I think in this case we could add a column "Response" and basically have the current dataframes (with indices for all parameters) rbinded with Response?

Response       | Parameter      | CI | ROPE
y1             | x1             |0.9 | 0.25
y1             | x1             |0.95| 0.13
y1             | x2             |0.9 | 0.122
y1             | x2             |0.95| 0.8
y2             | x1             |0.9 | 0.54
y2             | x1             |0.95| 0.8
y2             | x2             |0.9 | 0.456
y2             | x2             |0.95| 0.4

It's a bit more difficult. We first check the rope-range ( rope_range()), and then perform the equivalence-test. The good thing: insight::get_parameters() is enough for multivariate-response models, however, rope_range() returns an array of ranges. And this must be considered in the code...

This suggests some despair of this issue... 😆

haha 😂

It's a bit more difficult. We first check the rope-range ( rope_range()), and then perform the equivalence-test. The good thing: insight::get_parameters() is enough for multivariate-response models, however, rope_range() returns an array of ranges. And this must be considered in the code...

rope_range() now returns a named list, so I think this issue might be accommodated at this point?

I think we should (at some point?) add/allow multivariate statistics for multivariate models.

For example, instead of testing how much of the slope of X is in the ROPE of Y1, and also testing how much of the slope of X is in the ROPE of Y2, we can test how much of the slope(s) of X are in both the ROPE of Y1 and the ROPE of Y2 = what is the % of the posterior that indicates no multivariate effect at all:

library(brms)
library(bayestestR)
library(dplyr)

model <- brm(mvbind(mpg, wt) ~ cyl + disp, data = mtcars,
             chains = 1, iter = 200)

MVROPE <- rope_range(model)

samps <- insight::get_parameters(model)


# ROPE
samps %>% 
  mutate(
    across(starts_with("b_mpg"), ~between(.x, MVROPE$mpg[1], MVROPE$mpg[2])),
    across(starts_with("b_wt"), ~between(.x, MVROPE$wt[1], MVROPE$wt[2])),
  ) %>% 
  summarise(
    cyl = across(ends_with("cyl")) %>% apply(1, all) %>% mean(),
    disp = across(ends_with("disp")) %>% apply(1, all) %>% mean()
  ) %>% t() %>% `colnames<-`("% in ROPE")
#>      % in ROPE
#> cyl       0.06
#> disp      1.00

Similarly, we can also do this with pd: we can ask how much of the joint posterior of the effect(s) of X has a direction = what is the probability that the multivariate effect has a direction:

samps %>% 
  mutate(
    across(starts_with("b_mpg"), ~sign(.x) == sign(median(.x)) & sign(median(.x)) != 0),
    across(starts_with("b_wt"), ~sign(.x) == sign(median(.x)) & sign(median(.x)) != 0)
  ) %>% 
  summarise(
    cyl = across(ends_with("cyl")) %>% apply(1, all) %>% mean(),
    disp = across(ends_with("disp")) %>% apply(1, all) %>% mean()
  ) %>% t() %>% `colnames<-`("p(d)")
#>      p(d)
#> cyl  0.52
#> disp 0.96

Hey! Futzing with this with rope() on a bayesian SEM fit with brms. I like the function, but, filtering to a specific parameter is a pain and then adding ranges for those parameters is a pain. Behavior is inconsistent.

rope(rich_brms, 
     parameters = "firesev_age",
     range = list(firesev_age = c(-0.017, 0.017)))

Gives me

Error: With a multivariate model, `range` should be
  'default' or a list of named numeric vectors with
  length 2.

Which - that's the actual coefficient name.......... Ugh.

So, I see what you're trying to do with the parameters argument using regex and, while I applaud the goal, I'm finding it really hard to use. I would be happier with just "supply a vector of paramters". This would be nice as then that vector of parameters could match what is in the range list name. And probably easier to check the names of what goes into range against the names in parameters, I'm guessing.

It would also be nice to be able to specify, even for many parameters, just a vector of 2 numbers to set a range for all tests instead of having to go with a named list.

If your purpose had been to have all betas and intercepts, perhaps in the component argument, you could add betas and intercepts?

Also, can plotting MV models just use facet_wrap for each coef?

Can you give a reproducible example?

I think this can be solved by improving the docs. See this example, which I added to the docs:

library(bayestestR)
library(brms)
model <- brm(
  bf(mvbind(mpg, disp) ~ wt + cyl) + set_rescor(rescor = TRUE),
  data = mtcars
)

r <- list(
  mpg = list(b_mpg_wt = c(-1, 1), b_mpg_cyl = c(-2, 2)),
  disp = list(b_disp_wt = c(-5, 5), b_disp_cyl = c(-4, 4))
)
rope(model, range = r)