Print cross-validated eigenvalues

Usage

# S3 method for eigcv
print(x, ...)

Arguments

x: An eigcv object created by a call to eigcv().
...: Ignored.

Value

x, but invisibly.

Examples


library(fastRG)

set.seed(27)

B <- matrix(0.1, 5, 5)
diag(B) <- 0.3

model <- sbm(
  n = 1000,
  k = 5,
  B = B,
  expected_degree = 40,
  poisson_edges = FALSE,
  allow_self_loops = FALSE
)

A <- sample_sparse(model)

eigs<- eigcv(A, k_max = 10)
eigs
#> Estimated graph dimension:	 5
#> 
#> Number of bootstraps:		 10
#> Edge splitting probabaility:	 0.1
#> Significance level:		 0.05
#> 
#>  ------------ Summary of Tests ------------
#>   k          z        pvals         padj
#>   1 60.0858888 0.000000e+00 0.000000e+00
#>   2 11.7538714 3.372802e-32 3.372802e-32
#>   3 11.1552401 3.375515e-29 3.375515e-29
#>   4 11.3242906 4.974047e-30 4.974047e-30
#>   5  9.5379830 7.281856e-22 7.281856e-22
#>   6 -1.1633387 8.776540e-01 8.776540e-01
#>   7 -1.2996582 9.031409e-01 9.031409e-01
#>   8 -1.1750915 8.800209e-01 8.800209e-01
#>   9 -1.1354378 8.719040e-01 8.719040e-01
#>  10 -0.8694766 8.077067e-01 8.077067e-01
#> 

plot(eigs, type = "z-score")    # default

plot(eigs, type = "adjacency")

plot(eigs, type = "laplacian")