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.,
Useful for visualizing prior correlations from LKJ distributions.
marginalize_lkjcorr( data, K, predicate = NULL, dist = ".dist", args = ".args", dist_obj = ".dist_obj" )
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
Dimension of the correlation matrix. Must be greater than or equal to 2.
a bare expression for selecting the rows of
data to modify. This is useful
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
dplyr::filter()) that evaluates to
TRUE on the rows you want to modify.
NULL (the default), all
lkjcorr distributions in
data are modified.
The name of the column containing distribution names. See
The name of the column containing distribution arguments. See
The name of the column to contain a distributional object representing the
A data frame of the same size and column names as the input, with the
dist_obj columns modified on rows where
dist == "lkjcorr" such that they represent a
marginal LKJ correlation distribution with name
K equal to the input value of
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
K, the dimension
of the \(K \times K\) 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 (stored in
.args) is equal to the specified dimension
of the correlation matrix (
K), changes the distribution name in
and assigns a distributional object representing this distribution to
This allows the distribution to be easily visualized using the
family of ggplot2 stats.
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, xdist = .dist_obj)) + stat_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") #> coef prior .dist .args .dist_obj #> 1 a lkjcorr(3) lkjcorr_marginal 2, 3 lkjcorr_marginal(2, 3) #> 2 b lkjcorr(3) lkjcorr_marginal 4, 3 lkjcorr_marginal(4, 3)