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Graph Selection for var.estimate Object

Source: R/select.VAR_estimate.R
select.var_estimate.Rd

Graph Selection for var.estimate Object

Usage

# S3 method for class 'var_estimate'
select(object, cred = 0.95, alternative = "two.sided", ...)

Arguments

object

An object of class VAR.estimate.

cred

Numeric. The credible interval width for selecting the graph (defaults to 0.95; must be between 0 and 1).

alternative

A character string specifying the alternative hypothesis. It must be one of "two.sided" (default), "greater" or "less". See note for futher details.

...

Currently ignored.

Value

An object of class select.var_estimate, including

  • pcor_adj Adjacency matrix for the partial correlations.

  • beta_adj Adjacency matrix for the regression coefficients.

  • pcor_weighted_adj Weighted adjacency matrix for the partial correlations.

  • beta_weighted_adj Weighted adjacency matrix for the regression coefficients.

  • pcor_mu Partial correlation matrix (posterior mean).

  • beta_mu A matrix including the regression coefficients (posterior mean).

Examples

# \donttest{
# data
Y <- subset(ifit, id == 1)[,-1]

# fit model with alias (var_estimate also works)
fit <- var_estimate(Y, progress = FALSE)

# select graphs
select(fit, cred = 0.95)
#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Vector Autoregressive Model (VAR) 
#> --- 
#> Posterior Samples: 5000 
#> Credible Interval: 95 % 
#> --- 
#> Call: 
#> var_estimate(Y = Y, progress = FALSE)
#> --- 
#> Partial Correlations: 
#> 
#>               interested disinterested excited upset strong stressed steps
#> interested         0.000             0   0.381 -0.21  0.326    0.282 0.000
#> disinterested      0.000             0   0.000  0.00  0.000    0.000 0.000
#> excited            0.381             0   0.000  0.00  0.495    0.000 0.000
#> upset             -0.210             0   0.000  0.00  0.000    0.340 0.000
#> strong             0.326             0   0.495  0.00  0.000    0.000 0.193
#> stressed           0.282             0   0.000  0.34  0.000    0.000 0.000
#> steps              0.000             0   0.000  0.00  0.193    0.000 0.000
#> --- 
#> Coefficients: 
#> 
#>                  interested disinterested excited upset strong stressed steps
#> interested.l1             0             0       0 0.000      0    0.000     0
#> disinterested.l1          0             0       0 0.000      0    0.000     0
#> excited.l1                0             0       0 0.000      0    0.000     0
#> upset.l1                  0             0       0 0.428      0    0.316     0
#> strong.l1                 0             0       0 0.000      0    0.000     0
#> stressed.l1               0             0       0 0.000      0    0.000     0
#> steps.l1                  0             0       0 0.000      0    0.000     0
#> --- 

# }