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Predictability: Bayesian Variance Explained (R2)

Source: R/predictability.R
predictability.Rd

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,
  ...
)

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.

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.

Note

Binary and Ordinal Data:

R2 is computed from the latent data.

Mixed Data:

The mixed data approach is somewhat ad-hoc @see for example p. 277 in @hoff2007extending;textualBGGM. This is becaue uncertainty in the ranks is not incorporated, which means that variance explained is computed from the 'empirical' CDF.

Model Selection:

Currently the default to include all nodes in the model when computing R2. This can be changed (i.e., select = TRUE), which then sets those edges not detected to zero. This is accomplished by subsetting the correlation matrix according to each neighborhood of relations.

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
#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Metric: Bayes R2
#> Type: continuous 
#> --- 
#> Estimates:
#> 
#>  Node Post.mean Post.sd Cred.lb Cred.ub
#>    B1     0.444   0.047   0.354   0.539
#>    B2     0.496   0.047   0.420   0.595
#>    B3     0.559   0.049   0.469   0.653
#>    B4     0.502   0.050   0.421   0.605
#>    B5     0.457   0.046   0.373   0.560
# }