Introduction

This vignette shows how to make network plots.

# need the developmental version
if (!requireNamespace("remotes")) { 
  install.packages("remotes")   
}   

# install from github
remotes::install_github("donaldRwilliams/BGGM")
library(BGGM)
library(cowplot)

Estimate

For the estimate methods, it is currently only possible detect non-zero relations and the others are set to zero (no connection in the graph). In a future release, it will be possible to define a region of equivalence to directly assess null values. Hence, it is important to note those nodes not connected are not necessarily conditionally independent (absence of evidence is not evidence of absence).

# data
Y <- bfi[,1:25]

# fit model
fit <- estimate(Y)

# select the edge set
E <- select(fit, 
            cred = 0.95, 
            alternative = "two.sided")

plot(E)

# extract communities
comm <- substring(colnames(Y), 1, 1)

plot(E, 
     # enlarge edges
     edge_magnify = 5, 
     # cluster nodes
     groups = comm, 
     # change layout
     layout = "circle")$plt +
  # add custom labels
  scale_color_brewer(breaks = c("A", 
                                "C", 
                                "E", 
                                "N", 
                                "O"), 
                     labels =   c("Agreeableness", "Conscientiousness", 
                                  "Extraversion", "Neuroticism", 
                                  "Opennness"),
                     palette = "Set2")

plot(E, 
     # enlarge edges
     edge_magnify = 5, 
     # cluster nodes
     groups = comm, 
     # change layout
     layout = "random")$plt +
  # add custom labels
  scale_color_brewer(breaks = c("A", 
                                "C", 
                                "E", 
                                "N", 
                                "O"), 
                     labels =   c("Agreeableness", "Conscientiousness", 
                                  "Extraversion", "Neuroticism", 
                                  "Opennness"),
                     palette = "Set2")

Bayesian Hypothesis Testing

The Bayesian hypothesis testing methods offer several advantages, for example, that evidence for the null hypothesis of conditional independence is formally evaluated. As a result, the explore method in BGGM provides plots for both the conditional dependence and independence structure, in addition to a plot for which the evidence was ambiguous.

To highlight this advantage, ptsd data is used that has a relatively small sample size.

# fit model
fit <- explore(Y)

E <- select(fit, BF_cut = 3)

Then plot the results. Note that there are three plots, so the package cowplot is used to combine them into one plot.

plts <- plot(E, 
             edge_magnify = 5, 
             groups = comm)

plot_grid(
plts$H1_plt + 
  ggtitle("Conditional Dependence") + 
  theme(legend.position =  "none"),

plts$H0_plt + 
  ggtitle("Conditional Independence") + 
  theme(legend.position =  "none"),
plts$ambiguous_plt + 
  ggtitle("Ambiguous"), 
nrow = 1,
rel_widths = c(1, 1, 1.1)
)

As can be seen, there is not evidence for conditional independence for any of the relations. And the ambiguous network makes clear there is large uncertainty as to what or what might not be the “true” network structure. This basic idea of having three adjacency matrices was proposed in Williams and Mulder (2019).

Note

BGGM provides a publication ready plot, but it is also limited compared to qgraph (Epskamp et al. 2012). The one advantage of BGGM is that all plots are ggplots which then allows for combining them rather easily. An example is included in another vignette that shows how to combine several plots made with various methods in BGGM