plot.unitquantreg: Plot method for `unitquantreg` objects

Description

Provide diagnostic plots to check model assumptions for fitted model of class unitquantreg.

Usage

# S3 method for unitquantreg
plot(
  x,
  which = 1L:4L,
  caption = c("Residuals vs. indices of obs.", "Residuals vs. linear predictor",
    "Working response vs. linear predictor", "Half-normal plot of residuals"),
  sub.caption = paste(deparse(x$call), collapse = "\n"),
  main = "",
  ask = prod(par("mfcol")) < length(which) && dev.interactive(),
  ...,
  add.smooth = getOption("add.smooth"),
  type = "quantile",
  nsim = 99L,
  level = 0.95
)

Value

No return value, called for side effects.

Arguments

x: fitted model object of class unitquantreg.
which: integer. if a subset of the plots is required, specify a subset of the numbers 1 to 4, see below for further details.
caption: character. Captions to appear above the plots.
sub.caption: character. Common title-above figures if there are multiple.
main: character. Title to each plot in addition to the above caption.
ask: logical. If TRUE, the user is asked before each plot.
...: other parameters to be passed through to plotting functions.
add.smooth: logical. Indicates if a smoother should be added to most plots
type: character. Indicates type of residual to be used, see residuals.unitquantreg.
nsim: integer. Number of simulations in half-normal plots, see hnp.unitquantreg.
level: numeric. Confidence level of the simulated envelope, see hnp.unitquantreg.

Author

André F. B. Menezes

Details

The plot method for unitquantreg objects produces four types of diagnostic plot.

The which argument can be used to select a subset of currently four supported plot, which are: Residuals versus indices of observations (which = 1); Residuals versus linear predictor (which = 2); Working response versus linear predictor (which = 3) to check possible misspecification of link function; Half-normal plot of residuals (which = 4) to check distribution assumption.

References

Dunn, P. K. and Smyth, G. K. (2018) Generalized Linear Models With Examples in R, Springer, New York.