Get objects needed for a merged visualization task combining different experiments from different datasets (different X and y)

Here we get the 4 datasets from a given prediction experiment (clf object + X + y) needed for subsequent combination with other predition experiments for combined visualization (feature prevalence in FBM + feature importance + featureEffSizes + feature prevalence in groups)

Usage

getImportanceFeaturesFBMobjects(
  clf_res,
  X,
  y,
  verbose = TRUE,
  filter.cv.prev = 0.25,
  scaled.importance = FALSE,
  k_penalty = 0.75/100,
  k_max = 0
)

Arguments

clf_res

The result of a single experiment

X

The feature table used as input of fit function behind experiments in clf_res

y

The target class (binary/continuous)

verbose

print out informaiton

filter.cv.prev

keep only features found in at least (default: 0.25, i.e 25 percent) of the cross validation experiments

scaled.importance

the scaled importance is the importance multipied by the prevalence in the folds. If (default = TRUE) this will be used, the mean mda will be scaled by the prevalence of the feature in the folds and ordered subsequently

k_penalty

the sparsity penalty needed to select the best models of the population (default:0.75/100).

k_max

select the best population below a given threshold. If (default:0) no selection is performed.

Value

list of objects for subsequent combination