mergeMeltImportanceCV: mergeMeltImportanceCV
In predomics/predomicspkg: Interpretable Prediction in Omics Data

mergeMeltImportanceCV

R Documentation

mergeMeltImportanceCV

Description

mergeMeltImportanceCV returns a list of data frames that contain the feature importance of the different learners without any focus on sparsity.

Usage

mergeMeltImportanceCV(
  list.results,
  filter.cv.prev = 0.5,
  min.kfold.nb = FALSE,
  type = "mda",
  learner.grep.pattern = "*",
  nb.top.features = 25,
  feature.selection = NULL,
  fixed.order = FALSE,
  scaled.importance = TRUE,
  make.plot = TRUE,
  main = FALSE,
  cv.prevalence = TRUE
)

Arguments

`list.results.digest:`	a list of digest objects one for each learner used. For example, list(res.terda.digest, res.terga.digest, res.terbeam.digest)
`filter.cv.prev:`	filter variable for each learner based on the appearence prevalence in the cross validation.
`min.kfold.nb:`	wether we should restrict all experiments in the smallest number of k-folds of a comparative analyses (default = FALSE)
`type:`	the type of importance "mda (mean decreased accuracy)" or "pda (prevalence decreased accuracy)" (default = mda)
`learner.grep.pattern:`	select a subset of learners using a grep pattern (default:"*")
`nb.top.features:`	the number of top features to focus on the plot
`feature.selection:`	the names of the features to be selected (default:NULL)
`fixed.order:`	if the order of features in the plot should follow the feature selection one (default = FALSE)
`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
`make.plot:`	make a plot for all the learners
`main:`	should add the title to the graph for correct alignment (default:FALSE)
`cv.prevalence:`	wether or not to plot the distribution of the prevalence of the feature in the top-models for each k-fold in the graph (default:FALSE)