ggpmisc (version 0.3.0)

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", formula = NULL, resid.type = NULL,
  position = "identity", na.rm = FALSE, 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

character Currently only "lm" is implemented.

formula

a "formula" object.

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.

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.

x1

x coordinates of observations

x2

x coordinates of fitted values

y1

y coordinates of observations

y2

y coordinates of fitted values

residuals

residuals from the fit

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.

Examples

# NOT RUN {
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)) +
  stat_fit_residuals(formula = my.formula, resid.type = "working")

# }

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