plots/generating_plots.R

In SvenKlaassen/GGMtest: Hypothesis Testing for Gaussian Graphical Models

rm(list=ls())

library("huge")
library("igraph")
library("GGMtest")

set.seed(42)

# generate data (different graph structures: "random", "hub", "cluster", "band" and "scale-free")
L <- huge.generator(n = 100, d = 10, graph = "cluster", g = 4)
# true Graph
true_graph <- graph_from_adjacency_matrix(as.matrix(L$theta), mode='undirected', diag=F)

plot(true_graph, usearrows = FALSE, label=1:10, displaylabels=T, main = "True Graph",
     layout= layout.fruchterman.reingold, edge.width = 2, edge.color = "black")

# index pairs for inference
edgeset_1 <- matrix(c(1,2,2,3,2,4,1,4,4,5), byrow = T, ncol = 2)

# perform test:
ggm_model <- GGMtest(data = L$data,
                     edges = edgeset_1,
                     alpha = 0.05,
                     nuisance_estimaton = "lasso")

# p-value:
ggm_model$pvalue_max

# plot the confidence intervals (on a subset of edges)
plot_GGMtest(ggm_model,edges = edgeset_1)


L <- huge.generator(n = 2500, d = 25, graph = "random")

# all indices for inference
edgeset_2 <- t(combn(25,2))
ggm_model_2 <- GGMtest(L$data,edgeset_2, alpha = 0.05,nuisance_estimaton ="lasso")

# true Graph
par(mfrow=c(1,3))
graph_layout <- layout.circle
#graph_layout <- layout.fruchterman.reingold
true_adj_matrix <- as.matrix(L$theta)
true_graph <- graph_from_adjacency_matrix(true_adj_matrix , mode='undirected', diag=F )
plot(true_graph, usearrows = FALSE, vertex.label=1:25,displaylabels=T,main = "True Graph",
     layout= graph_layout,edge.color = "black",edge.width = 2)

# estimated graph
est_adj_matrix <- adj_GGMtest(ggm_model_2)
est_graph <- igraph::graph_from_adjacency_matrix(est_adj_matrix , mode='undirected', diag=F )
plot(est_graph, usearrows = FALSE, vertex.label=1:25,displaylabels=T,main = "Estimated Graph",
     layout= graph_layout,edge.color = "black",edge.width = 2)

#calculation matrix difference
diff_matrix <- true_adj_matrix - est_adj_matrix
diff_graph <- igraph::graph_from_adjacency_matrix(diff_matrix  , mode='undirected', diag=F, weighted = T)
E(diff_graph)$color[E(diff_graph)$weight == 1] <- "blue"
E(diff_graph)$color[E(diff_graph)$weight == -1] <- "red"
plot(diff_graph, usearrows = FALSE, vertex.label=1:25,displaylabels=T,main = "Graph Differences",
     layout= graph_layout,edge.width = 2)
legend(x=-1.5, y=-1.1, c("False Positive","False Negative"), pch=21,
       col="#777777", pt.bg=c("red","blue"), pt.cex=2, cex=.8, bty="n", ncol=1)

par(mfrow=c(1,1))


library("psych")
library("dplyr")

df <- bfi %>%
  select(1:25) %>%
  na.omit() %>%
  apply(2,function(x) x-mean(x))

# all indices for inference
edgeset_3 <- t(combn(25,2))

ggm_model_3 <- GGMtest(df,edgeset_3,alpha = 0.05,nuisance_estimaton ="lasso")

# estimated graph
est_adj_matrix <- adj_GGMtest(ggm_model_3)
est_graph <- igraph::graph_from_adjacency_matrix(est_adj_matrix , mode='undirected', diag=F )
plot(est_graph, usearrows = FALSE, vertex.label=colnames(df),displaylabels=T,main = "Estimated Graph",
     layout= layout.fruchterman.reingold,edge.color = "black",edge.width = 2)