Convert to a Competing Risk Prediction

Convert object to a PredictionCompRisks. An existing PredictionCompRisks is returned unchanged. Use mlr3::as_prediction() to convert its internal PredictionDataCompRisks data.

Usage

as_prediction_cmprsk(x, ...)

# S3 method for class 'PredictionCompRisks'
as_prediction_cmprsk(x, ...)

# S3 method for class 'list'
as_prediction_cmprsk(x, ...)

# S3 method for class 'data.frame'
as_prediction_cmprsk(x, ...)

# S3 method for class 'PredictionDataCompRisks'
as_prediction(x, check = FALSE, ...)

Arguments

x

(any)
Object to convert.

...

(any)
Additional arguments.

check

(logical(1))
Whether to validate internal PredictionDataCompRisks data during conversion with mlr3::as_prediction(). Use TRUE for user-supplied data; FALSE is intended for already validated internal data. With FALSE, correct behavior is not guaranteed if the input is invalid.

Value

PredictionCompRisks.

Details

A named list must contain row_ids, truth, and cif. These elements are passed to the PredictionCompRisks constructor and validated there (using check = TRUE by default). The cif element must be a nonempty list of matrices, with one matrix for every task cause. This requirement also applies to internal prediction data of PredictionDataCompRisks class.

If x is a data.frame/data.table input, the following requirements must be met:

• Cols row_ids, time, event, and a list-column CIF must be present.

• The per-observation CIF object must be a named list of numeric vectors (one per cause), with names exactly "1", "2", ..., "K", in that order (where K is the number of competing risks).

• Cause names should be identical across observations and in the same order; CIF vectors corresponding to the same cause should be of equal length (same time points).

• Values for the event column must be 0 (censoring) or one of the consecutive cause codes 1, 2, ..., K.

Examples

library(mlr3)
task = tsk("pbc")
learner = lrn("cmprsk.aalen")
learner$train(task)
p = learner$predict(task)

# from an existing PredictionCompRisks object (no change)
as_prediction_cmprsk(p)
#> 
#> ── <PredictionCompRisks> for 276 observations: ─────────────────────────────────
#>  row_ids time event       CIF
#>        1   13     2 <list[2]>
#>        2  147     0 <list[2]>
#>        3   33     2 <list[2]>
#>      ---  ---   ---       ---
#>      274   30     0 <list[2]>
#>      275   27     0 <list[2]>
#>      276   25     0 <list[2]>

# Convert a named list to a PredictionCompRisks object
# The list must contain `row_ids`, `truth`, and `cif` elements
p_list = list(
  row_ids = p$row_ids,
  truth = p$truth,
  cif = p$cif
)
as_prediction_cmprsk(p_list)
#> 
#> ── <PredictionCompRisks> for 276 observations: ─────────────────────────────────
#>  row_ids time event       CIF
#>        1   13     2 <list[2]>
#>        2  147     0 <list[2]>
#>        3   33     2 <list[2]>
#>      ---  ---   ---       ---
#>      274   30     0 <list[2]>
#>      275   27     0 <list[2]>
#>      276   25     0 <list[2]>

# Convert a data.frame to a PredictionCompRisks object
# The data.frame must contain the required columns: row_ids, time, event, and CIF
tab = as.data.frame(as.data.table(p))
as_prediction_cmprsk(tab)
#> 
#> ── <PredictionCompRisks> for 276 observations: ─────────────────────────────────
#>  row_ids time event       CIF
#>        1   13     2 <list[2]>
#>        2  147     0 <list[2]>
#>        3   33     2 <list[2]>
#>      ---  ---   ---       ---
#>      274   30     0 <list[2]>
#>      275   27     0 <list[2]>
#>      276   25     0 <list[2]>

# Convert internal prediction data using as_prediction()
p$data
#> <PredictionDataCompRisks:276>
as_prediction(p$data)
#> 
#> ── <PredictionCompRisks> for 276 observations: ─────────────────────────────────
#>  row_ids time event       CIF
#>        1   13     2 <list[2]>
#>        2  147     0 <list[2]>
#>        3   33     2 <list[2]>
#>      ---  ---   ---       ---
#>      274   30     0 <list[2]>
#>      275   27     0 <list[2]>
#>      276   25     0 <list[2]>