stat_rule: Construct limited rules offset from the origin
In corybrunson/ordr: A Tidyverse Extension for Ordinations and Biplots

stat_rule

R Documentation

Construct limited rules offset from the origin

Description

Determine axis limits and offset vectors from reference data.

Usage

stat_rule(
  mapping = NULL,
  data = NULL,
  geom = "rule",
  position = "identity",
  fun.lower = "minpp",
  fun.upper = "maxpp",
  fun.offset = "minabspp",
  fun.args = list(),
  referent = NULL,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

minpp(x, p = 0.1)

maxpp(x, p = 0.1)

minabspp(x, p = 0.1)

Arguments

`mapping`	Set of aesthetic mappings created by `aes()`. If specified and `inherit.aes = TRUE` (the default), it is combined with the default mapping at the top level of the plot. You must supply `mapping` if there is no plot mapping.
`data`	The data to be displayed in this layer. There are three options: If `NULL`, the default, the data is inherited from the plot data as specified in the call to `ggplot()`. A `data.frame`, or other object, will override the plot data. All objects will be fortified to produce a data frame. See `fortify()` for which variables will be created. A `function` will be called with a single argument, the plot data. The return value must be a `data.frame`, and will be used as the layer data. A `function` can be created from a `formula` (e.g. `~ head(.x, 10)`).
`geom`	The geometric object to use to display the data for this layer. When using a `⁠stat_*()⁠` function to construct a layer, the `geom` argument can be used to override the default coupling between stats and geoms. The `geom` argument accepts the following: A `Geom` ggproto subclass, for example `GeomPoint`. A string naming the geom. To give the geom as a string, strip the function name of the `geom_` prefix. For example, to use `geom_point()`, give the geom as `"point"`. For more information and other ways to specify the geom, see the layer geom documentation.
`position`	A position adjustment to use on the data for this layer. This can be used in various ways, including to prevent overplotting and improving the display. The `position` argument accepts the following: The result of calling a position function, such as `position_jitter()`. This method allows for passing extra arguments to the position. A string naming the position adjustment. To give the position as a string, strip the function name of the `position_` prefix. For example, to use `position_jitter()`, give the position as `"jitter"`. For more information and other ways to specify the position, see the layer position documentation.
`fun.lower`, `fun.upper`, `fun.offset`	Functions used to determine the limits of the rules and the translations of the axes from the projections of `referent` onto the axes and onto their normal vectors.
`fun.args`	Optional additional arguments passed on to the functions.
`referent`	The reference data set; see Details.
`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. It can also be a named logical vector to finely select the aesthetics to display.
`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 shouldn't inherit behaviour from the default plot specification, e.g. `borders()`.
`...`	Additional arguments passed to `ggplot2::layer()`.
`x`	A numeric vector.
`p`	A numeric value; the proportion of a range used as a buffer.

Details

Biplots with several axes can become cluttered and illegible. When this happens, Gower, Gardner–Lubbe, & le Roux (2011) recommend to translate the axes to a new point of intersection away from the origin, adjusting the axis markers accordingly. Then the axes converge in a region of the plot offset from most position markers or other elements. An alternative solution, implemented in the bipl5 package (https://github.com/RuanBuys/bipl5), is to translate each axis orthogonally away from the origin, which preserves the axis markers. This is the technique implemented here.

Separately, axes that fill the plotting window are uninformative when they exceed the range of the plotted position markers projected onto them. They may even be misinformative, suggesting that linear relationships extrapolate outside the data range. In these cases, Gower and Harding (1988) recommend using finite ranges determined by the data projection onto each axis.

Three functions control these operations: fun.offset computes the orthogonal distance of each axis from the origin, and fun.lower and fun.upper compute the distance along each axis of the endpoints to the (offset) origin. Both functions depend on what position data is to be offset from or limited to, which must be passed manually to the referent parameter.

Value

A ggproto layer.

Referential stats

This statistical transformation is done with respect to reference data passed to referent (ignored if NULL, the default, possibly resulting in empty output). See stat_referent() for more details. This relies on a sleight of hand through a new undocumented LayerRef class and associated ggplot2::ggplot_add() method. As a result, only layers constructed using this ⁠stat_*()⁠ shortcut will pass the necessary positional aesthetics to the ⁠$setup_params()⁠ step, making them available to pre-process referent data.

The biplot shortcuts automatically substitute the complementary matrix factor for referent = NULL and will use an integer vector to select a subset from this factor. These uses do not require the mapping passage.

Biplot layers

ggbiplot() uses ggplot2::fortify() internally to produce a single data frame with a .matrix column distinguishing the subjects ("rows") and variables ("cols"). The stat layers stat_rows() and stat_cols() simply filter the data frame to one of these two.

The geom layers ⁠geom_rows_*()⁠ and ⁠geom_cols_*()⁠ call the corresponding stat in order to render plot elements for the corresponding factor matrix. ⁠geom_dims_*()⁠ selects a default matrix based on common practice, e.g. points for rows and arrows for columns.

Computed variables

These are calculated during the statistical transformation and can be accessed with delayed evaluation.

axis: unique axis identifier (integer)
⁠lower,upper⁠: distances to endpoints from origin (before offset)
⁠yintercept,xintercept⁠: intercepts (possibly Inf) of offset axis

References

Gower JC, Gardner–Lubbe S, & le Roux NJ (2011) Understanding Biplots. Wiley, ISBN: 978-0-470-01255-0. https://www.wiley.com/go/biplots

Gower JC & Harding SA (1988) "Nonlinear biplots". Biometrika 75(3): 445–455. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/75.3.445")}

Examples

# stack loss gradient
stackloss |> 
  lm(formula = stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.) |> 
  coef() |> 
  as.list() |> as.data.frame() |> 
  subset(select = c(Air.Flow, Water.Temp, Acid.Conc.)) ->
  coef_data
# gradient rule with respect to two predictors
stackloss_centered <- scale(stackloss, scale = FALSE)
stackloss_centered |> 
  ggplot(aes(x = Acid.Conc., y = Air.Flow)) +
  coord_square() + geom_origin() +
  geom_point(aes(size = stack.loss, alpha = sign(stack.loss))) + 
  scale_size_area() + scale_alpha_binned(breaks = c(-1, 0, 1)) +
  stat_rule(
    geom = "axis",
    data = coef_data,
    referent = stackloss_centered,
    fun.offset = \(x) minabspp(x, p = .5)
  )

corybrunson/ordr documentation built on Feb. 24, 2025, 6:34 a.m.