Nothing
## ----eval = FALSE, message=FALSE----------------------------------------------
# # need the developmental version
# if (!requireNamespace("remotes")) {
# install.packages("remotes")
# }
#
# # install from github
# remotes::install_github("donaldRwilliams/BGGM")
# library(BGGM)
## ----eval=FALSE---------------------------------------------------------------
# # binary data
# Y <- women_math
#
# # fit model
# fit <- estimate(Y, type = "binary")
## ----eval=FALSE---------------------------------------------------------------
# r2 <- predictability(fit)
#
# # print
# r2
#
# #> BGGM: Bayesian Gaussian Graphical Models
# #> ---
# #> Metric: Bayes R2
# #> Type: binary
# #> ---
# #> Estimates:
# #>
# #> Node Post.mean Post.sd Cred.lb Cred.ub
# #> 1 0.016 0.012 0.002 0.046
# #> 2 0.103 0.023 0.064 0.150
# #> 3 0.155 0.030 0.092 0.210
# #> 4 0.160 0.021 0.118 0.201
# #> 5 0.162 0.022 0.118 0.202
# #> 6 0.157 0.028 0.097 0.208
# #> ---
## ----message=FALSE, eval=FALSE------------------------------------------------
# plot(r2,
# type = "error_bar",
# size = 4,
# cred = 0.90)
## ----message=FALSE, eval=FALSE------------------------------------------------
# plot(r2,
# type = "ridgeline",
# cred = 0.50)
## ----eval=FALSE---------------------------------------------------------------
# Y <- ptsd
#
# fit <- estimate(Y + 1, type = "ordinal")
## ----eval=FALSE---------------------------------------------------------------
# r2 <- predictability(fit)
#
# # print
# r2
#
# #> BGGM: Bayesian Gaussian Graphical Models
# #> ---
# #> Metric: Bayes R2
# #> Type: ordinal
# #> ---
# #> Estimates:
# #>
# #> Node Post.mean Post.sd Cred.lb Cred.ub
# #> 1 0.487 0.049 0.394 0.585
# #> 2 0.497 0.047 0.412 0.592
# #> 3 0.509 0.047 0.423 0.605
# #> 4 0.524 0.049 0.441 0.633
# #> 5 0.495 0.047 0.409 0.583
# #> 6 0.297 0.043 0.217 0.379
# #> 7 0.395 0.045 0.314 0.491
# #> 8 0.250 0.042 0.173 0.336
# #> 9 0.440 0.048 0.358 0.545
# #> 10 0.417 0.044 0.337 0.508
# #> 11 0.549 0.048 0.463 0.648
# #> 12 0.508 0.048 0.423 0.607
# #> 13 0.504 0.047 0.421 0.600
# #> 14 0.485 0.043 0.411 0.568
# #> 15 0.442 0.045 0.355 0.528
# #> 16 0.332 0.039 0.257 0.414
# #> 17 0.331 0.045 0.259 0.436
# #> 18 0.423 0.044 0.345 0.510
# #> 19 0.438 0.044 0.354 0.525
# #> 20 0.362 0.043 0.285 0.454
# #> ---
## ----eval=FALSE---------------------------------------------------------------
# plot(r2)
## ----eval=FALSE---------------------------------------------------------------
# # fit model
# fit <- estimate(Y)
#
# # predictability
# r2 <- predictability(fit)
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