ggpmisc (version 0.2.8)

stat_fit_deviations: Display residuals from 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",
  method = "lm", formula = NULL, position = "identity", na.rm = FALSE,
  show.legend = FALSE, inherit.aes = TRUE, ...)

Arguments

mapping

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

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

character Currently only "lm" is implemented.

formula

a "formula" object.

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.

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.

...

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

Details

This stat can be used to automatically show residuals as segments in a plot of a fitted model equation. At the moment it supports only linear models fitted with function lm(). This stat only generates the residuals, the predicted values need to be sepearately added to the plot, so to make sure that the same model formula is used in all steps it is best to save the formula as an object and supply this object as argument to the different statistics.

Examples

library(ggplot2)
# 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, group = c("A", "B"), y2 = y * c(0.5,2))
# give a name to a formula
my.formula <- y ~ poly(x, 3, raw = TRUE)
# plot
ggplot(my.data, aes(x, y)) +
  geom_smooth(method = "lm", formula = my.formula) +
  stat_fit_deviations(formula = my.formula, color = "red") +
  geom_point()