PLNnetworkfamily
or ZIPLNnetworkfamily
)Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of network fits (either PLNnetworkfamily
or ZIPLNnetworkfamily
)
# S3 method for 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 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 ZIPLNnetworkfamily
plot(
x,
type = c("criteria", "stability", "diagnostic"),
criteria = c("loglik", "pen_loglik", "BIC", "EBIC"),
reverse = FALSE,
log.x = TRUE,
stability = 0.9,
...
)
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).
an R6 object with class PLNnetworkfamily
or ZIPLNnetworkfamily
a character, either "criteria", "stability" or "diagnostic" for the type of plot.
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"
.
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
plot(PLNnetworkfamily)
: Display various outputs associated with a collection of network fits
plot(ZIPLNnetworkfamily)
: Display various outputs associated with a collection of network fits
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)
if (FALSE) {
plot(fits)
}
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