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Regression model with one categorical explanatory/predictor variable

Source: R/geom_categorical_model.R
geom_categorical_model.Rd

geom_categorical_model() fits a regression model using the categorical x axis as the explanatory variable, and visualizes the model's fitted values as piece-wise 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

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.

...

Other arguments passed on to layer()'s params argument. These arguments broadly fall into one of 4 categories below. Notably, further arguments to the position argument, or aesthetics that are required can not be passed through .... Unknown arguments that are not part of the 4 categories below are ignored.

  • Static aesthetics that are not mapped to a scale, but are at a fixed value and apply to the layer as a whole. For example, colour = "red" or linewidth = 3. The geom's documentation has an Aesthetics section that lists the available options. The 'required' aesthetics cannot be passed on to the params. Please note that while passing unmapped aesthetics as vectors is technically possible, the order and required length is not guaranteed to be parallel to the input data.

  • When constructing a layer using a stat_*() function, the ... argument can be used to pass on parameters to the geom part of the layer. An example of this is stat_density(geom = "area", outline.type = "both"). The geom's documentation lists which parameters it can accept.

  • Inversely, when constructing a layer using a geom_*() function, the ... argument can be used to pass on parameters to the stat part of the layer. An example of this is geom_area(stat = "density", adjust = 0.5). The stat's documentation lists which parameters it can accept.

  • The key_glyph argument of layer() may also be passed on through .... This can be one of the functions described as key glyphs, to change the display of the layer in the legend.

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)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
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)
#> # A tibble: 3 × 7
#>   term      estimate std_error statistic p_value lower_ci upper_ci
#>   <chr>        <dbl>     <dbl>     <dbl>   <dbl>    <dbl>    <dbl>
#> 1 intercept    19.2      0.404     47.5    0       18.4      20.0 
#> 2 drv: f        8.99     0.567     15.9    0        7.87     10.1 
#> 3 drv: r        1.82     0.913      2.00   0.047    0.026     3.62

# 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)