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ggpmisc (version 0.6.2)

stat_fit_deviations: Residuals from model fit as segments

Description

stat_fit_deviations fits a linear model and returns fitted values and residuals ready to be plotted as segments.

Usage

stat_fit_deviations(
  mapping = NULL,
  data = NULL,
  geom = "segment",
  position = "identity",
  ...,
  method = "lm",
  method.args = list(),
  n.min = 2L,
  formula = NULL,
  na.rm = FALSE,
  orientation = NA,
  show.legend = FALSE,
  inherit.aes = TRUE
)

stat_fit_fitted( mapping = NULL, data = NULL, geom = "point", method = "lm", method.args = list(), n.min = 2L, formula = NULL, position = "identity", na.rm = FALSE, orientation = NA, show.legend = FALSE, 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.

method

function or character If character, "lm", "rlm", "lqs", "rq" and the name of a function to be matched, possibly followed by the fit function's method argument separated by a colon (e.g. "rq:br"). Functions implementing methods must accept arguments to parameters formula, data, weights and method. A fitted() method must exist for the returned model fit object class.

method.args

named list with additional arguments.

n.min

integer Minimum number of distinct values in the explanatory variable (on the rhs of formula) for fitting to the attempted.

formula

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

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.

Computed variables

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

x

x coordinates of observations

x.fitted

x coordinates of fitted values

y

y coordinates of observations

y.fitted

y coordinates of fitted values

,
weights

the weights passed as input to lm(), rlm(), or lmrob(), using aesthetic weight. More generally the value returned by weights()

,
robustness.weights

the "weights" of the applied minimization criterion relative to those of OLS in rlm(), or lmrob()

To explore the values returned by this statistic we suggest the use of geom_debug. An example is shown below, where one can also see in addition to the computed values the default mapping of the fitted values to aesthetics xend and yend.

Details

This stat can be used to automatically highlight residuals as segments in a plot of a fitted model equation. This stat only returns the fitted values and observations, the prediction and its confidence need to be separately added to the plot when desired. Thus, to make sure that the same model formula is used in all plot layers, it is best to save the formula as an object and supply this object as argument to the different statistics.

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 and NA values removed. 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 instead of the original variable names. 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_glance(), stat_fit_residuals(), stat_fit_tb(), stat_fit_tidy()

Examples

Run this code
# generate artificial data
library(MASS)

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_smooth(method = "lm", formula = y ~ x) +
  stat_fit_deviations(method = "lm", formula = y ~ x, colour = "red") +
  geom_point()

# plot residuals from linear model with y as explanatory variable
ggplot(my.data, aes(x, y)) +
  geom_smooth(method = "lm", formula = y ~ x, orientation = "y") +
  stat_fit_deviations(method = "lm", formula = x ~ y, colour = "red") +
  geom_point()

# as above using orientation
ggplot(my.data, aes(x, y)) +
  geom_smooth(method = "lm", orientation = "y") +
  stat_fit_deviations(orientation = "y", colour = "red") +
  geom_point()

# both regressions and their deviations
ggplot(my.data, aes(x, y)) +
  geom_smooth(method = "lm") +
  stat_fit_deviations(colour = "blue") +
  geom_smooth(method = "lm", orientation = "y", colour = "red") +
  stat_fit_deviations(orientation = "y", colour = "red") +
  geom_point()

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

# plot linear regression
ggplot(my.data, aes(x, y)) +
  geom_smooth(method = "lm", formula = my.formula) +
  stat_fit_deviations(formula = my.formula, colour = "red") +
  geom_point()

ggplot(my.data, aes(x, y)) +
  geom_smooth(method = "lm", formula = my.formula) +
  stat_fit_deviations(formula = my.formula, method = stats::lm, colour = "red") +
  geom_point()

# plot robust regression
ggplot(my.data, aes(x, y)) +
  stat_smooth(method = "rlm", formula = my.formula) +
  stat_fit_deviations(formula = my.formula, method = "rlm", colour = "red") +
  geom_point()

# plot robust regression 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_smooth(method = MASS::rlm, formula = my.formula) +
  stat_fit_deviations(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") +
  geom_point()

# plot quantile regression (= median regression)
ggplot(my.data, aes(x, y)) +
  stat_quantile(formula = my.formula, quantiles = 0.5) +
  stat_fit_deviations(formula = my.formula, method = "rq", colour = "red") +
  geom_point()

# plot quantile regression (= "quartile" regression)
ggplot(my.data, aes(x, y)) +
  stat_quantile(formula = my.formula, quantiles = 0.75) +
  stat_fit_deviations(formula = my.formula, colour = "red",
                      method = "rq", method.args = list(tau = 0.75)) +
  geom_point()

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

if (gginnards.installed)
  library(gginnards)

# plot, using geom_debug() to explore the after_stat data
if (gginnards.installed)
  ggplot(my.data, aes(x, y)) +
    geom_smooth(method = "lm", formula = my.formula) +
    stat_fit_deviations(formula = my.formula, geom = "debug") +
    geom_point()

if (gginnards.installed)
  ggplot(my.data.outlier, aes(x, y)) +
    stat_smooth(method = MASS::rlm, formula = my.formula) +
    stat_fit_deviations(formula = my.formula, method = "rlm", geom = "debug") +
    geom_point()

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