3. Graphical VAR
library(idiographic)
data(srl)
vars <- c("efficacy", "value", "planning", "monitoring", "effort")
has_cograph <- requireNamespace("cograph", quietly = TRUE)graphical_var() estimates sparse
temporal and contemporaneous networks with lasso regularization and EBIC
model selection. It is the closest analogue to the chapter’s graphical
VAR section, and the regularized counterpart to the OLS baseline in the
Ordinary VAR vignette.
Fit one person
gvar_fit <- graphical_var(srl, vars = vars, id = "name", subject = "Grace",
n_lambda = 12, gamma = 0)
gvar_fit
#> Graphical VAR Result
#> Variables: 5 (efficacy, value, planning, monitoring, effort)
#> Observations: 155
#> Temporal edges: 0 / 25
#> Contemp edges: 3 / 10
#> EBIC: 698.29 (gamma=0.00)
#> Lambda: beta=0.2695, kappa=0.1534
#>
#> Temporal [directed]
#> no non-zero edges
#> efficacy value planning monitoring effort
#> efficacy 0 0 0 0 0
#> value 0 0 0 0 0
#> planning 0 0 0 0 0
#> monitoring 0 0 0 0 0
#> effort 0 0 0 0 0
#>
#> Contemporaneous [undirected]
#> weights [0.184, 0.286] | +3 / -0 edges
#> efficacy value planning monitoring effort
#> efficacy 0.00 0 0.00 0.26 0.00
#> value 0.00 0 0.00 0.00 0.00
#> planning 0.00 0 0.00 0.00 0.18
#> monitoring 0.26 0 0.00 0.00 0.29
#> effort 0.00 0 0.18 0.29 0.00
#>
#> plot(x) | plot(x, layer = "temporal")
#> edges(x) | nodes(x) | summary(x) | coefs(x) | matrices(x)The printout reports the selected EBIC, gamma, and the
two tuning parameters, then shows the sparse temporal and
contemporaneous networks. Many cells are exactly zero — that sparsity is
the point of the method.
Tidy tables
head(edges(gvar_fit))
#> network from to weight
#> 1 contemporaneous monitoring effort 0.2861771
#> 2 contemporaneous efficacy monitoring 0.2585143
#> 3 contemporaneous planning effort 0.1838825
nodes(gvar_fit)
#> network node strength out_strength in_strength self
#> 1 temporal efficacy 0.0000000 0 0 0
#> 2 temporal value 0.0000000 0 0 0
#> 3 temporal planning 0.0000000 0 0 0
#> 4 temporal monitoring 0.0000000 0 0 0
#> 5 temporal effort 0.0000000 0 0 0
#> 6 contemporaneous efficacy 0.2585143 NA NA 0
#> 7 contemporaneous value 0.0000000 NA NA 0
#> 8 contemporaneous planning 0.1838825 NA NA 0
#> 9 contemporaneous monitoring 0.5446914 NA NA 0
#> 10 contemporaneous effort 0.4700596 NA NA 0
summary(gvar_fit)
#> network n_nodes n_edges density mean_abs_weight n_positive n_negative
#> 1 temporal 5 0 0.0 0.000000 0 0
#> 2 contemporaneous 5 3 0.3 0.242858 3 0matrices(gvar_fit)
#>
#> $beta
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> efficacy -0.003 0 0 0 0 0
#> value 0.008 0 0 0 0 0
#> planning -0.005 0 0 0 0 0
#> monitoring 0.000 0 0 0 0 0
#> effort 0.010 0 0 0 0 0
#>
#> $temporal
#> efficacy value planning monitoring effort
#> efficacy 0 0 0 0 0
#> value 0 0 0 0 0
#> planning 0 0 0 0 0
#> monitoring 0 0 0 0 0
#> effort 0 0 0 0 0
#>
#> $kappa
#> efficacy value planning monitoring effort
#> efficacy 1.080 0.000 0.000 -0.292 0.000
#> value 0.000 1.011 0.000 0.000 0.000
#> planning 0.000 0.000 1.043 0.000 -0.202
#> monitoring -0.292 0.000 0.000 1.179 -0.334
#> effort 0.000 0.000 -0.202 -0.334 1.157
#>
#> $PCC
#> efficacy value planning monitoring effort
#> efficacy 0.000 0 0.000 0.259 0.000
#> value 0.000 0 0.000 0.000 0.000
#> planning 0.000 0 0.000 0.000 0.184
#> monitoring 0.259 0 0.000 0.000 0.286
#> effort 0.000 0 0.184 0.286 0.000
#>
#> $PDC
#> efficacy value planning monitoring effort
#> efficacy 0 0 0 0 0
#> value 0 0 0 0 0
#> planning 0 0 0 0 0
#> monitoring 0 0 0 0 0
#> effort 0 0 0 0 0
