Prediction Object for Competing Risks

This object stores the predictions returned by a learner of class LearnerCompRisks.

The task_type is set to "cmprsk".

For accessing survival and hazard functions, as well as other complex methods from a LearnerCompRisks object is not possible atm.

Super class

mlr3::Prediction -> PredictionCompRisks

Active bindings

truth: (Surv)
True (observed) outcome.
cif: (list())
Access the stored CIFs.

Methods

Inherited methods

Method `new()`

Creates a new instance of this R6 class.

Usage

PredictionCompRisks$new(
  task = NULL,
  row_ids = task$row_ids,
  truth = task$truth(),
  cif = NULL,
  check = TRUE
)

Arguments

task: (TaskCompRisks)
Task, used to extract defaults for row_ids and truth.
row_ids: (integer())
Row ids of the predicted observations, i.e. the row ids of the test set.
truth: (survival::Surv())
True (observed) response.
cif: (list())
A list of two or more matrix objects. Each matrix represents a different competing event and it stores the Cumulative Incidence function for each test observation. In each matrix, rows represent observations and columns time points. The names of the list must correspond to the competing event names (task$cmp_events).
check: (logical(1))
If TRUE, performs argument checks and predict type conversions.

Details

The cif input currently is a list of CIF matrices.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

PredictionCompRisks$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

library(mlr3)
task = tsk("pbc")
learner = lrn("cmprsk.aalen")
part = partition(task)
p = learner$train(task, part$train)$predict(task, part$test)
p
#> 
#> ── <PredictionCompRisks> for 91 observations: ──────────────────────────────────
#>  row_ids time event       CIF
#>        1   13     2 <list[2]>
#>        6   60     0 <list[2]>
#>       18   44     2 <list[2]>
#>      ---  ---   ---       ---
#>      268   40     0 <list[2]>
#>      274   30     0 <list[2]>
#>      276   25     0 <list[2]>

# CIF list: 1 matrix (obs x times) per competing event
names(p$cif) # competing events
#> [1] "1" "2"
# CIF matrix for competing event 1 (first 5 test observations and 20 time points)
p$cif[["1"]][1:5, 1:20]
#>      1 2 4 6 7 9 10 11 12 16 18 19 20 22         24         25         26
#> [1,] 0 0 0 0 0 0  0  0  0  0  0  0  0  0 0.01081081 0.01081081 0.01081081
#> [2,] 0 0 0 0 0 0  0  0  0  0  0  0  0  0 0.01081081 0.01081081 0.01081081
#> [3,] 0 0 0 0 0 0  0  0  0  0  0  0  0  0 0.01081081 0.01081081 0.01081081
#> [4,] 0 0 0 0 0 0  0  0  0  0  0  0  0  0 0.01081081 0.01081081 0.01081081
#> [5,] 0 0 0 0 0 0  0  0  0  0  0  0  0  0 0.01081081 0.01081081 0.01081081
#>              27         28         29
#> [1,] 0.01081081 0.01625043 0.01625043
#> [2,] 0.01081081 0.01625043 0.01625043
#> [3,] 0.01081081 0.01625043 0.01625043
#> [4,] 0.01081081 0.01625043 0.01625043
#> [5,] 0.01081081 0.01625043 0.01625043
# CIF matrix for competing event 2 (first 5 test observations and 20 time points)
p$cif[["2"]][1:5, 1:20]
#>               1          2          4          6          7          9
#> [1,] 0.01081081 0.02162162 0.03243243 0.04864865 0.05945946 0.06486486
#> [2,] 0.01081081 0.02162162 0.03243243 0.04864865 0.05945946 0.06486486
#> [3,] 0.01081081 0.02162162 0.03243243 0.04864865 0.05945946 0.06486486
#> [4,] 0.01081081 0.02162162 0.03243243 0.04864865 0.05945946 0.06486486
#> [5,] 0.01081081 0.02162162 0.03243243 0.04864865 0.05945946 0.06486486
#>              10         11         12         16         18        19        20
#> [1,] 0.07027027 0.07567568 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027
#> [2,] 0.07027027 0.07567568 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027
#> [3,] 0.07027027 0.07567568 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027
#> [4,] 0.07027027 0.07567568 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027
#> [5,] 0.07027027 0.07567568 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027
#>             22        24        25        26        27        28        29
#> [1,] 0.1081081 0.1135135 0.1243243 0.1297297 0.1297297 0.1351693 0.1460486
#> [2,] 0.1081081 0.1135135 0.1243243 0.1297297 0.1297297 0.1351693 0.1460486
#> [3,] 0.1081081 0.1135135 0.1243243 0.1297297 0.1297297 0.1351693 0.1460486
#> [4,] 0.1081081 0.1135135 0.1243243 0.1297297 0.1297297 0.1351693 0.1460486
#> [5,] 0.1081081 0.1135135 0.1243243 0.1297297 0.1297297 0.1351693 0.1460486

# data.table conversion
tab = as.data.table(p)
tab$CIF[[1]] # for first test observation, list of CIF vectors
#> $`1`
#>          1          2          4          6          7          9         10 
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 
#>         11         12         16         18         19         20         22 
#> 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 
#>         24         25         26         27         28         29         30 
#> 0.01081081 0.01081081 0.01081081 0.01081081 0.01625043 0.01625043 0.01625043 
#>         31         32         33         35         37         38         39 
#> 0.01625043 0.01625043 0.01625043 0.02172607 0.02172607 0.02172607 0.02172607 
#>         41         42         43         44         46         47         48 
#> 0.02172607 0.02743931 0.02743931 0.02743931 0.02743931 0.03371144 0.03371144 
#>         49         50         51         53         54         55         57 
#> 0.04009078 0.04647012 0.04647012 0.04647012 0.04647012 0.04647012 0.04647012 
#>         58         60         61         62         63         64         65 
#> 0.04647012 0.04647012 0.04647012 0.04647012 0.04647012 0.04647012 0.04647012 
#>         66         69         71         72         73         74         75 
#> 0.04647012 0.04647012 0.04647012 0.04647012 0.05479725 0.05479725 0.05479725 
#>         76         77         78         79         80         81         83 
#> 0.05479725 0.05479725 0.05479725 0.05479725 0.05479725 0.07346784 0.07346784 
#>         84         85         87         90         91         93         94 
#> 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 
#>         96         97         98        100        101        103        104 
#> 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 
#>        106        107        108        109        110        111        112 
#> 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 
#>        117        118        120        123        125        126        128 
#> 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 
#>        129        131        132        133        134        135        137 
#> 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 
#>        139        143        145        147        149 
#> 0.07346784 0.07346784 0.07346784 0.07346784 0.07346784 
#> 
#> $`2`
#>          1          2          4          6          7          9         10 
#> 0.01081081 0.02162162 0.03243243 0.04864865 0.05945946 0.06486486 0.07027027 
#>         11         12         16         18         19         20         22 
#> 0.07567568 0.08108108 0.08648649 0.09189189 0.09729730 0.10270270 0.10810811 
#>         24         25         26         27         28         29         30 
#> 0.11351351 0.12432432 0.12972973 0.12972973 0.13516935 0.14604858 0.15148820 
#>         31         32         33         35         37         38         39 
#> 0.15692781 0.15692781 0.16240345 0.17335474 0.17335474 0.18445707 0.19555940 
#>         41         42         43         44         46         47         48 
#> 0.19555940 0.19555940 0.19555940 0.20149071 0.21363355 0.21990567 0.22628501 
#>         49         50         51         53         54         55         57 
#> 0.22628501 0.23266435 0.23910065 0.23910065 0.23910065 0.25258045 0.25938578 
#>         58         60         61         62         63         64         65 
#> 0.26619112 0.28036305 0.28036305 0.28036305 0.28776049 0.28776049 0.28776049 
#>         66         69         71         72         73         74         75 
#> 0.28776049 0.29568631 0.29568631 0.29568631 0.29568631 0.30412159 0.32099215 
#>         76         77         78         79         80         81         83 
#> 0.32099215 0.32099215 0.33882674 0.34774403 0.34774403 0.35707932 0.36641462 
#>         84         85         87         90         91         93         94 
#> 0.37607182 0.38590146 0.38590146 0.39629821 0.40669495 0.41730388 0.41730388 
#>         96         97         98        100        101        103        104 
#> 0.41730388 0.41730388 0.41730388 0.41730388 0.44155284 0.44155284 0.45466039 
#>        106        107        108        109        110        111        112 
#> 0.46776794 0.48087549 0.48087549 0.48087549 0.49480226 0.50872903 0.52313604 
#>        117        118        120        123        125        126        128 
#> 0.55301723 0.55301723 0.55301723 0.57080366 0.57080366 0.60824876 0.60824876 
#>        129        131        132        133        134        135        137 
#> 0.60824876 0.60824876 0.60824876 0.60824876 0.63718362 0.63718362 0.66933345 
#>        139        143        145        147        149 
#> 0.66933345 0.66933345 0.66933345 0.66933345 0.66933345 
#>

Super class

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Details

Method clone()

Usage

Arguments

Examples

Method `new()`

Method `clone()`