Predicted band from quantile regression fits — stat_quant

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",
  ...,
  orientation = NA,
  quantiles = c(0.25, 0.5, 0.75),
  formula = NULL,
  fit.seed = NA,
  fm.values = FALSE,
  n = 80,
  method = "rq",
  method.args = list(),
  n.min = 3L,
  na.rm = FALSE,
  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.
orientation: character Either "x" or "y" controlling the default for formula. The letter indicates the aesthetic considered the explanatory variable in the model fit.
quantiles: A numeric vector of length 3, with unique values in \(0\ldots 1\). The three quantile regressions are mapped to y, ymax and ymin aesthetics, and by default plotted as a line and band.
formula: a formula object. Using aesthetic names x and y instead of original variable names.
fit.seed: RNG seed argument passed to set.seed(). Defaults to NA, indicating that set.seed() should not be called.
fm.values: logical Add metadata and parameter estimates extracted from the fitted model object; FALSE by default.
n: Number of points at which to predict with the fitted model.
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 passed to rq(), rqss() or to another function passed as argument to method.
n.min: integer Minimum number of distinct values in the explanatory variable (on the rhs of formula) for fitting to the attempted.
na.rm: a logical indicating whether NA values should be stripped before the computation proceeds.
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.

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.

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 `data` for plotting.

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.

Model fit methods supported

Several model fit functions are supported explicitly (see tables), and some of their differences smoothed out. Compatibility is checked late, based on the class of the returned fitted model object. This makes it possible to use wrapper functions that do model selection or other adjustments to the fit procedure on a per panel or per group basis. Moreover, if the value returned as model fit object is NULL no layer is added to the plot on a per group within panel basis.

In the case of fitted model objects of classes not explicitly supported an attempt is made to find the usual accessors and/or fitted object members, and if found, either complete or partial support is frequently achieved. In this case a message is issued encouraging users to check the valisdity of the values extracted.

The argument to parameter method can be either the name of a function object, possibly using double colon notation, or a character string matching the function name. This approach makes it possible to support model fit functions that are not dependencies of 'ggpmisc'. Either by attaching the package where the function is defined and passing it by name or as string, or using double colon notation when passing the name of the function. User-defined functions can be passed as argument to parameter method as long as they have parameters formula, data subset and possibly weights. Additional arguments can be passed to any method as a named list as an argument to parameter method.args. As in stat_smooth() prior weights are passed to the model fit functions' weights (plural!) parameter by mapping a numeric variable to plot aesthetic weight (singular!).

The table below lists natively supported model fit functions, with the caveat that only some 'broom' methods' specializations have been actually tested with statistics from 'ggpmisc'. In addition, the statistics based on 'broom' methods require the user to tailor their behaviour by passing additional arguments in the call.

Statistic	\(f\)	Supported model fit methods
`stat_poly_line()`	G	"lm", "rlm", "lts", "sma", "ma", "gls", others with methods `predict()` or `fitted()`
`stat_poly_eq()`	G	"lm", "rlm", "lts", "sma", "ma", "gls", others with needed accesors
`stat_quant_line()`	G	"rq", "rqss"
`stat_quant_band()`	G	"rq", "rqss"
`stat_quant_eq()`	G	"rq", "rqss"
`stat_ma_line()`	G	"SMA", "MA", "RMA", "OLS"
`stat_ma_eq()`	G	"SMA", "MA", "RMA", "OLS"
`stat_fit_residuals()`	G	"lm", "rlm", "lts", "sma", "ma", "gls", "rq", "rqss" others with method `residuals()`
`stat_fit_fitted()`	G	"lm", "rlm", "lts", "gls", "rq", "rqss" others with method `fitted()`
`stat_fit_deviations()`	G	"lm", "rlm", "lts", "gls", "rq", "rqss" others with methods `fitted()` and `weights()`
`stat_fit_augment()`	G	any with 'broom' method `augment()`
`stat_fit_glance()`	G	any with 'broom' method `glance()`
`stat_fit_tidy()`	G	any with 'broom' method `tidy()`
`stat_fit_tb()`	P	any with 'broom' method `tidy()`

The table below lists the names for fit methods coded in the statistics as given in the table above. The single colon notation is based on parsing the name and is available whenever passing the name of the fit method as a character string. In a string such as "head:tail" the "head" gives the name of the model fit function and the "tail" gives the argument to pass it's method parameter. In some cases the default formula = y ~ x needs to be overridden with an explicit argument.

Predefined method names	Model fit methods	R package	Object class
"lm", "lm:qr"	`lm()`	'stats'	"lm"
"rlm", "rlm:M", "rlm:MM"	`rlm()`	'MASS'	"rlm" ("lm")
"lts", "ltsReg"	`ltsReg()`	'robustbase'	"lts"
"ma", "sma", "sma:SMA", "sma:MA", "sma:OLS"	`sma()`	'smatr'	"ma" or "sma"
"gls", "gls:REML", "gls:ML"	`gls()`	'nlme'	"gls"
"rq", "rq:sfn", "rq:sfnc", "rq:lasso"	`rq()`	'quantreg'	"rq"
"rqss", "rqss:sfn", "rqss:sfnc", "rqss:lasso"	`rqss()`	'quantreg'	"rqss"
"SMA", "MA", "RMA", "OLS"	`lmodel2()`	'lmodel2'

Aesthetics

stat_quant_band() understands the following aesthetics. Required aesthetics are displayed in bold and defaults are displayed for optional aesthetics:

•	`x`
•	`y`
•	`group`	→ inferred

Learn more about setting these aesthetics in vignette("ggplot2-specs").

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))
#> Warning: Computation failed in `stat_quant_band()`.
#> Caused by error in `qss()`:
#> ! could not find function "qss"


ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_band(method = "rqss",
                  formula = x ~ qss(y, constraint = "D"))
#> Warning: Computation failed in `stat_quant_band()`.
#> Caused by error in `qss()`:
#> ! could not find function "qss"


# 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)
#> Warning: Solution may be nonunique
#> Warning: Solution may be nonunique


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_group()
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)

if (gginnards.installed)
  library(gginnards)

if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_quant_band(geom = "debug_group")
#> Warning: Ignoring unknown parameters: `se`

#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (head):"
#>          x     ymin        y     ymax flipped_aes PANEL group orientation
#> 1 1.600000 28.42857 29.85714 32.00000       FALSE     1    -1           x
#> 2 1.668354 28.16817 29.61302 31.76659       FALSE     1    -1           x
#> 3 1.736709 27.90778 29.36890 31.53319       FALSE     1    -1           x
#> 4 1.805063 27.64738 29.12477 31.29978       FALSE     1    -1           x
#> 5 1.873418 27.38698 28.88065 31.06638       FALSE     1    -1           x
#> 6 1.941772 27.12658 28.63653 30.83297       FALSE     1    -1           x

if (gginnards.installed)
  ggplot(mpg, aes(displ, hwy)) +
    stat_quant_band(geom = "debug_group", fm.values = TRUE)
#> Warning: Ignoring unknown parameters: `se`

#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (head):"
#>          x     ymin fm1.class fm1.formula.chr        y fm2.class
#> 1 1.600000 28.42857        rq           y ~ x 29.85714        rq
#> 2 1.668354 28.16817        rq           y ~ x 29.61302        rq
#> 3 1.736709 27.90778        rq           y ~ x 29.36890        rq
#> 4 1.805063 27.64738        rq           y ~ x 29.12477        rq
#> 5 1.873418 27.38698        rq           y ~ x 28.88065        rq
#> 6 1.941772 27.12658        rq           y ~ x 28.63653        rq
#>   fm2.formula.chr     ymax fm3.class fm3.formula.chr   n fm.method flipped_aes
#> 1           y ~ x 32.00000        rq           y ~ x 234        rq       FALSE
#> 2           y ~ x 31.76659        rq           y ~ x 234        rq       FALSE
#> 3           y ~ x 31.53319        rq           y ~ x 234        rq       FALSE
#> 4           y ~ x 31.29978        rq           y ~ x 234        rq       FALSE
#> 5           y ~ x 31.06638        rq           y ~ x 234        rq       FALSE
#> 6           y ~ x 30.83297        rq           y ~ x 234        rq       FALSE
#>   PANEL group orientation
#> 1     1    -1           x
#> 2     1    -1           x
#> 3     1    -1           x
#> 4     1    -1           x
#> 5     1    -1           x
#> 6     1    -1           x