Extract and plot the network (partial correlation, support or inverse covariance) from a PLNnetworkfit object

Extract and plot the network (partial correlation, support or inverse covariance) from a PLNnetworkfit object

Usage

# S3 method for class 'PLNnetworkfit'
plot(
  x,
  type = c("partial_cor", "support"),
  output = c("igraph", "corrplot"),
  edge.color = c("#F8766D", "#00BFC4"),
  remove.isolated = FALSE,
  node.labels = NULL,
  layout = layout_in_circle,
  plot = TRUE,
  ...
)

Arguments

x

an R6 object with class PLNnetworkfit

type

character. Value of the weight of the edges in the network, either "partial_cor" (partial correlation) or "support" (binary). Default is "partial_cor".

output

the type of output used: either 'igraph' or 'corrplot'. Default is 'igraph'.

edge.color

Length 2 color vector. Color for positive/negative edges. Default is c("#F8766D", "#00BFC4"). Only relevant for igraph output.

remove.isolated

if TRUE, isolated node are remove before plotting. Only relevant for igraph output.

node.labels

vector of character. The labels of the nodes. The default will use the column names ot the response matrix.

layout

an optional igraph layout. Only relevant for igraph output.

plot

logical. Should the final network be displayed or only sent back to the user. Default is TRUE.

...

Not used (S3 compatibility).

Value

Send back an invisible object (igraph or Matrix, depending on the output chosen) and optionally displays a graph (via igraph or corrplot for large ones)

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)
#> 
#>  Initialization...
#>  Adjusting 30 PLN with sparse inverse covariance estimation
#> 	Joint optimization alternating gradient descent and graphical-lasso
#> 	sparsifying penalty = 4.479926 
	sparsifying penalty = 4.137977 
	sparsifying penalty = 3.822129 
	sparsifying penalty = 3.530389 
	sparsifying penalty = 3.260917 
	sparsifying penalty = 3.012014 
	sparsifying penalty = 2.78211 
	sparsifying penalty = 2.569754 
	sparsifying penalty = 2.373607 
	sparsifying penalty = 2.192431 
	sparsifying penalty = 2.025085 
	sparsifying penalty = 1.870512 
	sparsifying penalty = 1.727737 
	sparsifying penalty = 1.595861 
	sparsifying penalty = 1.47405 
	sparsifying penalty = 1.361537 
	sparsifying penalty = 1.257612 
	sparsifying penalty = 1.16162 
	sparsifying penalty = 1.072954 
	sparsifying penalty = 0.9910565 
	sparsifying penalty = 0.91541 
	sparsifying penalty = 0.8455375 
	sparsifying penalty = 0.7809984 
	sparsifying penalty = 0.7213854 
	sparsifying penalty = 0.6663227 
	sparsifying penalty = 0.6154629 
	sparsifying penalty = 0.5684851 
	sparsifying penalty = 0.5250931 
	sparsifying penalty = 0.4850132 
	sparsifying penalty = 0.4479926 

#>  Post-treatments
#>  DONE!
myNet <- getBestModel(fits)
if (FALSE) { # \dontrun{
plot(myNet)
} # }