predictability.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)

## ----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)