Computes and extracts stratification data from a
familiarEnsemble object. This includes the data required to draw
Kaplan-Meier plots, as well as logrank and hazard-ratio tests between the
respective risk groups.
extract_risk_stratification_data(
object,
data,
cl = NULL,
is_pre_processed = FALSE,
ensemble_method = waiver(),
detail_level = waiver(),
confidence_level = waiver(),
message_indent = 0L,
verbose = FALSE,
...
)A list with data.tables containing information concerning risk group stratification.
A familiarEnsemble object, which is an ensemble of one or more
familiarModel objects.
A dataObject object, data.table or data.frame that
constitutes the data that are assessed.
Cluster created using the parallel package. This cluster is then
used to speed up computation through parallellisation.
Flag that indicates whether the data was already
pre-processed externally, e.g. normalised and clustered. Only used if the
data argument is a data.table or data.frame.
Method for ensembling predictions from models for the same sample. Available methods are:
median (default): Use the median of the predicted values as the ensemble
value for a sample.
mean: Use the mean of the predicted values as the ensemble value for a
sample.
(optional) Sets the level at which results are computed and aggregated.
ensemble: Results are computed at the ensemble level, i.e. over all
models in the ensemble. This means that, for example, bias-corrected
estimates of model performance are assessed by creating (at least) 20
bootstraps and computing the model performance of the ensemble model for
each bootstrap.
hybrid (default): Results are computed at the level of models in an
ensemble. This means that, for example, bias-corrected estimates of model
performance are directly computed using the models in the ensemble. If there
are at least 20 trained models in the ensemble, performance is computed for
each model, in contrast to ensemble where performance is computed for the
ensemble of models. If there are less than 20 trained models in the
ensemble, bootstraps are created so that at least 20 point estimates can be
made.
model: Results are computed at the model level. This means that, for
example, bias-corrected estimates of model performance are assessed by
creating (at least) 20 bootstraps and computing the performance of the model
for each bootstrap.
Note that each level of detail has a different interpretation for bootstrap
confidence intervals. For ensemble and model these are the confidence
intervals for the ensemble and an individual model, respectively. That is,
the confidence interval describes the range where an estimate produced by a
respective ensemble or model trained on a repeat of the experiment may be
found with the probability of the confidence level. For hybrid, it
represents the range where any single model trained on a repeat of the
experiment may be found with the probability of the confidence level. By
definition, confidence intervals obtained using hybrid are at least as
wide as those for ensemble. hybrid offers the correct interpretation if
the goal of the analysis is to assess the result of a single, unspecified,
model.
hybrid is generally computationally less expensive then ensemble, which
in turn is somewhat less expensive than model.
A non-default detail_level parameter can be specified for separate
evaluation steps by providing a parameter value in a named list with data
elements, e.g. list("auc_data"="ensemble", "model_performance"="hybrid").
This parameter can be set for the following data elements: auc_data,
decision_curve_analyis, model_performance, permutation_vimp,
ice_data, prediction_data and confusion_matrix.
(optional) Numeric value for the level at which
confidence intervals are determined. In the case bootstraps are used to
determine the confidence intervals bootstrap estimation, familiar uses the
rule of thumb \(n = 20 / ci.level\) to determine the number of required
bootstraps.
The default value is 0.95.
Number of indentation steps for messages shown during computation and extraction of various data elements.
Flag to indicate whether feedback should be provided on the computation and extraction of various data elements.
Unused arguments.