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 = 4.479926 
	sparsifying penalty = 4.137977 
	sparsifying penalty = 3.822129 
	sparsifying penalty = 3.530389 
	sparsifying penalty = 3.260917 
	sparsifying penalty = 3.012014 
	sparsifying penalty = 2.78211 
	sparsifying penalty = 2.569754 
	sparsifying penalty = 2.373607 
	sparsifying penalty = 2.192431 
	sparsifying penalty = 2.025085 
	sparsifying penalty = 1.870512 
	sparsifying penalty = 1.727737 
	sparsifying penalty = 1.595861 
	sparsifying penalty = 1.47405 
	sparsifying penalty = 1.361537 
	sparsifying penalty = 1.257612 
	sparsifying penalty = 1.16162 
	sparsifying penalty = 1.072954 
	sparsifying penalty = 0.9910565 
	sparsifying penalty = 0.91541 
	sparsifying penalty = 0.8455375 
	sparsifying penalty = 0.7809984 
	sparsifying penalty = 0.7213854 
	sparsifying penalty = 0.6663227 
	sparsifying penalty = 0.6154629 
	sparsifying penalty = 0.5684851 
	sparsifying penalty = 0.5250931 
	sparsifying penalty = 0.4850132 
	sparsifying penalty = 0.4479926 

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