netstat_custom.R
In BGGM: Bayesian Gaussian Graphical Models

## ----eval = FALSE-------------------------------------------------------------
# # need the developmental version
# if (!requireNamespace("remotes")) {
#   install.packages("remotes")
# }
# 
# # install from github
# remotes::install_github("donaldRwilliams/BGGM")

## ----warning =FALSE, message=FALSE--------------------------------------------
# need these packages
library(BGGM)
library(ggplot2)
library(assortnet)
library(networktools)

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

## ----message=FALSE, warning=FALSE, eval=FALSE---------------------------------
# library(BGGM)
# 
# # copula ggm
# fit <- estimate(Y, type = "mixed", iter = 1000)

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

## ----eval = FALSE, message=FALSE, results='hide'------------------------------
# # 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
# #> ---

## ----eval = FALSE, results='hide'---------------------------------------------
# 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
# #> ---

## ----message=FALSE, eval=FALSE------------------------------------------------
# plot(net_stat)

## ----eval = FALSE, message=FALSE, results='hide'------------------------------
# # 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
# #> ---

## ----message = FALSE, eval=FALSE----------------------------------------------
# plot(net_stat,
#      fill = "lightblue") +
#   ggtitle("Bridge Strength") +
#   xlab("Score")

## ----eval = FALSE, message=FALSE, results='hide'------------------------------
# # 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
# #> ---

## ----eval=FALSE---------------------------------------------------------------
# hist(net_stat$results, main = "Assortment")