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 = 7.541317
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!
head(coefficient_path(fits))
#> Node1 Node2 Coeff Penalty Edge
#> 1 Aga Che 0 7.541317 Aga|Che
#> 2 Ath Che 0 7.541317 Ath|Che
#> 3 Cea Che 0 7.541317 Cea|Che
#> 4 Ced Che 0 7.541317 Ced|Che
#> 5 All Che 0 7.541317 All|Che
#> 6 Che Hyc 0 7.541317 Che|Hyc