lkjcorr_marginal: Marginal distribution of a single correlation from an LKJ...

In ggdist: Visualizations of Distributions and Uncertainty

Marginal distribution of a single correlation from an LKJ distribution

Description

Marginal distribution for the correlation in a single cell from a correlation matrix distributed according to an LKJ distribution.

Usage

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)

Arguments

x, q	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, log.p	logical; if TRUE, probabilities p are given as log(p).
lower.tail	logical; if TRUE (default), probabilities are P[X \le x] otherwise, P[X > x].
p	vector of probabilities.
n	number of observations. If length(n) > 1, the length is taken to be the number required.

Details

The LKJ distribution is a distribution over correlation matrices with a single parameter, \eta. For a given \eta and a K \times K correlation matrix R:

R \sim \textrm{LKJ}(\eta)

Each off-diagonal entry of R, r_{ij}: i \ne j, has the following marginal distribution (Lewandowski, Kurowicka, and Joe 2009):

\frac{r_{ij} + 1}{2} \sim \textrm{Beta}\left(\eta - 1 + \frac{K}{2}, \eta - 1 + \frac{K}{2}\right)

In other words, r_{ij} is marginally distributed according to the above Beta distribution scaled into (-1,1).

Value

• dlkjcorr_marginal gives the density

• plkjcorr_marginal gives the cumulative distribution function (CDF)

• qlkjcorr_marginal gives the quantile function (inverse CDF)

• rlkjcorr_marginal generates random draws.

The length of the result is determined by n for rlkjcorr_marginal, and is the maximum of the lengths of the numerical arguments for the other functions.

The numerical arguments other than n are recycled to the length of the result. Only the first elements of the logical arguments are used.

References

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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.jmva.2009.04.008")}.

See Also

parse_dist() and marginalize_lkjcorr() for parsing specs that use the LKJ correlation distribution and the stat_slabinterval() family of stats for visualizing them.

Examples

library(dplyr)
library(ggplot2)
library(forcats)

theme_set(theme_ggdist())

expand.grid(
  eta = 1:6,
  K = 2:6
) %>%
  ggplot(aes(y = fct_rev(ordered(eta)), dist = "lkjcorr_marginal", arg1 = K, arg2 = eta)) +
  stat_slab() +
  facet_grid(~ paste0(K, "x", K)) +
  labs(
    title = paste0(
      "Marginal correlation for LKJ(eta) prior on different matrix sizes:\n",
      "dlkjcorr_marginal(K, eta)"
    ),
    subtitle = "Correlation matrix size (KxK)",
    y = "eta",
    x = "Marginal correlation"
  ) +
  theme(axis.title = element_text(hjust = 0))

ggdist documentation built on Nov. 27, 2023, 9:06 a.m.