ggpmisc (version 0.3.2)

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",
  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_. 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. Using aesthetic names instead of original variable names.

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.

x

x coordinates of observations

y.fitted

x coordinates of fitted values

y

y coordinates of observations

y.fitted

y coordinates of fitted values

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. At the moment it supports only linear models fitted with function lm(). This stat only generates the residuals, the predicted values need to be separately 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.

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 statistics for linear model fits: stat_fit_residuals, stat_poly_eq

Examples

# NOT RUN {
library(gginnards) # needed for geom_debug()
# 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()

# plot, using geom_debug()
ggplot(my.data, aes(x, y)) +
  geom_smooth(method = "lm", formula = my.formula) +
  stat_fit_deviations(formula = my.formula, color = "red",
  geom = "debug") +
  geom_point()

# }