Convert the partial correlation matrices into correlation matrices. To our knowledge,
this is the only Bayesian
implementation in R that can estiamte Pearson's, tetrachoric (binary), polychoric
(ordinal with more than two cateogries), and rank based correlation coefficients.
pcor_to_cor(object, iter = NULL)
| object | An object of class |
|---|---|
| iter | numeric. How many iterations (i.e., posterior samples) should be used ? The default uses all of the samples, but note that this can take a long time with large matrices. |
R An array including the correlation matrices
(of dimensions p by p by iter)
R_mean Posterior mean of the correlations (of dimensions p by p)
The 'default' prior distributions are specified for partial correlations in particular. This means that the implied prior distribution will not be the same for the correlations.
# \donttest{ # note: iter = 250 for demonstrative purposes # data Y <- BGGM::ptsd ######################### ###### continuous ####### ######################### # estimate the model fit <- estimate(Y, iter = 250, progress = FALSE) # compute correlations cors <- pcor_to_cor(fit) ######################### ###### ordinal ######### ######################### # first level must be 1 ! Y <- Y + 1 # estimate the model fit <- estimate(Y, type = "ordinal", iter = 250, progress = FALSE)#> Warning: imputation during model fitting is #> currently only implemented for 'continuous' data.# compute correlations cors <- pcor_to_cor(fit) ######################### ####### mixed ###### ######################### # rank based correlations # estimate the model fit <- estimate(Y, type = "mixed", iter = 250, progress = FALSE)#> Warning: imputation during model fitting is #> currently only implemented for 'continuous' data.# compute correlations cors <- pcor_to_cor(fit) # }