Nothing
## ----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")
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