LMScore | R Documentation |
Methods (AUCt, Brier) to score the predictive performance of dynamic risk markers from LM super models
LMScore( preds, formula, metrics = c("auc", "brier"), cause, tLM, unit, split.method, B, ... )
preds |
A named list of prediction models, where allowed entries are outputs from predLMrisk |
formula |
A survival or event history formula. The left hand side is used to compute the expected event status. If none is given, it is obtained from the prediction object. |
metrics |
Character vector specifying which metrics to apply. Choices are "auc" and "brier". Case matters. |
cause |
Cause of interest if considering competing risks |
tLM |
Landmark times for which scores must be given. These must be a subset of LM times used during the prediction |
unit |
Time unit for window of prediction, e.g., "year", "month", etc. Used for printing results. |
split.method |
Method for cross-validation. Right now, as in riskRegression, the only option is bootcv. |
B |
The number of bootstap steps for cross-validation. |
... |
Additional arguments to pass to Score (riskRegression package) |
See the Github for example code
An object of class "LMScore", which has components:
auct: dataframe containing time-dependent auc information if "auc" was a metric
briert: dataframe containing time-dependent brier score if "brier" was a metric
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