makeFeatureModelPrevalenceNetworkMiic: Prints as text the detail on a given experiment along with...
In predomics/predomicspkg: Interpretable Prediction in Omics Data

makeFeatureModelPrevalenceNetworkMiic

R Documentation

Prints as text the detail on a given experiment along with summarized results (if computed)

This function will use the miic package to compute the co-occurance of features in a population of models

makeFeatureModelPrevalenceNetworkMiic(
  pop.noz,
  feature.annot,
  cor.th = 0.3,
  verbose = TRUE,
  layout = "circlular"
)

`pop.noz:`	a data.frame of in features in the rows and models in the columns. This table contains the feature coefficients in the models and is obtained by makeFeatureAnnot()
`feature.annot:`	a data frame with annotation on features obtained by makeFeatureAnnot()
`cor.th:`	a threshold abtained on the partial correlation value
`verbose:`	print out information during run
`layout:`	the network layout by default is circular (layout_in_circle) and will be a weighted Fruchterman-Reingold otherwise