ggpmisc (version 0.4.2-1)

stat_fit_residuals: Residuals from a model fit

Description

stat_fit_residuals fits a linear model and returns residuals ready to be plotted as points.

Usage

stat_fit_residuals(
  mapping = NULL,
  data = NULL,
  geom = "point",
  method = "lm",
  method.args = list(),
  formula = NULL,
  resid.type = NULL,
  position = "identity",
  na.rm = FALSE,
  orientation = NA,
  show.legend = FALSE,
  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

method

function or character If character, "lm", "rlm", and "rq" are implemented. If a function, it must support parameters formula and data.

method.args

named list with additional arguments.

formula

a "formula" object. Using aesthetic names instead of original variable names.

resid.type

character passed to residuals() as argument for type.

position

The position adjustment to use for overlapping points on this layer

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 should not inherit behaviour from the default plot specification, e.g. borders.

...

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

Computed variables

Data frame with same nrow as data as subset for each group containing five numeric variables.

x

x coordinates of observations

y.resid

residuals from fitted values

y.resid.abs

absolute residuals from the fit

.

By default stat(y.resid) is mapped to the y aesthetic.

Details

This stat can be used to automatically plot residuals as points in a plot. At the moment it supports only linear models fitted with function lm(). This stat only generates the residuals.

A ggplot statistic receives as data a data frame that is not the one passed as argument by the user, but instead a data frame with the variables mapped to aesthetics. In other words, it respects the grammar of graphics and consequently within the model formula names of aesthetics like $x$ and $y$ should be used intead of the original variable names, while data is automatically passed the data frame. This helps ensure that the model is fitted to the same data as plotted in other layers.

See Also

Other ggplot statistics for model fits: stat_fit_augment(), stat_fit_deviations(), stat_fit_glance(), stat_fit_tb(), stat_fit_tidy()

Examples

# NOT RUN {
# generate artificial data
set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x, y)

# plot residuals from linear model
ggplot(my.data, aes(x, y)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  stat_fit_residuals(formula = y ~ x)

# plot residuals from linear model with y as explanatory variable
ggplot(my.data, aes(x, y)) +
  geom_vline(xintercept = 0, linetype = "dashed") +
  stat_fit_residuals(formula = x ~ y) +
  coord_flip()

# give a name to a formula
my.formula <- y ~ poly(x, 3, raw = TRUE)

# plot residuals from linear model
ggplot(my.data, aes(x, y)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  stat_fit_residuals(formula = my.formula) +
  coord_flip()

ggplot(my.data, aes(x, y)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  stat_fit_residuals(formula = my.formula, resid.type = "response")

# plot residuals from robust regression
ggplot(my.data, aes(x, y)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  stat_fit_residuals(formula = my.formula, method = "rlm")

# plot residuals with weights indicated by colour
my.data.outlier <- my.data
my.data.outlier[6, "y"] <- my.data.outlier[6, "y"] * 10
ggplot(my.data.outlier, aes(x, y)) +
  stat_fit_residuals(formula = my.formula, method = "rlm",
                      mapping = aes(colour = after_stat(weights)),
                      show.legend = TRUE) +
  scale_color_gradient(low = "red", high = "blue", limits = c(0, 1),
                       guide = "colourbar")

# plot weighted residuals with weights indicated by colour
ggplot(my.data.outlier) +
  stat_fit_residuals(formula = my.formula, method = "rlm",
                     mapping = aes(x = x,
                                   y = stage(start = y, after_stat = y * weights),
                                   colour = after_stat(weights)),
                     show.legend = TRUE) +
  scale_color_gradient(low = "red", high = "blue", limits = c(0, 1),
                       guide = "colourbar")

# plot residuals from quantile regression (median)
ggplot(my.data, aes(x, y)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  stat_fit_residuals(formula = my.formula, method = "rq")

# plot residuals from quantile regression (upper quartile)
ggplot(my.data, aes(x, y)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  stat_fit_residuals(formula = my.formula, method = "rq",
  method.args = list(tau = 0.75))

# inspecting the returned data
if (requireNamespace("gginnards", quietly = TRUE)) {
  library(gginnards)

  ggplot(my.data, aes(x, y)) +
   stat_fit_residuals(formula = my.formula, resid.type = "working",
                      geom = "debug")

  ggplot(my.data, aes(x, y)) +
    stat_fit_residuals(formula = my.formula, method = "rlm",
                       geom = "debug")
}

# }