PLNnetworkfamily
)R/PLNnetworkfamily-S3methods.R
plot.PLNnetworkfamily.Rd
Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of PLNnetwork fits (a PLNnetworkfamily
)
an R6 object with class PLNnetworkfamily
a character, either "criteria", "stability" or "diagnostic" for the type of plot.
vector of characters. The criteria to plot in c("loglik", "BIC", "ICL", "R_squared", "EBIC", "pen_loglik").
Default is c("loglik", "pen_loglik", "BIC", "EBIC"). Only relevant when type = "criteria"
.
A logical indicating whether to plot the value of the criteria in the "natural" direction (loglik - 0.5 penalty) or in the "reverse" direction (-2 loglik + penalty). Default to FALSE, i.e use the natural direction, on the same scale as the log-likelihood.
logical: should the x-axis be represented in log-scale? Default is TRUE
.
scalar: the targeted level of stability in stability plot. Default is .9.
additional parameters for S3 compatibility. Not used
Produces either a diagnostic plot (with type = 'diagnostic'
), a stability plot
(with type = 'stability'
) or the evolution of the criteria of the different models considered
(with type = 'criteria'
, the default).
The BIC and ICL criteria have the form 'loglik - 1/2 * penalty'
so that they are on the same scale as the model log-likelihood. You can change this direction and use the alternate form '-2*loglik + penalty', as some authors do, by setting reverse = TRUE
.
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!
if (FALSE) {
plot(fits)
}