Create data and plots for brms random effect models

Usage

ranefdata(object, usevars, newdata, idvar)

Arguments

object: a brmsfit objectx
usevars: a character vector of random effects to plot
newdata: a data.table object with the data used to generate the random effects, this is used as an anchor for the random intercepts so they have a meaningful 0 point
idvar: a character string specifying the grouping variable name for the random effects

Value

a list with the following components:

plota list of ggplot objects
plotdata list of data.table objects with the data used to generate the plots
relonga data.table object with the random effects in long format
yhata list of data.table objects with the expected values for the random effects
usevarsa character vector of the random effects to plot
idvara character string specifying the grouping variable name for the random effects

Examples

# \donttest{
if (FALSE) {
library(data.table)
library(brms)
library(cmdstanr)
library(ggpubr)

dmixed <- withr::with_seed(
  seed = 12345, code = {
    nGroups <- 100
    nObs <- 20
    theta.location <- matrix(rnorm(nGroups * 2), nrow = nGroups, ncol = 2)
    theta.location[, 1] <- theta.location[, 1] - mean(theta.location[, 1])
    theta.location[, 2] <- theta.location[, 2] - mean(theta.location[, 2])
    theta.location[, 1] <- theta.location[, 1] / sd(theta.location[, 1])
    theta.location[, 2] <- theta.location[, 2] / sd(theta.location[, 2])
    theta.location <- theta.location %*% chol(matrix(c(1.5, -.25, -.25, .5^2), 2))
    theta.location[, 1] <- theta.location[, 1] - 2.5
    theta.location[, 2] <- theta.location[, 2] + 1
    dmixed <- data.table(
      x = rep(rep(0:1, each = nObs / 2), times = nGroups))
    dmixed[, ID := rep(seq_len(nGroups), each = nObs)]

    for (i in seq_len(nGroups)) {
      dmixed[ID == i, y := rnorm(
        n = nObs,
        mean = theta.location[i, 1] + theta.location[i, 2] * x,
        sd = exp(1 + theta.location[i, 1] + theta.location[i, 2] * x))
        ]
    }
    copy(dmixed)
  })

ls.me <- brm(bf(
  y ~ 1 + x + (1 + x | p | ID),
  sigma ~ 1 + x + (1 + x | p | ID)),
  family = "gaussian",
  data = dmixed, seed = 1234,
  silent = 2, refresh = 0, iter = 2000, warmup = 1000, thin = 1,
  chains = 4L, cores = 4L, backend = "cmdstanr")

out <- ranefdata(
  ls.me,
  newdata = data.table(ID = dmixed$ID[1], x = 0), 
  usevars = c("Intercept", "x", "sigma_Intercept", "sigma_x"),
  idvar = "ID")

do.call(ggarrange, c(out$replots, ncol=2,nrow=2))
do.call(ggarrange, c(out$scatterplots, ncol=2,nrow=3))
}
# }