`PLNnetworkfit`

object`R/PLNnetworkfit-S3methods.R`

`plot.PLNnetworkfit.Rd`

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

object

- 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).

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)

```
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 = 7.264447
sparsifying penalty = 6.709958
sparsifying penalty = 6.197792
sparsifying penalty = 5.72472
sparsifying penalty = 5.287757
sparsifying penalty = 4.884147
sparsifying penalty = 4.511344
sparsifying penalty = 4.166997
sparsifying penalty = 3.848934
sparsifying penalty = 3.555148
sparsifying penalty = 3.283787
sparsifying penalty = 3.033138
sparsifying penalty = 2.801621
sparsifying penalty = 2.587776
sparsifying penalty = 2.390253
sparsifying penalty = 2.207807
sparsifying penalty = 2.039287
sparsifying penalty = 1.88363
sparsifying penalty = 1.739854
sparsifying penalty = 1.607053
sparsifying penalty = 1.484388
sparsifying penalty = 1.371086
sparsifying penalty = 1.266432
sparsifying penalty = 1.169766
sparsifying penalty = 1.080479
sparsifying penalty = 0.998007
sparsifying penalty = 0.9218299
sparsifying penalty = 0.8514675
sparsifying penalty = 0.7864757
sparsifying penalty = 0.7264447
#> Post-treatments
#> DONE!
myNet <- getBestModel(fits)
if (FALSE) {
plot(myNet)
}
```