update example

Author: Carol-seven

README.Rmd

@@ -38,8 +38,8 @@ matrix, making them user-friendly and useful for researchers and practitioners.
 
 $$
 \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}_{\Omega \succ 0}
-\left\{ -\log\det(\Omega) + \text{tr}(S\Omega)
-+ \mathcal{P}_{\lambda,\alpha,\gamma}(\Omega) \right\},
+\left\{ -\log\det(\Omega) + \text{tr}(S\Omega) +
+\mathcal{P}_{\lambda,\alpha,\gamma}(\Omega) \right\},
 $$
 $$
 \mathcal{P}_{\lambda,\alpha,\gamma}(\Omega)
@@ -124,19 +124,30 @@ sim <- gen_prec_sbm(p = 30, K = 3,
                     weight.paras = list(c(shape = 20, rate = 10),
                                         c(min = 0, max = 5)),
                     cond.target = 100)
+## ground truth visualization
+plot(sim)
 
-## synthetic data
+## n-by-p data matrix
 library(MASS)
 X <- mvrnorm(n = 20, mu = rep(0, 30), Sigma = sim$Sigma)
 
-## solution
-res <- grasps(X = X, membership = sim$membership, penalty = "adapt", crit = "HBIC")
+## precision matrix: adaptive lasso; BIC
+prec <- grasps(X = X, membership = sim$membership, penalty = "adapt", crit = "BIC")
 
-## visualization
-plot(res)
+## precision matrix visualization
+plot(prec)
 
 ## performance
-performance(hatOmega = res$hatOmega, Omega = sim$Omega)
+performance(hatOmega = prec$hatOmega, Omega = sim$Omega)
+
+## 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)
 ```
 
 

README.md

@@ -17,8 +17,8 @@ matrix, making them user-friendly and useful for researchers and practitioners.
 
 $$
 \hat{\Omega}(\lambda,\alpha,\gamma) = {\arg\min}_{\Omega \succ 0}
-\left\{ -\log\det(\Omega) + \text{tr}(S\Omega)
-+ \mathcal{P}_{\lambda,\alpha,\gamma}(\Omega) \right\},
+\left\{ -\log\det(\Omega) + \text{tr}(S\Omega) +
+\mathcal{P}_{\lambda,\alpha,\gamma}(\Omega) \right\},
 $$
 $$
 \mathcal{P}_{\lambda,\alpha,\gamma}(\Omega)
@@ -90,40 +90,58 @@ sim <- gen_prec_sbm(p = 30, K = 3,
                     weight.paras = list(c(shape = 20, rate = 10),
                                         c(min = 0, max = 5)),
                     cond.target = 100)
+## ground truth visualization
+plot(sim)
+```
+
+<img src="man/figures/README-unnamed-chunk-2-1.png" alt="" width="100%" />
 
-## synthetic data
+``` r
+
+## n-by-p data matrix
 library(MASS)
 X <- mvrnorm(n = 20, mu = rep(0, 30), Sigma = sim$Sigma)
 
-## solution
-res <- grasps(X = X, membership = sim$membership, penalty = "adapt", crit = "HBIC")
+## precision matrix: adaptive lasso; BIC
+prec <- grasps(X = X, membership = sim$membership, penalty = "adapt", crit = "BIC")
 
-## visualization
-plot(res)
+## precision matrix visualization
+plot(prec)
 ```
 
-<img src="man/figures/README-unnamed-chunk-2-1.png" alt="" width="100%" />
+<img src="man/figures/README-unnamed-chunk-2-2.png" alt="" width="100%" />
 
 ``` r
 
 ## performance
-performance(hatOmega = res$hatOmega, Omega = sim$Omega)
+performance(hatOmega = prec$hatOmega, Omega = sim$Omega)
 #>      measure    value
-#> 1   sparsity   0.9103
-#> 2  Frobenius  24.6557
-#> 3         KL   7.2100
-#> 4  quadratic  54.4284
-#> 5   spectral  13.1234
-#> 6         TP  22.0000
-#> 7         TN 370.0000
-#> 8         FP  17.0000
-#> 9         FN  26.0000
-#> 10       TPR   0.4583
-#> 11       FPR   0.0439
-#> 12        F1   0.5057
-#> 13       MCC   0.4545
+#> 1   sparsity   0.8805
+#> 2  Frobenius  23.9013
+#> 3         KL   7.6775
+#> 4  quadratic  69.1639
+#> 5   spectral  12.3571
+#> 6         TP  23.0000
+#> 7         TN 358.0000
+#> 8         FP  29.0000
+#> 9         FN  25.0000
+#> 10       TPR   0.4792
+#> 11       FPR   0.0749
+#> 12        F1   0.4600
+#> 13       MCC   0.3904
+
+## 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)
 ```
 
+<img src="man/figures/README-unnamed-chunk-2-3.png" alt="" width="100%" />
+
 ## Reference
 
 <div id="refs" class="references csl-bib-body hanging-indent">

inst/example/ex-grasps.R

@@ -10,7 +10,7 @@ sim <- gen_prec_sbm(p = 30, K = 3,
                     weight.paras = list(c(shape = 20, rate = 10),
                                         c(min = 0, max = 5)),
                     cond.target = 100)
-## visualization
+## ground truth visualization
 plot(sim)
 
 ## n-by-p data matrix