Description Usage Arguments Details References See Also Examples
Marginal distribution for the correlation in a single cell from a correlation matrix distributed according to an LKJ distribution.
1 2 3 4 5 6 7  dlkjcorr_marginal(x, K, eta, log = FALSE)
plkjcorr_marginal(q, K, eta, lower.tail = TRUE, log.p = FALSE)
qlkjcorr_marginal(p, K, eta, lower.tail = TRUE, log.p = FALSE)
rlkjcorr_marginal(n, K, eta)

x 
vector of quantiles. 
K 
Dimension of the correlation matrix. Must be greater than or equal to 2. 
eta 
Parameter controlling the shape of the distribution 
log 
logical; if TRUE, probabilities p are given as log(p). 
q 
vector of quantiles. 
lower.tail 
logical; if TRUE (default), probabilities are P[X ≤ x] otherwise, P[X > x]. 
log.p 
logical; if TRUE, probabilities p are given as log(p). 
p 
vector of probabilities. 
n 
number of observations. If 
The LKJ distribution is a distribution over correlation matrices with a single parameter, eta. For a given eta and a KxK correlation matrix R:
R ~ LKJ(eta)
Each offdiagonal entry of R, r[i,j]: i != j, has the following marginal distribution (Lewandowski, Kurowicka, and Joe 2009):
(r[i,j] + 1)/2 ~ Beta(eta  1 + K/2, eta  1 + K/2)
In other words, r[i,j] is marginally distributed according to the above Beta distribution scaled into (1,1).
Lewandowski, D., Kurowicka, D., & Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis, 100(9), 1989–2001. doi: 10.1016/j.jmva.2009.04.008.
parse_dist
and marginalize_lkjcorr
for parsing specs that use the
LKJ correlation distribution and the stat_dist_slabinterval
family of stats for visualizing them.
1 2 3 4 5 6 7 8 9  library(dplyr)
library(ggplot2)
data.frame(prior = "lkjcorr_marginal(2, 3)") %>%
parse_dist(prior) %>%
ggplot(aes(y = prior, dist = .dist, args = .args)) +
stat_dist_halfeyeh() +
xlim(1, 1) +
xlab("Marginal correlation for LKJ(3) prior on 2x2 correlation matrix")

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