R packages

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

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

Data

In all examples, a subset of ptsd data is used. The subset includes two of the “communities” of symptoms (details for these data can be found in Armour et al. 2017). The data are ordinal (5-level Likert).

# need these packages
library(BGGM)
library(ggplot2)
library(assortnet)
library(networktools)

# data
Y <- ptsd[,1:7]

Fit Model

For these data, the GGM is estimated with a semi-parametric copula (Hoff 2007). In BGGM, this implemented with type = mixed which is kind of a misnomer because the data do not have to be “mixed” (consisting of continuous and discrete variables). Note that the model is fitted only once which highlights that only the posterior samples are needed to compute any network statistic.

library(BGGM)

# copula ggm
fit <- estimate(Y, type = "mixed", iter = 1000)

Expected Influence

The first example computes expected influence (Robinaugh, Millner, and McNally 2016). The first step is to define a function

# define function
f <- function(x,...){
  networktools::expectedInf(x,...)$step1
}

Note that x takes the matrix which is then passed to expectedInf. The ... allows for passing additional arguments to the expectedInf function. An example is provided below. With the function defined, the next step is to compute the network statistic.

# iter = 250 for demonstrative purposes
# (but note even 1000 iters takes less than 1 second)
# compute
net_stat <- roll_your_own(object = fit,
                          FUN = f,
                          select = FALSE,
                          iter = 250)
# print
net_stat

#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Network Stats: Roll Your Own
#> Posterior Samples: 250 
#> --- 
#> Estimates: 
#> 
#>  Node Post.mean Post.sd Cred.lb Cred.ub
#>     1     0.701   0.099   0.508   0.871
#>     2     0.912   0.113   0.722   1.179
#>     3     0.985   0.112   0.742   1.199
#>     4     1.056   0.105   0.851   1.247
#>     5     1.056   0.116   0.862   1.288
#>     6     0.491   0.092   0.329   0.679
#>     7     0.698   0.098   0.521   0.878
#> --- 

The option select = FALSE indicates to compute the statistics from the partial correlation matrices (nothing set to zero). This can be changed with select = TRUE. Internally, each of the sampled partial correlation matrices is multiplied by the adjacency matrix.

net_stat <- roll_your_own(object = fit,
                          FUN = f,
                          select = TRUE,
                          iter = 250)

# print
net_stat

#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Network Stats: Roll Your Own
#> Posterior Samples: 250 
#> --- 
#> Estimates: 
#> 
#>  Node Post.mean Post.sd Cred.lb Cred.ub
#>     1     0.636   0.136   0.386   0.874
#>     2     0.792   0.113   0.580   0.996
#>     3     0.777   0.122   0.544   1.001
#>     4     0.910   0.121   0.667   1.143
#>     5     0.525   0.104   0.331   0.727
#>     6     0.484   0.110   0.270   0.686
#>     7     0.247   0.081   0.088   0.412
#> --- 

The results are then plotted with

plot(net_stat)

Bridge Strength

The next example computes bridge strength (Jones, Ma, and McNally 2019). This requires the user to define clusters or “communities”.

# clusters
communities <- substring(colnames(Y), 1, 1)

# function is slow
f <- function(x, ...){
networktools::bridge(x, ...)$`Bridge Strength`
}


# compute
net_stat <- roll_your_own(object = fit,
                          FUN = f, 
                          communities = communities,
                          iter = 250)

# print
net_stat

#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Network Stats: Roll Your Own
#> Posterior Samples: 250 
#> --- 
#> Estimates: 
#> 
#>  Node Post.mean Post.sd Cred.lb Cred.ub
#>     1     0.162   0.082   0.035   0.347
#>     2     0.250   0.113   0.061   0.501
#>     3     0.180   0.104   0.049   0.480
#>     4     0.280   0.098   0.090   0.480
#>     5     0.375   0.093   0.196   0.558
#>     6     0.617   0.166   0.339   1.002
#>     7     0.628   0.166   0.400   1.025
#> --- 

Notice communities. This is passed to ... in the function f, which, in turn, is passed to the function bridge. Any number of arguments can be passed this way. Here are the results

This can then be plotted and further customized (the returned object is a ggplot)

plot(net_stat, 
     fill = "lightblue") + 
  ggtitle("Bridge Strength") + 
  xlab("Score")

Assortment

The next example computes assortment (Newman 2003).

# clusters
communities <- substring(colnames(Y), 1, 1)

# define function
f <- function(x,...){
  assortnet::assortment.discrete(x, ...)$r
}

net_stat <- roll_your_own(object = fit,
                          FUN = f,
                          types = communities,
                          weighted = TRUE,
                          SE = FALSE, M = 1, 
                          iter = 250)

# print
net_stat

#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Network Stats: Roll Your Own
#> Posterior Samples: 250 
#> --- 
#> Estimates: 
#> 
#>  Post.mean Post.sd Cred.lb Cred.ub
#>      0.261   0.124   -0.01   0.469
#> --- 

This example demonstrate that ... can take several arguments. The results are stored in the net_stat object. They can be accessed with

hist(net_stat$results, main = "Assortment")