stat_quant_band {ggpmisc}

Predicted band from quantile regression fits

Description

Predicted values are computed and, by default, plotted as a band plus an optional line within. stat_quant_band() supports the use of both x and y as explanatory variable in the model formula.

Usage

stat_quant_band(
  mapping = NULL,
  data = NULL,
  geom = "smooth",
  position = "identity",
  ...,
  quantiles = c(0.25, 0.5, 0.75),
  formula = NULL,
  fm.values = FALSE,
  n = 80,
  method = "rq",
  method.args = list(),
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

The aesthetic mapping, usually constructed with aes. Only needs to be set at the layer level if you are overriding the plot defaults.

data

A layer specific dataset, only needed if you want to override the plot defaults.

geom

The geometric object to use display the data.

position

The position adjustment to use for overlapping points on this layer.

...

other arguments passed on to layer. This can include aesthetics whose values you want to set, not map. See layer for more details.

quantiles

numeric vector Two or three values in 0..1 indicating the quantiles at the edges of the band and optionally a line within the band.

formula

a formula object. Using aesthetic names x and y instead of original variable names.

fm.values

logical Add n as a column to returned data? ('FALSE' by default.)

n

Number of points at which to evaluate smoother.

method

function or character If character, "rq", "rqss" or the name of a model fit function are accepted, possibly followed by the fit function's method argument separated by a colon (e.g. "rq:br"). If a function different to rq(), it must accept arguments named formula, data, weights, tau and method and return a model fit object of class rq, rqs or rqss.

method.args

named list with additional arguments.

na.rm

a logical indicating whether NA values should be stripped before the computation proceeds.

orientation

character Either "x" or "y" controlling the default for formula.

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.

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.

Details

This statistic is similar to stat_quant_line but plots the quantiles differently with the band representing a region between two quantiles, while in stat_quant_line() the bands plotted when se = TRUE represent confidence intervals for the fitted quantile lines.

geom_smooth, which is used by default, treats each axis differently and thus is dependent on orientation. If no argument is passed to formula, it defaults to y ~ x but x ~y is also accepted, and equivalent to y ~ x plus orientation = "y". Package 'ggpmisc' does not define a new geometry matching this statistic as it is enough for the statistic to return suitable 'x' and 'y' values.

Value

The value returned by the statistic is a data frame, that will have n rows of predicted values for three quantiles as y, ymin and ymax, plus x.

Aesthetics

stat_quant_eq expects x and y, aesthetics to be used in the formula rather than the names of the variables mapped to them. If present, the variable mapped to the weight aesthetics is passed as argument to parameter weights of the fitting function. All three must be mapped to numeric variables. In addition, the aesthetics recognized by the geometry ("geom_smooth" is the default) are obeyed and grouping respected.

See Also

Other ggplot statistics for quantile regression: stat_quant_eq(), stat_quant_line()

Examples

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band()

# If you need the fitting to be done along the y-axis set the orientation
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(orientation = "y")

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(formula = y ~ x)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(formula = x ~ y)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(formula = y ~ poly(x, 3))

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(formula = x ~ poly(y, 3))

# Instead of rq() we can use rqss() to fit an additive model:
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(method = "rqss",
                  formula = y ~ qss(x))

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(method = "rqss",
                  formula = x ~ qss(y, constraint = "D"))

# Regressions are automatically fit to each group (defined by categorical
# aesthetics or the group aesthetic) and for each facet.

ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
  stat_quant_band(formula = y ~ x)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(formula = y ~ poly(x, 2)) +
  facet_wrap(~drv)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(linetype = "dashed", color = "darkred", fill = "red")

ggplot(mpg, aes(displ, hwy)) +
  stat_quant_band(color = NA, alpha = 1) +
  geom_point()

ggplot(mpg, aes(displ, hwy)) +
  stat_quant_band(quantiles = c(0, 0.1, 0.2)) +
  geom_point()

# Inspecting the returned data using geom_debug()
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)

if (gginnards.installed)
  library(gginnards)

if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_quant_band(geom = "debug")

if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_quant_band(geom = "debug", fm.values = TRUE)