ggpubr (version 0.4.0)

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


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


  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,



Set of aesthetic mappings created by aes() or 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)).


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.


character One of "expression", "latex" or "text".


The geometric object to use display the data


Position adjustment, either as a string, or the result of a call to a position adjustment function.


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


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().


other arguments to pass to geom_text or geom_label.

Computed variables


x position for left edge


y position near upper edge


equation for the fitted polynomial as a character string to be parsed


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


Adjusted \(R^2\) of the fitted model as a character string to be parsed


AIC for the fitted model.


BIC for the fitted model.


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).

See Also



Run this code
# 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
  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
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4) <- 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(, aes(x, y2, color = group)) +
  geom_point() +
  stat_smooth(aes(fill = group, color = group), method = "lm", formula = formula) +
    aes(label =  paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")),
    formula = formula
  ) +
ggpar(p, palette = "jco")

# }

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