ggplot2 autoplot for brsmm models

Produces ggplot2 diagnostics tailored to mixed beta interval models.

Usage

autoplot.brsmm(
  object,
  type = c("calibration", "score_dist", "ranef_qq", "residuals_by_group"),
  bins = 10L,
  scores = NULL,
  residual_type = c("response", "pearson"),
  max_groups = 25L,
  ...
)

Arguments

object

A fitted "brsmm" object.

type

Plot type: "calibration", "score_dist", "ranef_qq", or "residuals_by_group".

bins

Number of bins used in calibration plots.

scores

Optional integer vector of scores for "score_dist". Defaults to all scores from 0 to ncuts.

residual_type

Residual type passed to residuals.brsmm for type = "residuals_by_group".

max_groups

Maximum number of groups displayed in "residuals_by_group".

...

Currently ignored.

Value

A ggplot2 object.

Examples

# \donttest{
if (requireNamespace("ggplot2", quietly = TRUE)) {
  set.seed(123)
  g <- 10
  ni <- 8
  id <- factor(rep(seq_len(g), each = ni))
  n <- length(id)
  x1 <- rnorm(n)
  b <- rnorm(g, sd = 0.4)

  mu <- plogis(0.1 + 0.5 * x1 + b[as.integer(id)])
  phi <- plogis(-0.2)
  shp <- brs_repar(mu = mu, phi = rep(phi, n), repar = 2)
  y <- round(stats::rbeta(n, shp$shape1, shp$shape2) * 100)
  d <- data.frame(y = y, x1 = x1, id = id)

  fit_mm <- brsmm(y ~ x1, random = ~ 1 | id, data = d, repar = 2)

  autoplot.brsmm(fit_mm, type = "calibration")
  autoplot.brsmm(fit_mm, type = "ranef_qq")
}

# }