ggpmisc (version 0.4.3)

stat_quant_line: Predicted line from quantile regression fit

Description

Predicted values are computed and, by default, plotted. Depending on the fit method, a confidence band can be computed and plotted. The confidence band can be interpreted similarly as that produced by stat_smooth() and stat_poly_line().

Usage

stat_quant_line(
  mapping = NULL,
  data = NULL,
  geom = "smooth",
  position = "identity",
  ...,
  quantiles = c(0.25, 0.5, 0.75),
  formula = NULL,
  se = length(quantiles) == 1L,
  n = 80,
  method = "rq",
  method.args = list(),
  level = 0.95,
  type = "direct",
  interval = "confidence",
  na.rm = FALSE,
  orientation = NA,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

mapping

The aesthetic mapping, usually constructed with aes or 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 Values in 0..1 indicating the quantiles.

formula

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

se

logical Passed to quantreg::predict.rq().

n

Number of points at which to evaluate smoother.

method

function or character If character, "rq" and "rqss" are accepted. If a function, it must have formal parameters formula and data and return a model fit object for which summary() and coefficients() are consistent with those for lm fits.

method.args

named list with additional arguments passed to rq() or rqss()..

level

numeric in range [0..1] Passed to quantreg::predict.rq().

type

character Passed to quantreg::predict.rq().

interval

character Passed to quantreg::predict.rq().

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.

Value

The value returned by the statistic is a data frame, that will have n rows of predicted values and and their confidence limits for each quantile, with each quantile in a group. The variables are x and y with y containing predicted values. In addition, quantile and quantile.f indicate the quantile used and and edited group preserves the original grouping adding a new "level" for each quantile. Is se = TRUE, a confidence band is computed and values for it returned in ymax and ymin.

Aesthetics

stat_quant_line understands x and y, to be referenced in the formula and weight passed as argument to parameter weights. All three must be mapped to numeric variables. In addition, the aesthetics understood by the geom ("geom_smooth" is the default) are understood and grouping respected.

Details

stat_quant_line() behaves similarly to ggplot2::stat_smooth() and stat_poly_line() but supports fitting regressions for multiple quantiles in the same plot layer. This statistic interprets the argument passed to formula accepting y as well as x as explanatory variable, matching stat_quant_eq(). While stat_quant_eq() supports only method "rq", stat_quant_line() and stat_quant_band() support both "rq" and "rqss", In the case of "rqss" the model formula makes normally use of qss() to formulate the spline and its constraints.

geom_smooth, which is used by default, treats each axis different and thus is dependent on orientation. If no argument is passed to formula, it defaults to y ~ x. Formulas with y as explanatory variable are treated as if x was the explanatory variable and orientation = "y".

Package 'ggpmisc' does not define a new geometry matching this statistic as it is enough for the statistic to return suitable x, y, ymin, ymax and group values.

There are multiple uses for double regression on x and y. For example, when two variables are subject to mutual constrains, it is useful to consider both of them as explanatory and interpret the relationship based on them. So, from version 0.4.1 'ggpmisc' makes it possible to easily implement the approach described by Cardoso (2019) under the name of "Double quantile regression".

References

Cardoso, G. C. (2019) Double quantile regression accurately assesses distance to boundary trade-off. Methods in ecology and evolution, 10(8), 1322-1331.

See Also

rq, rqss and qss.

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

Examples

# NOT RUN {
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_line()

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_line(se = TRUE)

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

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_line(orientation = "y", se = TRUE)

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

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

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

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

# Instead of rq() we can use rqss() to fit an additive model:
library(quantreg)
ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_line(method = "rqss",
                  formula = y ~ qss(x, constraint = "D"),
                  quantiles = 0.5)

ggplot(mpg, aes(displ, hwy)) +
  geom_point() +
  stat_quant_line(method = "rqss",
                  formula = x ~ qss(y, constraint = "D"),
                  quantiles = 0.5)

ggplot(mpg, aes(displ, hwy)) +
  geom_point()+
  stat_quant_line(method="rqss",
                  interval="confidence",
                  se = TRUE,
                  mapping = aes(fill = factor(after_stat(quantile)),
                                color = factor(after_stat(quantile))),
                  quantiles=c(0.05,0.5,0.95))

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

ggplot(mpg, aes(displ, hwy, colour = drv, fill = drv)) +
  geom_point() +
  stat_quant_line(method = "rqss",
                  formula = y ~ qss(x, constraint = "V"),
                   quantiles = 0.5)

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

# }