Combine CVNs of size 1 — combine

Function to combine single CVNs. Handy, if CVNs are computed in parallel using foreach loop and n_cores = 1

Usage

combine_cvn(cvn_list, minimal = TRUE)

Arguments

cvn_list: a list of CVNs, each of size 1 (one lambda/gamma pair)
minimal: logical; stripped CVN (default = TRUE)

Value

a CVN of size length(cvn_list)

Examples

data(grid)

#' Choice of the weight matrix W. Each of 2 covariates has 3 categories
#' (uniform random)
W_grid <- create_weight_matrix("grid", k = 3, l = 3)

fit1 <- CVN(data = grid, W = W_grid, lambda1 = 1, lambda2 = 0.5, 
            n_cores = 1, eps = 1e-2, maxiter = 200, minimal = TRUE)
#> Estimating a CVN with 9 graphs...
#> 
#> Number of cores: 1
#> Uses a warmstart...
#> 
#> -------------------------
#> iteration 1  |  2.180956
#> iteration 2  |  0.115992
#> iteration 3  |  0.085701
#> iteration 4  |  0.032432
#> iteration 5  |  0.027966
#> iteration 6  |  0.014144
#> iteration 7  |  0.011243
#> iteration 8  |  0.008368
            
fit2 <- CVN(data = grid, W = W_grid, lambda1 = 2, lambda2 = 0.75, 
            gamma_ebic = 0.75,
            n_cores = 1, eps = 1e-2, maxiter = 200, minimal = TRUE)
#> Estimating a CVN with 9 graphs...
#> 
#> Number of cores: 1
#> Uses a warmstart...
#> 
#> -------------------------
#> iteration 1  |  1.771692
#> iteration 2  |  0.153946
#> iteration 3  |  0.092411
#> iteration 4  |  0.072031
#> iteration 5  |  0.057845
#> iteration 6  |  0.029927
#> iteration 7  |  0.018960
#> iteration 8  |  0.019064
#> iteration 9  |  0.013124
#> iteration 10  |  0.011946
#> -------------------------
#> iteration 11  |  0.009990
            
(fit <- combine_cvn(list(fit1, fit2)))            
#> Warning: gamma for ebic NOT equal in all CVNs!
#> Covariate-varying Network (CVN)
#> 
#> ✓ all converged
#> 
#> Number of graphs (m)    : 9
#> Number of variables (p) : 10
#> Number of lambda pairs  : 2
#> 
#> Weight matrix (W):
#> 9 x 9 sparse Matrix of class "dsCMatrix"
#>                        
#>  [1,] . 1 . 1 . . . . .
#>  [2,] 1 . 1 . 1 . . . .
#>  [3,] . 1 . . . 1 . . .
#>  [4,] 1 . . . 1 . 1 . .
#>  [5,] . 1 . 1 . 1 . 1 .
#>  [6,] . . 1 . 1 . . . 1
#>  [7,] . . . 1 . . . 1 .
#>  [8,] . . . . 1 . 1 . 1
#>  [9,] . . . . . 1 . 1 .
#> 
#>   id lambda1 lambda2      gamma1      gamma2 converged       value n_iterations
#> 2  1       2    0.75 0.004938272 0.000462963      TRUE 0.009989568           12
#> 1  2       1    0.50 0.002469136 0.000308642      TRUE 0.008368467            9
#>        aic      bic     ebic edges_median edges_iqr gamma_ebic
#> 2 13891.32 15204.33 18685.83           28         2       0.75
#> 1 14408.49 15903.86 18547.23           32         0       0.50