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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