`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 = 7.54443
sparsifying penalty = 6.96857
sparsifying penalty = 6.436665
sparsifying penalty = 5.94536
sparsifying penalty = 5.491556
sparsifying penalty = 5.07239
sparsifying penalty = 4.685219
sparsifying penalty = 4.3276
sparsifying penalty = 3.997278
sparsifying penalty = 3.692169
sparsifying penalty = 3.410349
sparsifying penalty = 3.15004
sparsifying penalty = 2.9096
sparsifying penalty = 2.687513
sparsifying penalty = 2.482377
sparsifying penalty = 2.2929
sparsifying penalty = 2.117885
sparsifying penalty = 1.956228
sparsifying penalty = 1.806911
sparsifying penalty = 1.668991
sparsifying penalty = 1.541598
sparsifying penalty = 1.42393
sparsifying penalty = 1.315242
sparsifying penalty = 1.214851
sparsifying penalty = 1.122122
sparsifying penalty = 1.036472
sparsifying penalty = 0.9573588
sparsifying penalty = 0.8842844
sparsifying penalty = 0.8167877
sparsifying penalty = 0.754443
#> Post-treatments
#> DONE!
myNet <- getBestModel(fits)
if (FALSE) {
plot(myNet)
}
```