fusemlr {fuseMLR} | R Documentation |
fusemlr
Description
Trains the Training object passed as argument. A training object must contain
the training layers and a training meta-layer. A training layer encapsulates
data modalities, a variable selection method and a learner. Use the function
createTraining to create a training object, createTrainLayer to add training
layers to the created training object, and createTrainMetaLayer to add a meta-layer
with the corresponding meta-learner to the training object. The function fusemlr
is designed to train all training layers and the meta-learner. After training
the layer-specific base models and the meta-model will be stored in the training
object which can be used for predictions.
Usage
fusemlr(
training,
ind_subset = NULL,
use_var_sel = FALSE,
resampling_method = NULL,
resampling_arg = list(),
seed = NULL
)
Arguments
training |
|
ind_subset |
|
use_var_sel |
|
resampling_method |
|
resampling_arg |
|
seed |
|
Value
The current object is returned, with each learner trained on each layer.
References
Fouodo C.J.K, Bleskina M. and Szymczak S. (2024). fuseMLR: An R package for integrative prediction modeling of multi-omics data, paper submitted.
See Also
createTrainLayer, createTrainMetaLayer, extractModel and extractData.