marginalize_lkjcorr: Turn spec for LKJ distribution into spec for marginal LKJ...
In ggdist: Visualizations of Distributions and Uncertainty

Description Usage Arguments Details Value See Also Examples

Turns specs for an LKJ correlation matrix distribution as returned by parse_dist() into specs for the marginal distribution of a single cell in an LKJ-distributed correlation matrix (i.e., lkjcorr_marginal()). Useful for visualizing prior correlations from LKJ distributions.

1	marginalize_lkjcorr(data, K, predicate = NULL, dist = ".dist", args = ".args")

`data`	A data frame containing a column with distribution names (`".dist"` by default) and a list column of distribution arguments (`".args"` by default), such as output by `parse_dist()`.
`K`	Dimension of the correlation matrix. Must be greater than or equal to 2.
`predicate`	a bare expression for selecting the rows of `data` to modify. This is useful if `data` contains more than one row with an LKJ prior in it and you only want to modify some of the distributions; if this is the case, give row a predicate expression (such as you might supply to `dplyr::filter()`) that evaluates to `TRUE` on the rows you want to modify. If `NULL` (the default), all `lkjcorr` distributions in `data` are modified.
`dist`	The name of the column containing distribution names. See `parse_dist()`.
`args`	The name of the column containing distribution arguments. See `parse_dist()`.

The LKJ(eta) prior on a correlation matrix induces a marginal prior on each correlation in the matrix that depends on both the value of eta and K,the dimension of the KxK correlation matrix. Thus to visualize the marginal prior on the correlations, it is necessary to specify the value of K, which depends on what your model specification looks like.

Given a data frame representing parsed distribution specifications (such as returned by parse_dist()), this function updates any rows with .dist == "lkjcorr" so that the first argument to the distribution is equal to the specified dimension of the correlation matrix (K) and changes the distribution name to "lkjcorr_marginal", allowing the distribution to be easily visualized using the stat_dist_slabinterval() family of ggplot2 stats.

A data frame of the same size and column names as the input, with the dist and args columns modified on rows where dist == "lkjcorr" such that they represent a marginal LKJ correlation distribution with name lkjcorr_marginal and args having K equal to the input value of K.

parse_dist(), lkjcorr_marginal()

library(dplyr)
library(ggplot2)

# Say we have an LKJ(3) prior on a 2x2 correlation matrix. We can visualize
# its marginal distribution as follows...
data.frame(prior = "lkjcorr(3)") %>%
  parse_dist(prior) %>%
  marginalize_lkjcorr(K = 2) %>%
  ggplot(aes(y = prior, dist = .dist, args = .args)) +
  stat_dist_halfeye() +
  xlim(-1, 1) +
  xlab("Marginal correlation for LKJ(3) prior on 2x2 correlation matrix")

# Say our prior list has multiple LKJ priors on correlation matrices
# of different sizes, we can supply a predicate expression to select
# only those rows we want to modify
data.frame(coef = c("a", "b"), prior = "lkjcorr(3)") %>%
  parse_dist(prior) %>%
  marginalize_lkjcorr(K = 2, coef == "a") %>%
  marginalize_lkjcorr(K = 4, coef == "b")