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 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.
geom_categorical_model(
mapping = NULL,
data = NULL,
position = "identity",
...,
se = TRUE,
level = 0.95,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
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.
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 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.
Display confidence interval around model lines? TRUE
by
default.
Level of confidence interval to use (0.95 by default).
If FALSE
, the default, missing values are removed with
a warning. If TRUE
, missing values are silently removed.
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.
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()
.
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)