topmodels

S3 Methods for Plotting Q-Q Residuals Plots

Description

Generic plotting functions for Q-Q residual plots for objects of class “qqrplot” returned by link{qqrplot}.

Usage

## S3 method for class 'qqrplot'
plot(
  x,
  single_graph = FALSE,
  detrend = NULL,
  simint = NULL,
  confint = NULL,
  confint_type = c("pointwise", "simultaneous", "beta", "normal", "ks", "ell"),
  confint_level = 0.95,
  ref = NULL,
  ref_type = NULL,
  xlim = c(NA, NA),
  ylim = c(NA, NA),
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  axes = TRUE,
  box = TRUE,
  col = "black",
  pch = 19,
  simint_col = "black",
  simint_alpha = 0.2,
  confint_col = "black",
  confint_lty = 2,
  confint_lwd = 1.25,
  confint_alpha = NULL,
  ref_col = "black",
  ref_lty = 2,
  ref_lwd = 1.25,
  ...
)

## S3 method for class 'qqrplot'
points(
  x,
  detrend = NULL,
  simint = FALSE,
  col = "black",
  pch = 19,
  simint_col = "black",
  simint_alpha = 0.2,
  ...
)

## S3 method for class 'qqrplot'
autoplot(
  object,
  single_graph = FALSE,
  detrend = NULL,
  simint = NULL,
  confint = NULL,
  confint_type = c("pointwise", "simultaneous", "beta", "normal", "ks", "ell"),
  confint_level = 0.95,
  ref = NULL,
  ref_type = NULL,
  xlim = c(NA, NA),
  ylim = c(NA, NA),
  xlab = NULL,
  ylab = NULL,
  main = NULL,
  legend = FALSE,
  theme = NULL,
  alpha = NA,
  colour = "black",
  fill = NA,
  shape = 19,
  size = 2,
  stroke = 0.5,
  simint_fill = "black",
  simint_alpha = 0.2,
  confint_colour = NULL,
  confint_fill = NULL,
  confint_size = NULL,
  confint_linetype = NULL,
  confint_alpha = NULL,
  ref_colour = "black",
  ref_size = 0.5,
  ref_linetype = 2,
  ...
)

Arguments

`x`, `object`	an object of class `qqrplot` as returned by `qqrplot`.
`single_graph`	logical, defaults to `FALSE`. In case of multiple Q-Q residual plots: should all be drawn in a single graph?
`detrend`	logical. Should the qqrplot be detrended, i.e, plotted as a ‘wormplot()’? If `NULL` (default) this is extracted from `x`/`object`.
`simint`	logical or quantile specification. Should the simint of quantiles of the randomized quantile residuals be visualized?
`confint`	logical or character string describing the style for plotting ‘c("polygon", "line")’.
`confint_type`	character. Method for creating the confidence intervals. There are two methods for pointwise confidence intervals: Based on the ‘"beta"’ or ‘"normal"’ distribution, yielding very similar results. And there are two methods for simultaneous confidence intervals: Based on the Kolmogorov-Smirnov test (‘"ks"’) or equal local levels (‘"ell"’), where the latter has much better properties but requires the qqconf package to be installed ([qqconf::get_qq_band()]). Finally, the methods ‘"pointwise"’ and ‘"simultaneous"’ are simply aliases for the preferred corresponding methods ‘"beta"’ and ‘"ell"’, respectively.
`confint_level`	numeric. The confidence level required, defaults to `0.95`.
`ref`	logical. Should a reference line be plotted?
`ref_type`	character specifying that the ‘"identity"’ line should be used as a reference or, alternatively, a line through the ‘"quartiles"’ of the quantile residuals. Moreover, also a numeric vector of length two can be used to define the probabilities of the quantiles to be used for defining the reference line. Note, that the reference is also used for detrending the quantile residuals. For uniform scales, the identity line must be used for reference (‘ref_type = "identity"’).
`xlim`, `ylim`, `axes`, `box`	additional graphical parameters for base plots, whereby `x` is a object of class `qqrplot`.
`xlab`, `ylab`, `main`, `…`	graphical plotting parameters passed to `plot` or `points`, respectively.
`col`, `pch`	graphical parameters for the main part of the base plot.
`simint_col`, `simint_alpha`, `confint_col`, `confint_lty`, `confint_lwd`, `ref_col`, `ref_lty`, `ref_lwd`	Further graphical parameters for the ‘confint’ and ‘simint’ line/polygon in the base plot.
`legend`	logical. Should a legend be added in the `ggplot2` style graphic?
`theme`	name of the ‘ggplot2’ theme to be used. If `theme = NULL`, the `theme_bw` is employed.
`colour`, `fill`, `alpha`, `shape`, `size`, `stroke`	graphical parameters passed to `ggplot2` style plots.
`simint_fill`, `confint_colour`, `confint_fill`, `confint_size`, `confint_linetype`, `confint_alpha`, `ref_colour`, `ref_size`, `ref_linetype`	Further graphical parameters for the ‘confint’ and ‘simint’ line/polygon using `autoplot`.

Details

Q-Q residuals plots draw quantile residuals (by default on the standard normal scale) against theoretical quantiles from the same distribution. Alternatively, quantile residuals can also be compared on the uniform scale (scale = “uniform”) using no transformation.

Q-Q residuals plots can be rendered as ggplot2 or base R graphics by using the generics autoplot or plot. points (points.qqrplot) can be used to add Q-Q residuals to an existing base R graphics panel.

References

Dunn KP, Smyth GK (1996). “Randomized Quantile Residuals.” Journal of Computational and Graphical Statistics, 5(3), 236–244. doi:10.2307/1390802

Examples

library("topmodels")


## speed and stopping distances of cars
m1_lm <- lm(dist ~ speed, data = cars)

## compute and plot qqrplot
qqrplot(m1_lm)

## customize colors
qqrplot(m1_lm, plot = "base", ref_col = "blue", lty = 2, pch = 20)

## add separate model
if (require("crch", quietly = TRUE)) {
  m1_crch <- crch(dist ~ speed | speed, data = cars)
  points(qqrplot(m1_crch, plot = FALSE), col = 2, lty = 2, simint = 2)
}

[1] "expected"

#-------------------------------------------------------------------------------
if (require("crch")) {

  ## precipitation observations and forecasts for Innsbruck
  data("RainIbk", package = "crch")
  RainIbk <- sqrt(RainIbk)
  RainIbk$ensmean <- apply(RainIbk[,grep('^rainfc',names(RainIbk))], 1, mean)
  RainIbk$enssd <- apply(RainIbk[,grep('^rainfc',names(RainIbk))], 1, sd)
  RainIbk <- subset(RainIbk, enssd > 0)

  ## linear model w/ constant variance estimation
  m2_lm <- lm(rain ~ ensmean, data = RainIbk)

  ## logistic censored model 
  m2_crch <- crch(rain ~ ensmean | log(enssd), data = RainIbk, left = 0, dist = "logistic")

  ## compute qqrplots
  qq2_lm <- qqrplot(m2_lm, plot = FALSE)
  qq2_crch <- qqrplot(m2_crch, plot = FALSE)

  ## plot in single graph
  plot(c(qq2_lm, qq2_crch), col = c(1, 2), simint_col = c(1, 2), single_graph = TRUE)
}

#-------------------------------------------------------------------------------
## determinants for male satellites to nesting horseshoe crabs
data("CrabSatellites", package = "countreg")

## linear poisson model
m3_pois  <- glm(satellites ~ width + color, data = CrabSatellites, family = poisson)

## compute and plot qqrplot as "ggplot2" graphic
qqrplot(m3_pois, plot = "ggplot2")