Add Regression Line Equation and R-Square to a GGPLOT.

Source: R/stat_regline_equation.R

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,
  ...
)

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)).

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.

Computed variables

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.

References

the source code of the function stat_regline_equation() is inspired from the code of the function stat_poly_eq() (in ggpmisc package).

See also

ggscatter

Examples


# 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")