BGGM (version 2.1.5)

predictability: Predictability: Bayesian Variance Explained (R2)

Description

Compute nodewise predictability or Bayesian variance explained @R2 @gelman_r2_2019BGGM. In the context of GGMs, this method was described in Williams2019;textualBGGM.

Usage

predictability(
  object,
  select = FALSE,
  cred = 0.95,
  BF_cut = 3,
  iter = NULL,
  progress = TRUE,
  ...
)

Value

An object of classes bayes_R2 and metric, including

• scores A list containing the posterior samples of R2. The is one element

  for each node.

Arguments

object

object of class estimate or explore

select

logical. Should the graph be selected ? The default is currently FALSE.

cred

numeric. credible interval between 0 and 1 (default is 0.95) that is used for selecting the graph.

BF_cut

numeric. evidentiary threshold (default is 3).

iter

interger. iterations (posterior samples) used for computing R2.

progress

Logical. Should a progress bar be included (defaults to TRUE) ?

...

currently ignored.

References

Examples

# \donttest{

# data
Y <- ptsd[,1:5]

fit <- estimate(Y, iter = 250, progress = FALSE)

r2 <- predictability(fit, select = TRUE,
                     iter = 250, progress = FALSE)

# summary
r2
# }