PLNnetworkfit
objectR/PLNnetworkfit-S3methods.R
plot.PLNnetworkfit.Rd
Extract and plot the network (partial correlation, support or inverse covariance) from a PLNnetworkfit
object
an R6 object with class PLNnetworkfit
character. Value of the weight of the edges in the network, either "partial_cor" (partial correlation) or "support" (binary). Default is "partial_cor"
.
the type of output used: either 'igraph' or 'corrplot'. Default is 'igraph'
.
Length 2 color vector. Color for positive/negative edges. Default is c("#F8766D", "#00BFC4")
. Only relevant for igraph output.
if TRUE
, isolated node are remove before plotting. Only relevant for igraph output.
vector of character. The labels of the nodes. The default will use the column names ot the response matrix.
an optional igraph layout. Only relevant for igraph output.
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 = 7.541317
sparsifying penalty = 6.965695
sparsifying penalty = 6.43401
sparsifying penalty = 5.942907
sparsifying penalty = 5.48929
sparsifying penalty = 5.070297
sparsifying penalty = 4.683286
sparsifying penalty = 4.325815
sparsifying penalty = 3.995629
sparsifying penalty = 3.690646
sparsifying penalty = 3.408942
sparsifying penalty = 3.148741
sparsifying penalty = 2.9084
sparsifying penalty = 2.686404
sparsifying penalty = 2.481353
sparsifying penalty = 2.291954
sparsifying penalty = 2.117011
sparsifying penalty = 1.955421
sparsifying penalty = 1.806166
sparsifying penalty = 1.668303
sparsifying penalty = 1.540962
sparsifying penalty = 1.423342
sparsifying penalty = 1.3147
sparsifying penalty = 1.21435
sparsifying penalty = 1.121659
sparsifying penalty = 1.036044
sparsifying penalty = 0.9569638
sparsifying penalty = 0.8839195
sparsifying penalty = 0.8164507
sparsifying penalty = 0.7541317
#> Post-treatments
#> DONE!
myNet <- getBestModel(fits)
if (FALSE) {
plot(myNet)
}