R/PLNnetworkfamily-S3methods.R
coefficient_path.Rd
Extract the regularization path of a PLNnetwork fit
coefficient_path(Robject, precision = TRUE, corr = TRUE)
an object with class PLNnetworkfamily
, i.e. an output from PLNnetwork()
a logical, should the coefficients of the precision matrix Omega or the covariance matrix Sigma be sent back. Default is TRUE
.
a logical, should the correlation (partial in case precision = TRUE
) be sent back. Default is TRUE
.
Sends back a tibble/data.frame.
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!
head(coefficient_path(fits))
#> Node1 Node2 Coeff Penalty Edge
#> 1 Aga Che 0 7.54443 Aga|Che
#> 2 Ath Che 0 7.54443 Ath|Che
#> 3 Cea Che 0 7.54443 Cea|Che
#> 4 Ced Che 0 7.54443 Ced|Che
#> 5 All Che 0 7.54443 All|Che
#> 6 Che Hyc 0 7.54443 Che|Hyc