View source: R/lkjcorr_marginal.R
marginalize_lkjcorr | R Documentation |
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.
marginalize_lkjcorr(
data,
K,
predicate = NULL,
dist = ".dist",
args = ".args",
dist_obj = ".dist_obj"
)
data |
A data frame containing a column with distribution names ( |
K |
Dimension of the correlation matrix. Must be greater than or equal to 2. |
predicate |
a bare expression for selecting the rows of |
dist |
The name of the column containing distribution names. See |
args |
The name of the column containing distribution arguments. See |
dist_obj |
The name of the column to contain a distributional object representing the
distribution. See |
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 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 .dist
to "lkjcorr_marginal"
,
and assigns a distributional object representing this distribution to .dist_obj
.
This allows the distribution to be easily visualized using the stat_slabinterval()
family of ggplot2 stats.
A data frame of the same size and column names as the input, with the dist
, and args
,
and dist_obj
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, 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")
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