Visualize a summary of an experiment/set of experiments
analyzeImportanceFeaturesFBM.RdVisualization of 4 panels corresponding to feature prevalence in FBM, feature importance, feature prevalence in groups, effect sizes of feature abundances vs y-variable (cliff's delta for binary y; spearman rho for continuous y). Can be applied to single classification task or to multiple classification tasks carried out on the same X-y dataset
Usage
analyzeImportanceFeaturesFBM(
clf_res,
X,
y,
makeplot = TRUE,
saveplotobj = TRUE,
name = "",
verbose = TRUE,
pdf.dims = c(width = 25, height = 20),
filter.cv.prev = 0.25,
nb.top.features = 100,
scaled.importance = FALSE,
k_penalty = 0.75/100,
k_max = 0
)Arguments
- clf_res
The result of an experiment or multiple experiments (list of experiments)
- X
The feature table used as input of fit function behind experiments in clf_res
- y
The target class (binary/continuous)
- makeplot
make a pdf file with the resulting plots (default:TRUE)
- saveplotobj
make a .Rda file with a list of the individual plots (default:TRUE)
- name
the suffix of the pdf file (default:"")
- verbose
print out informaiton
- pdf.dims
dimensions of the pdf object (default: c(w = 25, h = 20))
- filter.cv.prev
keep only features found in at least (default: 0.25, i.e 25 percent) of the cross validation experiments
- nb.top.features
the maximum number (default: 100) of most important features to be shown. If the number of features in FBM < nb.top.features, the number of features in FBM will be shown instead
- scaled.importance
the scaled importance is the importance multiplied 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.