pcor_to_cor: Compute Correlations from the Partial Correlations

Description Usage Arguments Value Note Examples

View source: R/pcor_2_cor.BGGM.R

Description

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.

Usage

1
pcor_to_cor(object, iter = NULL)

Arguments

object

An object of class estimate or explore

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.

Value

Note

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.

Examples

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# 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)

BGGM documentation built on Aug. 20, 2021, 5:08 p.m.