View source: R/pcor_2_cor.BGGM.R
pcor_to_cor | R Documentation |
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.
# 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)
# compute correlations
cors <- pcor_to_cor(fit)
#########################
####### mixed ######
#########################
# rank based correlations
# estimate the model
fit <- estimate(Y, type = "mixed",
iter = 250,
progress = FALSE)
# compute correlations
cors <- pcor_to_cor(fit)
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