mcmc_diagnostics.R
In BGGM: Bayesian Gaussian Graphical Models

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

## ----echo=FALSE, message=FALSE------------------------------------------------
library(BGGM)

## ----eval=FALSE---------------------------------------------------------------
# # data
# Y <- ptsd[,1:10]
# 
# # fit model
# # + 1 makes first category a 1
# fit <- estimate(Y + 1, type = "ordinal")

## ----eval=FALSE---------------------------------------------------------------
# convergence(fit, print_names = TRUE)
# 
# #>  [1] "B1--B2"         "B1--B3"         "B2--B3"         "B1--B4"         "B2--B4"         "B3--B4"         "B1--B5"
# #>  [8] "B2--B5"         "B3--B5"         "B4--B5"         "B1--C1"         "B2--C1"         "B3--C1"         "B4--C1"
# #> [15] "B5--C1"         "B1--C2"         "B2--C2"         "B3--C2"         "B4--C2"         "B5--C2"         "C1--C2"
# #> [22] "B1--D1"         "B2--D1"         "B3--D1"         "B4--D1"         "B5--D1"         "C1--D1"         "C2--D1"
# #> [29] "B1--D2"         "B2--D2"         "B3--D2"         "B4--D2"         "B5--D2"         "C1--D2"         "C2--D2"
# #> [36] "D1--D2"         "B1--D3"         "B2--D3"         "B3--D3"         "B4--D3"         "B5--D3"         "C1--D3"
# #> [43] "C2--D3"         "D1--D3"         "D2--D3"         "B1_(Intercept)" "B2_(Intercept)" "B3_(Intercept)" "B4_(Intercept)"
# #> [50] "B5_(Intercept)" "C1_(Intercept)" "C2_(Intercept)" "D1_(Intercept)" "D2_(Intercept)" "D3_(Intercept)"

## ----eval=FALSE---------------------------------------------------------------
# convergence(fit, param = "B1--B2", type = "acf")

## ----eval=FALSE---------------------------------------------------------------
# # sim time series
# ts.sim <- arima.sim(list(order = c(1,1,0), ar = 0.7), n = 200)
# 
# acf(ts.sim)

## ----eval=FALSE---------------------------------------------------------------
# # extract samples
# samps <- fit$post_samp$pcors
# 
# # iterations
# iter <- fit$iter
# 
# # thinning interval
# thin <-  5
# 
# # save every 5th (add 50 which is the burnin)
# new_iter <- length(seq(1,to = iter + 50 , by = thin))
# 
# # replace (add 50 which is the burnin)
# fit$post_samp$pcors <- samps[,,seq(1,to = iter + 50, by = thin)]
# 
# # replace iter
# fit$iter <- new_iter - 50
# 
# # check thinned
# convergence(fit, param = "B1--B2", type = "acf")

## ----eval=FALSE---------------------------------------------------------------
# convergence(fit, param = "B1--B2", type = "trace")