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Summary
- Thanks for an amazing package!
ppc_stat_grouped()works with a multivariate model fit viabrms::brm()✅ppc_stat_grouped()fails with a multivariate model whensubset()is used inside themvbf()❌- Passing pre-filtered
newdatais a workaround; other workarounds welcome - May apply to
ppc_*other thanppc_stat_grouped(); that's all I tested
In the second example below, I get the error message:
Using all posterior draws for ppc type 'stat_grouped' by default.
Error in `validate_group()`:
! length(group) must be equal to the number of observations.
Run `rlang::last_trace()` to see where the error occurred.
> rlang::last_trace(drop = FALSE)
<error/rlang_error>
Error in `validate_group()`:
! length(group) must be equal to the number of observations.
---
Backtrace:
▆
1. ├─bayesplot::pp_check(...)
2. └─brms:::pp_check.brmsfit(...)
3. └─brms::do_call(ppc_fun, ppc_args)
4. └─brms:::eval2(call, envir = args, enclos = envir)
5. └─base::eval(expr, envir, ...)
6. └─base::eval(expr, envir, ...)
7. ├─bayesplot (local) .fun(y = .x1, yrep = .x2, group = .x3, stat = .x4)
8. │ └─base::eval(ungroup_call("ppc_stat", call), parent.frame())
9. │ └─base::eval(ungroup_call("ppc_stat", call), parent.frame())
10. └─bayesplot::ppc_stat(...)
11. └─bayesplot::ppc_stat_data(...)
12. └─bayesplot:::validate_group(group, length(y))
13. └─rlang::abort("length(group) must be equal to the number of observations.")
Reprex
library(tidyverse)
library(brms)
library(bayesplot)
rbernoulli <- function (n, prob = 0.5) {
sample(c(0L, 1L), size = n, replace = TRUE, prob = c(1 - prob, prob))
}
dat <-
tibble(
grp = LETTERS[1:9],
y0 = map(seq(0.1, 0.9, by = 0.1), ~ rbernoulli(300, prob = .)),
y1 = map(seq(0.1, 0.9, by = 0.1), ~ rbernoulli(300, prob = . / 2))) %>%
unnest(
c(y0, y1))
#' Just a little helper function to simplify the cases below
fit_logistic <- function (...) {
brm(
...,
data = dat,
cores = 4, backend = "cmdstanr", # comment out as you see fit
family = bernoulli())
}
#' Works great
fit <- fit_logistic(mvbf(y0 ~ (1|grp), y1 ~ (1|grp)))
pp_check(fit, resp = "y1", group = "grp", type = "stat_grouped", stat = "mean")
#' Variation: use `subset(...)` inside the `mvbf()`
#' Fails with "length(group) must be equal to the number of observations"
fit_sub <- fit_logistic(mvbf(y0 ~ (1|grp), y1 | subset(y0 == 1) ~ (1|grp)))
pp_check(fit_sub, resp = "y1", group = "grp", type = "stat_grouped", stat = "mean")
#' If we pass in `newdata` that matches the `subset()` criteria --- here, `y0 == 1` --- then success again
newdata <- filter(dat, y0 == 1)
pp_check(fit_sub, newdata = newdata, resp = "y1", group = "grp", type = "stat_grouped", stat = "mean")Metadata
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