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
)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.
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.