Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of network fits (either PLNnetworkfamily or ZIPLNnetworkfamily)

Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of network fits (either PLNnetworkfamily or ZIPLNnetworkfamily)

Usage

# S3 method for class 'Networkfamily'
plot(
  x,
  type = c("criteria", "stability", "diagnostic"),
  criteria = c("loglik", "pen_loglik", "BIC", "EBIC"),
  reverse = FALSE,
  log.x = TRUE,
  stability = 0.9,
  ...
)

# S3 method for class 'PLNnetworkfamily'
plot(
  x,
  type = c("criteria", "stability", "diagnostic"),
  criteria = c("loglik", "pen_loglik", "BIC", "EBIC"),
  reverse = FALSE,
  log.x = TRUE,
  stability = 0.9,
  ...
)

# S3 method for class 'ZIPLNnetworkfamily'
plot(
  x,
  type = c("criteria", "stability", "diagnostic"),
  criteria = c("loglik", "pen_loglik", "BIC", "EBIC"),
  reverse = FALSE,
  log.x = TRUE,
  stability = 0.9,
  ...
)

Arguments

x: an R6 object with class PLNnetworkfamily or ZIPLNnetworkfamily
type: a character, either "criteria", "stability" or "diagnostic" for the type of plot.
criteria: Vector of criteria to plot, to be selected among "loglik" (log-likelihood), "BIC", "ICL", "R_squared", "EBIC" and "pen_loglik" (penalized log-likelihood). Default is c("loglik", "pen_loglik", "BIC", "EBIC"). Only used when type = "criteria".
reverse: 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.
log.x: logical: should the x-axis be represented in log-scale? Default is TRUE.
stability: scalar: the targeted level of stability in stability plot. Default is .9.
...: additional parameters for S3 compatibility. Not used

Value

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).

Details

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.

Functions

plot(PLNnetworkfamily): Display various outputs associated with a collection of network fits
plot(ZIPLNnetworkfamily): Display various outputs associated with a collection of network fits

Examples

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 = 0.5246191 
	sparsifying penalty = 0.4845754 
	sparsifying penalty = 0.4475881 
	sparsifying penalty = 0.4134241 
	sparsifying penalty = 0.3818678 
	sparsifying penalty = 0.3527202 
	sparsifying penalty = 0.3257973 
	sparsifying penalty = 0.3009295 
	sparsifying penalty = 0.2779598 
	sparsifying penalty = 0.2567434 
	sparsifying penalty = 0.2371464 
	sparsifying penalty = 0.2190452 
	sparsifying penalty = 0.2023257 
	sparsifying penalty = 0.1868823 
	sparsifying penalty = 0.1726178 
	sparsifying penalty = 0.159442 
	sparsifying penalty = 0.1472719 
	sparsifying penalty = 0.1360308 
	sparsifying penalty = 0.1256477 
	sparsifying penalty = 0.1160571 
	sparsifying penalty = 0.1071986 
	sparsifying penalty = 0.09901618 
	sparsifying penalty = 0.09145836 
	sparsifying penalty = 0.08447742 
	sparsifying penalty = 0.07802933 
	sparsifying penalty = 0.07207342 
	sparsifying penalty = 0.06657212 
	sparsifying penalty = 0.06149072 
	sparsifying penalty = 0.05679719 
	sparsifying penalty = 0.05246191 

#>  Post-treatments
#>  DONE!
if (FALSE) { # \dontrun{
plot(fits)
} # }