geom_categorical_model: Regression model with one categorical explanatory/predictor...

In moderndive: Tidyverse-Friendly Introductory Linear Regression

Regression model with one categorical explanatory/predictor variable

Description

geom_categorical_model() fits a regression model using the categorical x axis as the explanatory variable, and visualizes the model's fitted values as piecewise horizontal line segments. Confidence interval bands can be included in the visualization of the model. Like geom_parallel_slopes(), this function has the same nature as geom_smooth() from the ggplot2 package, but provides functionality that geom_smooth() currently doesn't have. When using a categorical predictor variable, the intercept corresponds to the mean for the baseline group, while coefficients for the non-baseline groups are offsets from this baseline. Thus in the visualization the baseline for comparison group's median is marked with a solid line, whereas all offset groups' medians are marked with dashed lines.

Usage

geom_categorical_model(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  se = TRUE,
  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.
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().

See Also

geom_parallel_slopes()

Examples

library(dplyr)
library(ggplot2)

p <- ggplot(mpg, aes(x = drv, y = hwy)) +
  geom_point() +
  geom_categorical_model()
p

# In the above visualization, the solid line corresponds to the mean of 19.2
# for the baseline group "4", whereas the dashed lines correspond to the
# means of 28.19 and 21.02 for the non-baseline groups "f" and "r" respectively.
# In the corresponding regression table however the coefficients for "f" and "r"
# are presented as offsets from the mean for "4":
model <- lm(hwy ~ drv, data = mpg)
get_regression_table(model)

# You can use different colors for each categorical level
p %+% aes(color = drv)

# But mapping the color aesthetic doesn't change the model that is fit
p %+% aes(color = class)

moderndive documentation built on Dec. 2, 2022, 1:08 a.m.