geom_ and stat_ for Producing Quantile Residual Q-Q Plots with ‘ggplot2’

Description

Various geom_ and stat_ used within autoplot for producing quantile residual Q-Q plots.

Usage

geom_qqrplot(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_qqrplot_simint(
  mapping = NULL,
  data = NULL,
  geom = "qqrplot_simint",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_qqrplot_simint(
  mapping = NULL,
  data = NULL,
  stat = "qqrplot_simint",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

stat_qqrplot_ref(
  mapping = NULL,
  data = NULL,
  geom = "qqrplot_ref",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  detrend = FALSE,
  identity = TRUE,
  probs = c(0.25, 0.75),
  scale = c("normal", "uniform"),
  ...
)

geom_qqrplot_ref(
  mapping = NULL,
  data = NULL,
  stat = "qqrplot_ref",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  detrend = FALSE,
  identity = TRUE,
  probs = c(0.25, 0.75),
  scale = c("normal", "uniform"),
  ...
)

geom_qqrplot_confint(
  mapping = NULL,
  data = NULL,
  stat = "qqrplot_confint",
  position = "identity",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE,
  xlim = NULL,
  n = 101,
  detrend = FALSE,
  type = c("pointwise", "simultaneous", "tail-sensitive"),
  level = 0.95,
  identity = TRUE,
  probs = c(0.25, 0.75),
  scale = c("normal", "uniform"),
  style = c("polygon", "line"),
  ...
)

GeomQqrplotConfint

Arguments

mapping	Set of aesthetic mappings created by aes(). If specified and inherit.aes = TRUE (the default), it is combined with the default mapping at the top level of the plot. You must supply mapping if there is no plot mapping.
data	The data to be displayed in this layer. There are three options: If NULL, the default, the data is inherited from the plot data as specified in the call to ggplot(). A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See fortify() for which variables will be created. A function will be called with a single argument, the plot data. The return value must be a data.frame, and will be used as the layer data. A function can be created from a formula (e.g. ~ head(.x, 10)).
stat	The statistical transformation to use on the data for this layer. When using a geom_*() function to construct a layer, the stat argument can be used the override the default coupling between geoms and stats. The stat argument accepts the following: A Stat ggproto subclass, for example StatCount. A string naming the stat. To give the stat as a string, strip the function name of the stat_ prefix. For example, to use stat_count(), give the stat as “count”. For more information and other ways to specify the stat, see the layer stat documentation.
position	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The position argument accepts the following: The result of calling a position function, such as position_jitter(). This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the position_ prefix. For example, to use position_jitter(), give the position as “jitter”. For more information and other ways to specify the position, see the layer position documentation.
na.rm	If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.
show.legend	logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped. FALSE never includes, and TRUE always includes. It can also be a named logical vector to finely select the aesthetics to display.
inherit.aes	If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn’t inherit behaviour from the default plot specification, e.g. borders().
…	Other arguments passed on to layer()’s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through …. Unknown arguments that are not part of the 4 categories below are ignored. Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = “red” or linewidth = 3. The geom’s documentation has an Aesthetics section that lists the available options. The ‘required’ aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data. When constructing a layer using a stat_*() function, the … argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = “area”, outline.type = “both”). The geom’s documentation lists which parameters it can accept. Inversely, when constructing a layer using a geom_*() function, the … argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = “density”, adjust = 0.5). The stat’s documentation lists which parameters it can accept. The key_glyph argument of layer() may also be passed on through …. This can be one of the functions described as key glyphs, to change the display of the layer in the legend.
geom	The geometric object to use to display the data for this layer. When using a stat_*() function to construct a layer, the geom argument can be used to override the default coupling between stats and geoms. The geom argument accepts the following: A Geom ggproto subclass, for example GeomPoint. A string naming the geom. To give the geom as a string, strip the function name of the geom_ prefix. For example, to use geom_point(), give the geom as “point”. For more information and other ways to specify the geom, see the layer geom documentation.
detrend	logical, default FALSE. If set to TRUE the qqrplot is detrended, i.e, plotted as a wormplot.
identity	logical. Should the identity line be plotted or a theoretical line which passes through probs quantiles on the “uniform” or “normal” scale.
probs	numeric vector of length two, representing probabilities of reference line used.
scale	character. Scale on which the quantile residuals will be shown: “uniform” (default) for uniform scale or “normal” for normal scale. Used for the reference line which goes through the first and third quartile of theoretical distributions.
xlim	NULL (default) or numeric. The x limits for computing the confidence intervals.
n	positive numeric. Number of points used to compute the confidence intervals, the more the smoother.
type	character. Should “pointwise” (default), “simultaneous”, or “tail-sensitive” confidence intervals of the (randomized) quantile residuals be visualized. Simultaneous confidence intervals are based on the Kolmogorov-Smirnov test.
level	numeric. The confidence level required, defaults to 0.95.
style	character. Style for plotting confidence intervals. Either “polygon” (default) or “line”).

Format

An object of class GeomQqrplotConfint (inherits from Geom, ggproto, gg) of length 6.

Examples

library("topmodels")

if (require("ggplot2")) {
  ## Fit model
  data("CrabSatellites", package = "countreg")
  m1_pois <- glm(satellites ~ width + color, data = CrabSatellites, family = poisson)
  m2_pois <- glm(satellites ~ color, data = CrabSatellites, family = poisson)
  
  ## Compute qqrplot
  q1 <- qqrplot(m1_pois, plot = FALSE)
  q2 <- qqrplot(m2_pois, plot = FALSE)
  
  d <- c(q1, q2) 
  
  ## Get label names
  xlab <- unique(attr(d, "xlab"))
  ylab <- unique(attr(d, "ylab"))
  main <- attr(d, "main")
  main <- make.names(main, unique = TRUE)
  d$group <- factor(d$group, labels = main)
  
  ## Polygon CI around identity line used as reference 
  gg1 <- ggplot(data = d, aes(x = expected, y = observed, na.rm = TRUE)) + 
    geom_qqrplot_ref() + 
    geom_qqrplot_confint(fill = "red") + 
    geom_qqrplot() + 
    geom_qqrplot_simint(
      aes(
        x = simint_expected, 
        ymin = simint_observed_lwr, 
        ymax = simint_observed_upr,
        group = group
      )
    ) + 
    xlab(xlab) + ylab(ylab)

  gg1
  gg1 + facet_wrap(~group)
  
  ## Polygon CI around robust reference line
  gg2 <- ggplot(data = d, aes(x = expected, y = observed, na.rm = TRUE)) + 
    geom_qqrplot_ref(identity = FALSE, scale = attr(d, "scale")) + 
    geom_qqrplot_confint(identity = FALSE, scale = attr(d, "scale"), style = "line") + 
    geom_qqrplot() + 
    geom_qqrplot_simint(
      aes(
        x = simint_expected, 
        ymin = simint_observed_lwr, 
        ymax = simint_observed_upr,
        group = group
      )
    ) + 
    xlab(xlab) + ylab(ylab)

  gg2
  gg2 + facet_wrap(~group)

  ## Use different `scale`s with confidence intervals
  q1 <- qqrplot(m1_pois, scale = "uniform", plot = FALSE)
  q2 <- qqrplot(m2_pois, plot = FALSE)
  
  gg3 <- ggplot(data = q1, aes(x = expected, y = observed, na.rm = TRUE)) +
    geom_qqrplot_ref() +
    geom_qqrplot_confint(fill = "red", scale = "uniform") +
    geom_qqrplot()
  gg3
  
  gg4 <- ggplot(data = q2, aes(x = expected, y = observed, na.rm = TRUE)) +
    geom_qqrplot_ref() +
    geom_qqrplot_confint(fill = "red", scale = "uniform") +
    geom_qqrplot()
  gg4
}