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 Networkfamily
, 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 = 7.264447
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!
head(coefficient_path(fits))
#> Node1 Node2 Coeff Penalty Edge
#> 1 Aga Che 0 7.264447 Aga|Che
#> 2 Ath Che 0 7.264447 Ath|Che
#> 3 Cea Che 0 7.264447 Cea|Che
#> 4 Ced Che 0 7.264447 Ced|Che
#> 5 All Che 0 7.264447 All|Che
#> 6 Che Hyc 0 7.264447 Che|Hyc