Prediction Object for Competing Risks — PredictionCompRisks • mlr3cmprsk

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

The task_type is set to "cmprsk".

Causes use consecutive codes 1, 2, ..., K, matching the task used for prediction. By design, predictions contain one CIF matrix for every cause in task$cmp_events, including causes that are not observed among the predicted observations. Filtering prediction rows retains all cause levels and CIFs, even for unobserved causes. Combining predictions requires the same cause set.

Accessing all-cause survival or cause-specific hazard functions or similar quantities from a LearnerCompRisks object is not possible at the moment.

Super class

mlr3::Prediction -> PredictionCompRisks

Active bindings

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

Methods

Inherited methods

`PredictionCompRisks$new()`

Creates a new instance of this R6 class.

Usage

PredictionCompRisks$new(
  task = NULL,
  row_ids = task$row_ids,
  truth = task$truth(),
  cif,
  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. State names must be "1", "2", ..., "K", in that order, matching the CIF list.
cif: (list())
A required list of two or more matrix objects. Each matrix represents a different competing event (or cause) and 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 cause names in the truth object, i.e. "1", "2", ..., "K", exactly in that order.
check: (logical(1))
If TRUE, performs argument checks. Use TRUE for user-supplied data. With FALSE, inputs are assumed valid and correct behavior is not guaranteed.

Details

The cif input is a list of CIF matrices. With check = TRUE, nonempty predictions are validated using assert_cif_list(). This checks the list structure and cause names, the time points used for prediction, and validates each CIF matrix, including probabilities in [0, 1] and non-decreasing probabilities over time.

With options(mlr3cmprsk.warn_cif_sum = TRUE), joint coherence is checked separately by aligning the CIF matrices on a common time grid and summing their probabilities across causes for each observation and time point. A sum greater than 1, allowing a numerical tolerance of sqrt(.Machine$double.eps), triggers a warning of class Mlr3WarningCIFSumExceedsOne. This diagnostic is disabled by default; see the Package Options section in mlr3cmprsk-package. The prediction is retained without modifying its probabilities. Such sums can occur with independently fitted cause-specific models, such as the Fine-Gray model.

`PredictionCompRisks$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]>
#>      ---  ---   ---       ---
#>      266   41     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 5 6 7 9 10 11 12 16          17          18          19          20
#> [1,] 0 0 0 0 0 0 0  0  0  0  0 0.005405405 0.005405405 0.005405405 0.005405405
#> [2,] 0 0 0 0 0 0 0  0  0  0  0 0.005405405 0.005405405 0.005405405 0.005405405
#> [3,] 0 0 0 0 0 0 0  0  0  0  0 0.005405405 0.005405405 0.005405405 0.005405405
#> [4,] 0 0 0 0 0 0 0  0  0  0  0 0.005405405 0.005405405 0.005405405 0.005405405
#> [5,] 0 0 0 0 0 0 0  0  0  0  0 0.005405405 0.005405405 0.005405405 0.005405405
#>               22         24         25         26         27
#> [1,] 0.005405405 0.01081081 0.01081081 0.01081081 0.01621622
#> [2,] 0.005405405 0.01081081 0.01081081 0.01081081 0.01621622
#> [3,] 0.005405405 0.01081081 0.01081081 0.01081081 0.01621622
#> [4,] 0.005405405 0.01081081 0.01081081 0.01081081 0.01621622
#> [5,] 0.005405405 0.01081081 0.01081081 0.01081081 0.01621622
# CIF matrix for competing event 2 (first 5 test observations and 20 time points)
p$cif[["2"]][1:5, 1:20]
#>                1          2          4          5          6          7
#> [1,] 0.005405405 0.01621622 0.02702703 0.03243243 0.04324324 0.04864865
#> [2,] 0.005405405 0.01621622 0.02702703 0.03243243 0.04324324 0.04864865
#> [3,] 0.005405405 0.01621622 0.02702703 0.03243243 0.04324324 0.04864865
#> [4,] 0.005405405 0.01621622 0.02702703 0.03243243 0.04324324 0.04864865
#> [5,] 0.005405405 0.01621622 0.02702703 0.03243243 0.04324324 0.04864865
#>               9         10         11         12         16         17
#> [1,] 0.05405405 0.05945946 0.06486486 0.07027027 0.07567568 0.07567568
#> [2,] 0.05405405 0.05945946 0.06486486 0.07027027 0.07567568 0.07567568
#> [3,] 0.05405405 0.05945946 0.06486486 0.07027027 0.07567568 0.07567568
#> [4,] 0.05405405 0.05945946 0.06486486 0.07027027 0.07567568 0.07567568
#> [5,] 0.05405405 0.05945946 0.06486486 0.07027027 0.07567568 0.07567568
#>              18         19         20        22        24        25        26
#> [1,] 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027 0.1135135 0.1189189
#> [2,] 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027 0.1135135 0.1189189
#> [3,] 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027 0.1135135 0.1189189
#> [4,] 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027 0.1135135 0.1189189
#> [5,] 0.08108108 0.08648649 0.09189189 0.0972973 0.1027027 0.1135135 0.1189189
#>             27
#> [1,] 0.1189189
#> [2,] 0.1189189
#> [3,] 0.1189189
#> [4,] 0.1189189
#> [5,] 0.1189189

# data.table conversion
tab = as.data.table(p)
tab$CIF[[1]] # for first test observation, list of CIF vectors
#> $`1`
#>           1           2           4           5           6           7 
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 
#>           9          10          11          12          16          17 
#> 0.000000000 0.000000000 0.000000000 0.000000000 0.000000000 0.005405405 
#>          18          19          20          22          24          25 
#> 0.005405405 0.005405405 0.005405405 0.005405405 0.010810811 0.010810811 
#>          26          27          28          29          30          31 
#> 0.010810811 0.016216216 0.021655618 0.021655618 0.021655618 0.021655618 
#>          33          35          38          39          40          44 
#> 0.021655618 0.027167790 0.027167790 0.027167790 0.027167790 0.027167790 
#>          46          47          48          49          50          51 
#> 0.027167790 0.033488601 0.033488601 0.039974302 0.046460004 0.046460004 
#>          55          57          58          60          63          66 
#> 0.046460004 0.046460004 0.046460004 0.046460004 0.046460004 0.054253750 
#>          68          69          73          74          75          78 
#> 0.054253750 0.054253750 0.062439768 0.062439768 0.062439768 0.062439768 
#>          81          83          84          85          90          91 
#> 0.081340395 0.081340395 0.081340395 0.081340395 0.081340395 0.081340395 
#>          93         101         104         106         107         110 
#> 0.081340395 0.081340395 0.081340395 0.081340395 0.081340395 0.081340395 
#>         111         112         117         123         126         134 
#> 0.081340395 0.081340395 0.081340395 0.081340395 0.081340395 0.081340395 
#>         137 
#> 0.081340395 
#> 
#> $`2`
#>           1           2           4           5           6           7 
#> 0.005405405 0.016216216 0.027027027 0.032432432 0.043243243 0.048648649 
#>           9          10          11          12          16          17 
#> 0.054054054 0.059459459 0.064864865 0.070270270 0.075675676 0.075675676 
#>          18          19          20          22          24          25 
#> 0.081081081 0.086486486 0.091891892 0.097297297 0.102702703 0.113513514 
#>          26          27          28          29          30          31 
#> 0.118918919 0.118918919 0.124358321 0.135237124 0.146115927 0.151555329 
#>          33          35          38          39          40          44 
#> 0.157030753 0.168055097 0.179232557 0.190410017 0.196079743 0.202101080 
#>          46          47          48          49          50          51 
#> 0.214334907 0.220655718 0.227141420 0.227141420 0.227141420 0.233627121 
#>          55          57          58          60          63          66 
#> 0.247339748 0.254263279 0.261256047 0.275830446 0.283277803 0.283277803 
#>          68          69          73          74          75          78 
#> 0.291164332 0.299050861 0.299050861 0.307343191 0.324148979 0.342190486 
#>          81          83          84          85          90          91 
#> 0.351640800 0.361091113 0.370704363 0.380667185 0.390817986 0.400968786 
#>          93         101         104         106         107         110 
#> 0.411533905 0.436271744 0.449309254 0.462346764 0.475384273 0.489236627 
#>         111         112         117         123         126         134 
#> 0.503088982 0.517419003 0.547140529 0.564831914 0.602076934 0.630857177 
#>         137 
#> 0.662835224 
#>