Plot Function for S3 Class "adjmat"

Visualize an adjacency matrix as a heatmap. This function is shared by objects returned from prec_to_adj.

Usage

# S3 method for class 'adjmat'
plot(x, ...)

Arguments

x

An object inheriting from S3 class "adjmat", typically returned by prec_to_adj.

...

Additional arguments passed to ggplot.

Value

A heatmap of class ggplot showing the matrix entries. The plot title also reports matrix dimension and sparsity.

Examples

library(grasps)

## reproducibility for everything
set.seed(1234)

## block-structured precision matrix based on SBM
sim <- gen_prec_sbm(p = 30, K = 3,
                    within.prob = 0.25, between.prob = 0.05,
                    weight.dists = list("gamma", "unif"),
                    weight.paras = list(c(shape = 20, rate = 10),
                                        c(min = 0, max = 5)),
                    cond.target = 100)
## ground truth visualization
plot(sim)


## n-by-p data matrix
library(MASS)
X <- mvrnorm(n = 20, mu = rep(0, 30), Sigma = sim$Sigma)

## precision matrix: adaptive lasso; BIC
prec <- grasps(X = X, membership = sim$membership, penalty = "adapt", crit = "BIC")

## precision matrix visualization
plot(prec)


## performance
performance(hatOmega = prec$hatOmega, Omega = sim$Omega)
#>      measure    value
#> 1   sparsity   0.7862
#> 2  Frobenius  34.8430
#> 3         KL  12.5488
#> 4  quadratic 161.4326
#> 5   spectral  19.8343
#> 6         TP  24.0000
#> 7         TN 318.0000
#> 8         FP  69.0000
#> 9         FN  24.0000
#> 10       TPR   0.5000
#> 11       FPR   0.1783
#> 12        F1   0.3404
#> 13       MCC   0.2459

## adjacency matrix: diagonal = 0; raw partial correlations;
##                   no thresholding; weighted network
adj <- prec_to_adj(prec$hatOmega,
                   diag.zero = TRUE, absolute = FALSE,
                   threshold = NULL, weighted = TRUE)

## adjacency matrix visualization
plot(adj)