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

Usage

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

Arguments

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

Value

plots a graph