Description Usage Arguments Details Value See Also Examples
View source: R/lkjcorr_marginal.R
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 ( |
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 |
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()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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")
|
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