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Diagnostic plots for mixed beta interval regression

Source: R/plot-brsmm.R
plot.brsmm.Rd

Produces diagnostic plots for fitted "brsmm" models: residuals vs indices, Cook's distance, residuals vs linear predictor, residuals vs fitted values, half-normal envelope, and predicted vs observed.

Usage

# S3 method for class 'brsmm'
plot(
  x,
  which = 1:4,
  type = c("response", "pearson"),
  nsim = 100L,
  level = 0.9,
  caption = c("Residuals vs indices", "Cook's distance", "Residuals vs linear predictor",
    "Residuals vs fitted values", "Half-normal plot", "Predicted vs observed",
    "Random-effects Q-Q", "Random-effects caterpillar"),
  sub.caption = NULL,
  ask = prod(par("mfcol")) < length(which) && dev.interactive(),
  gg = FALSE,
  title = NULL,
  theme = NULL,
  ...
)

Arguments

x

A fitted "brsmm" object.

which

Integer vector selecting which panels to draw (default 1:4).

type

Residual type passed to residuals.brsmm ("response" or "pearson").

nsim

Number of simulations for half-normal envelope.

level

Confidence level for the half-normal envelope.

caption

Character vector of plot captions.

sub.caption

Optional subtitle; defaults to model call.

ask

Logical: prompt before each new page?

gg

Logical: use ggplot2 backend?

title

Optional global title for ggplot output. If NULL, panel captions are used.

theme

Optional ggplot2 theme object (e.g., ggplot2::theme_bw()). If NULL, a minimal theme is used.

...

Further arguments passed to base plot().

Value

Invisibly returns x.

See also

brsmm, residuals.brsmm, autoplot.brsmm

Examples

# \donttest{
dat <- data.frame(
  y = c(
    0, 5, 20, 50, 75, 90, 100, 30, 60, 45,
    10, 40, 55, 70, 85, 25, 35, 65, 80, 15
  ),
  x1 = rep(c(1, 2), 10),
  id = factor(rep(1:4, each = 5))
)
prep <- brs_prep(dat, ncuts = 100)
#> brs_prep: n = 20 | exact = 0, left = 1, right = 1, interval = 18
fit <- brsmm(y ~ x1, random = ~ 1 | id, data = prep)
plot(fit, which = 1:4)

# }