+
#> [421] 177.0+ 180.0+ 180.0+   5.0   11.0+   4.0+ 180.0+   7.0   15.0+ 180.0+
#> [431]  10.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+   7.0    0.5  180.0+
#> [441]  12.0  180.0+ 180.0+  46.0  180.0+   1.0  180.0+  52.0  180.0+ 180.0+
#> [451]  12.0  180.0+ 180.0+ 180.0+ 180.0+ 180.0+  33.0    5.0  180.0+  12.0 
#> [461] 180.0+   7.0+  79.0    3.0  180.0+  18.0  180.0+ 180.0+  11.0  180.0+
#> [471]   7.0  180.0+   7.0  180.0+  10.0  180.0+ 119.0   12.0  180.0+   8.0 
#> [481]  80.0  180.0+ 180.0+   4.0+   2.0  180.0+ 152.0+   3.0   29.0   24.0 
#> [491]  32.0   23.0    6.0    3.0+ 180.0+ 180.0+ 180.0+   1.0   11.0    4.0 
#> [501]   4.0  180.0+   6.0    3.0+   2.0+ 180.0+   1.0  171.0    1.0   43.0 
#> [511]   3.0  180.0+   6.0  138.0  180.0+ 180.0+   8.0   40.0   59.0   17.0 
#> [521] 161.0   93.0  164.0   37.0  180.0+ 175.0+   5.0+ 180.0+ 171.0+  71.0 
#> [531]  20.0+   1.0   10.0    6.0+ 180.0+   1.0  180.0+ 180.0+ 180.0+   4.0 
#> [541]   9.0+ 180.0+ 177.0+ 169.0    4.0  180.0+ 180.0+ 180.0+ 180.0+   7.0+
#> [551] 180.0+   8.0+ 180.0+ 180.0+ 180.0+ 177.0+   0.5  180.0+ 180.0+ 128.0 
#> [561] 167.0    3.0+  62.0    4.0    1.0   38.0   90.0  180.0+ 180.0+  89.0 
#> [571] 180.0+   4.0   71.0    1.0   19.0   30.0  180.0+ 180.0+ 154.0    2.0 
#> [581] 180.0+ 180.0+  12.0    5.0+ 180.0+  77.0  180.0+  83.0  126.0    8.0 
#> [591] 180.0+   3.0  180.0+ 180.0+   3.0+ 180.0+ 180.0+ 180.0+ 180.0+   5.0 
#> [601]   6.0   70.0  180.0+ 180.0+ 180.0+   3.0  180.0+ 180.0+  92.0  180.0+
#> [611]  38.0    4.0  177.0    6.0+  62.0   20.0    4.0   22.0   65.0  180.0+
#> [621]  46.0  115.0  180.0+ 180.0+   4.0    4.0  119.0  180.0+   1.0+   8.0 
#> [631] 110.0  180.0+  14.0    1.0+   8.0  180.0+  25.0   24.0   11.0  126.0 
#> [641] 180.0+   4.0    1.0   36.0   89.0    3.0+   1.0  158.0   74.0  180.0+
#> [651] 169.0    4.0    0.5  180.0+ 180.0+  50.0    2.0   76.0   16.0   67.0 
#> [661]  12.0    8.0   26.0   53.0    7.0+   0.5   69.0  180.0+   3.0   15.0+
#> 
#> $weights
#> NULL
#> 
#> $offset
#> NULL
#> 


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

# Score the predictions
predictions$score()
#> surv.cindex 
#>   0.8377533