Add Squared Reduction Visualization to a Plot

gf_squareduce() is a fully supported alias of gf_square_reduce(), named the way the classroom that asked for it says it.

Usage

gf_square_reduce(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  model,
  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()
)

gf_squareduce(
  object = NULL,
  gformula = NULL,
  data = NULL,
  ...,
  model,
  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_reduce() measures a model, not an aesthetic formula; a model given positionally lands here and is moved to model.

data

Not used. The reductions 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.

model

A model already fit by lm() or aov(). The plot supplies the observations' position on the other axis; the model supplies what it predicted for each of them. May be given positionally or as model =. A fit without an intercept, or one fit with weights, is refused: a reduction is only a reduction because total, error and reduction add up, and that identity is what an unweighted intercept guarantees. gf_resid() measures either of those fits happily, needing no such identity.

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 reduction is drawn by its own geom and stat, with a jitter that holds the outcome axis still so its squares start at the grand mean without floating off it, while jittering the other axis exactly the points layer's own jitter did.

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_reduce() is TRUE – see gf_square_resid() for why: a square is a filled region drawn in the geom's own colors, and inheriting a plot's mapped color would outline every square in the color of the group it measures instead of leaving one neutral area per observation. Set it to TRUE to take the outline anyway.

environment

The environment mappings are resolved in.

Value

A ggplot object with squared reduction polygons added.

Details

Draws squared reduction polygons between the grand mean and the values a fitted model predicts, so the model's share of the sum of squares is an area you can see. The square is built on the reduction itself and turns with it: a model of the variable the plot puts on x squares the horizontal distance. Its side is scaled to stay square on the page rather than in data units.

aspect belongs to all three square layers or to none of them. The squared reduction plus the squared residual equaling the squared total is a claim about areas on the page, and it only holds while gf_square_resid(), gf_square_reduce() and any squared total drawn alongside them all read the same aspect.

Examples

set.seed(1)
penguins_20 <- sample(penguins, 20)

# two squares of one decomposition: the squared residual (firebrick) and the
# squared reduction (blue), both reading the same aspect so the areas mean
# what they say
flipper_model <- lm(body_mass_kg ~ flipper_length_m, data = penguins_20)
gf_point(body_mass_kg ~ flipper_length_m, data = penguins_20) %>%
  gf_model(flipper_model) %>%
  gf_square_resid(flipper_model, color = "firebrick") %>%
  gf_square_reduce(flipper_model, color = "blue")


# and for a two-group model on a jitter plot
gentoo_model <- lm(body_mass_kg ~ gentoo, data = penguins_20)
gf_jitter(body_mass_kg ~ gentoo, data = penguins_20, width = .1) %>%
  gf_model(gentoo_model) %>%
  gf_square_reduce(gentoo_model, color = "blue")