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

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

Usage

# S3 method for class 'ZIPLNfit_sparse'
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,
  edge.alpha = 0.2,
  plot = TRUE,
  ...
)

Arguments

x

an R6 object with class ZIPLNfit_sparse

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.

edge.alpha

opacity of the weakest edge, the strongest one being fully opaque, so that the strength of an edge can be read off a dense network. Default is 0.2. Set it to 1 for uniformly opaque edges. Only relevant for igraph output with type = "partial_cor".

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)
fit <- ZIPLN(Abundance ~ 1, data = trichoptera, control = ZIPLN_param(penalty = 0.1))
#> ℹ The penalty now applies on the correlation scale by default: the penalties
#>   given are taken as such, between 0 and 1. Until version 1.3.2 they were on
#>   the covariance scale.
#> ℹ Set `penalty_scale = "covariance"` in the control parameters to get the
#>   former behavior back, or `penalty_scale = "correlation"` to keep the
#>   penalties as they are, without this message.
#> This message is displayed once per session.
#> 
#>  Initialization...
#>  Adjusting a ZI-PLN model with sparse covariance model and single specific parameter(s) in Zero inflation component.
#>  DONE!
if (FALSE) { # \dontrun{
plot(fit)
} # }