Create data and plots for brms random effect models

Usage

ranefdata(object, usevars, newdata, idvar, CI = 0.95, robust = FALSE)

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
CI: a numeric value between 0 and 1 specifying the interval to use. Defaults to 0.95.
robust: a logical value indicating whether to use robust estimates or not. Defaults to FALSE. Passed on to posterior_summary and .summary.ID.

Value

a list with the following components: * plot: a list of ggplot objects * plotdat: a list of data.table objects with the data used to generate the plots * relong: a data.table object with the random effects in long format * yhat: a list of data.table objects with the expected values for the random effects * usevars: a character vector of the random effects to plot * idvar: a character string specifying the grouping variable name for the random effects

Examples

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

current.seed <- .Random.seed
set.seed(12345)
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))
      ]
  }

## note this model takes several minutes, even on a high performance machine
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)

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))

## set seed back to what it was
set.seed(current.seed)

## cleanup
rm(current.seed, nGroups, nObs, theta.location, dmixed, ls.me, out)
}
# }