Parallel slopes regression model

Source: R/geom_parallel_slopes.R

geom_parallel_slopes() fits parallel slopes model and adds its line output(s) to a ggplot object. Basically, it fits a unified model with intercepts varying between groups (which should be supplied as standard ggplot2 grouping aesthetics: group, color, fill, etc.). This function has the same nature as geom_smooth() from ggplot2 package, but provides functionality that geom_smooth() currently doesn't have.

geom_parallel_slopes(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  se = TRUE,
  formula = y ~ x,
  n = 100,
  fullrange = FALSE,
  level = 0.95,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

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)).

position

Position adjustment, either as a string naming the adjustment (e.g. "jitter" to use position_jitter), or the result of a call to a position adjustment function. Use the latter if you need to change the settings of the adjustment.

...

Other arguments passed on to layer(). These are often aesthetics, used to set an aesthetic to a fixed value, like colour = "red" or size = 3. They may also be parameters to the paired geom/stat.

se

Display confidence interval around model lines? TRUE by default.

formula

Formula to use per group in parallel slopes model. Basic linear y ~ x by default.

n

Number of points per group at which to evaluate model.

fullrange

If TRUE, the smoothing line gets expanded to the range of the plot, potentially beyond the data. This does not extend the line into any additional padding created by expansion.

level

Level of confidence interval to use (0.95 by default).

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

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().

Examples

library(dplyr)
library(ggplot2)

ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes(se = FALSE)


# Basic usage
ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes()

ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes(se = FALSE)


# Supply custom aesthetics
ggplot(evals, aes(x = age, y = score, color = ethnicity)) +
  geom_point() +
  geom_parallel_slopes(se = FALSE, size = 4)
#> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
#> ℹ Please use `linewidth` instead.


# Fit non-linear model
example_df <- house_prices %>%
  slice(1:1000) %>%
  mutate(
    log10_price = log10(price),
    log10_size = log10(sqft_living)
  )
ggplot(example_df, aes(x = log10_size, y = log10_price, color = condition)) +
  geom_point(alpha = 0.1) +
  geom_parallel_slopes(formula = y ~ poly(x, 2))


# Different grouping
ggplot(example_df, aes(x = log10_size, y = log10_price)) +
  geom_point(alpha = 0.1) +
  geom_parallel_slopes(aes(fill = condition))