Add Squared Residual Visualization from a Function to a Plot

Usage

gf_square_resid_fun(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  fun,
  aspect = 4/6,
  alpha = 0.1,
  xlab,
  ylab,
  title,
  subtitle,
  caption,
  geom = coursekata::GeomSquareResid,
  stat = coursekata::StatResid,
  position = "identity",
  show.legend = NA,
  show.help = NULL,
  inherit = FALSE,
  environment = parent.frame()
)

Arguments

object: A ggformula plot object, typically created with gf_point().
gformula: Not used. gf_square_resid_fun() measures a function, not an aesthetic formula; a function given positionally lands here and is moved to fun.
data: Not used. The residuals are measured over the data the plot was built from. Anything supplied here is left for ggformula and ggplot2 to answer, exactly as it is for any other gf_ layer.
...: Additional arguments. Typically these are (a) ggplot2 aesthetics to be set with attribute = value, such as color or fill, (b) ggplot2 aesthetics to be mapped with attribute = ~ expression, or (c) attributes of the layer as a whole.
fun: A function of one argument. It is called on the x values the plot draws and must return one predicted y for each of them. May be given positionally or as fun =.
aspect: The square's aspect ratio. Default is 4/6. Must be named.
alpha: The transparency of the square's fill. Default is 0.1. Must be named.
xlab, ylab, title, subtitle, caption: Labels for the plot.
geom, stat, position: Not set by the caller. A squared residual is drawn by its own geom and stat, and moved by the position the observations are already drawn with, so that a square stays on the point it belongs to.
show.legend: Whether this layer contributes to the legend.
show.help: Print the layer's own help instead of drawing.
inherit: Whether the layer inherits the plot's aesthetics. FALSE, where gf_resid_fun() is TRUE: a square is a filled region drawn in the geom's own colors, so inheriting a plot's mapped color outlines every square in the color of the group it measures instead of leaving one neutral area per observation. The axes and the prediction are stated outright, so nothing the square needs is lost by not inheriting. Set it to TRUE to take the outline anyway.
environment: The environment mappings are resolved in.

Value

A ggplot object with squared residual polygons added.

Details

Draws squared residual polygons between observed points and the values predicted by a user-supplied function of x. Where gf_square_resid() measures a fitted model, this measures a function you wrote: it is called on the x values the plot draws, and each square is built on the residual from an observation to what the function predicts for it.

Examples

set.seed(1)
df <- data.frame(X = 1:10, Y = 2 + 3 * (1:10) + rnorm(10))
my_fun <- function(x) 2 + 3 * x

gf_point(Y ~ X, data = df) %>%
  gf_function(my_fun) %>%
  gf_square_resid_fun(my_fun, color = "red", alpha = 0.3)