`R/stat_regline_equation.R`

`stat_regline_equation.Rd`

Add regression line equation and R^2 to a ggplot. Regression
model is fitted using the function `lm`

.

```
stat_regline_equation(
mapping = NULL,
data = NULL,
formula = y ~ x,
label.x.npc = "left",
label.y.npc = "top",
label.x = NULL,
label.y = NULL,
output.type = "expression",
geom = "text",
position = "identity",
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
...
)
```

- 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)`

).- formula
a formula object

- label.x.npc, label.y.npc
can be

`numeric`

or`character`

vector of the same length as the number of groups and/or panels. If too short they will be recycled.If

`numeric`

, value should be between 0 and 1. Coordinates to be used for positioning the label, expressed in "normalized parent coordinates".If

`character`

, allowed values include: i) one of c('right', 'left', 'center', 'centre', 'middle') for x-axis; ii) and one of c( 'bottom', 'top', 'center', 'centre', 'middle') for y-axis.

If too short they will be recycled.

- label.x, label.y
`numeric`

Coordinates (in data units) to be used for absolute positioning of the label. If too short they will be recycled.- output.type
character One of "expression", "latex" or "text".

- geom
The geometric object to use to display the data, either as a

`ggproto`

`Geom`

subclass or as a string naming the geom stripped of the`geom_`

prefix (e.g.`"point"`

rather than`"geom_point"`

)- 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.- na.rm
If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.

- 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()`

.- ...
other arguments to pass to

`geom_text`

or`geom_label`

.

- x
x position for left edge

- y
y position near upper edge

- eq.label
equation for the fitted polynomial as a character string to be parsed

- rr.label
\(R^2\) of the fitted model as a character string to be parsed

- adj.rr.label
Adjusted \(R^2\) of the fitted model as a character string to be parsed

- AIC.label
AIC for the fitted model.

- BIC.label
BIC for the fitted model.

- hjust
Set to zero to override the default of the "text" geom.

the source code of the function `stat_regline_equation()`

is
inspired from the code of the function `stat_poly_eq()`

(in ggpmisc
package).

```
# Simple scatter plot with correlation coefficient and
# regression line
#::::::::::::::::::::::::::::::::::::::::::::::::::::
ggscatter(mtcars, x = "wt", y = "mpg", add = "reg.line") +
stat_cor(label.x = 3, label.y = 34) +
stat_regline_equation(label.x = 3, label.y = 32)
# Groupped scatter plot
#::::::::::::::::::::::::::::::::::::::::::::::::::::
ggscatter(
iris, x = "Sepal.Length", y = "Sepal.Width",
color = "Species", palette = "jco",
add = "reg.line"
) +
facet_wrap(~Species) +
stat_cor(label.y = 4.4) +
stat_regline_equation(label.y = 4.2)
# Polynomial equation
#::::::::::::::::::::::::::::::::::::::::::::::::::::
# Demo data
set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x, y, group = c("A", "B"),
y2 = y * c(0.5,2), block = c("a", "a", "b", "b"))
# Fit polynomial regression line and add labels
formula <- y ~ poly(x, 3, raw = TRUE)
p <- ggplot(my.data, aes(x, y2, color = group)) +
geom_point() +
stat_smooth(aes(fill = group, color = group), method = "lm", formula = formula) +
stat_regline_equation(
aes(label = paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")),
formula = formula
) +
theme_bw()
ggpar(p, palette = "jco")
```