sharp (version 1.4.7)

SelectionPerformanceGraph: Graph representation of selection performance

Description

Generates an igraph object representing the True Positive, False Positive and False Negative edges by comparing the set of selected edges to the set of true edges. This function can only be used in simulation studies (i.e. when the true model is known).

Usage

SelectionPerformanceGraph(
  theta,
  theta_star,
  col = c("tomato", "forestgreen", "navy"),
  lty = c(2, 3, 1),
  node_colour = NULL,
  show_labels = TRUE,
  ...
)

Value

An igraph object.

Arguments

theta

binary adjacency matrix or output of GraphicalModel, VariableSelection, or BiSelection.

theta_star

true binary adjacency matrix or output of SimulateGraphical or SimulateRegression.

col

vector of edge colours. The first entry of the vector defines the colour of False Positive edges, second entry is for True Negatives and third entry is for True Positives.

lty

vector of line types for edges. The order is defined as for argument col.

node_colour

optional vector of node colours. This vector must contain as many entries as there are rows/columns in the adjacency matrix and must be in the same order (the order is used to assign colours to nodes). Integers, named colours or RGB values can be used.

show_labels

logical indicating if the node labels should be displayed.

...

additional arguments to be passed to Graph.

See Also

SimulateGraphical, SimulateRegression, GraphicalModel, VariableSelection, BiSelection

Other functions for model performance: ClusteringPerformance(), SelectionPerformance()

Examples

# \donttest{
# Data simulation
set.seed(1)
simul <- SimulateGraphical(pk = 30)

# Stability selection
stab <- GraphicalModel(xdata = simul$data, K = 10)

# Performance graph
perfgraph <- SelectionPerformanceGraph(
  theta = stab,
  theta_star = simul
)
plot(perfgraph)
# }