stat_poly_eq | R Documentation |
R^2
, AIC and BIC of fitted polynomialstat_poly_eq
fits a polynomial by default with stats::lm()
but
alternatively using robust regression. From the fitted model it
generates several labels including the equation, p-value, F-value,
coefficient of determination (R^2), 'AIC', 'BIC', and number of observations.
stat_poly_eq(
mapping = NULL,
data = NULL,
geom = "text_npc",
position = "identity",
...,
formula = NULL,
method = "lm",
method.args = list(),
n.min = 2L,
eq.with.lhs = TRUE,
eq.x.rhs = NULL,
small.r = FALSE,
small.p = FALSE,
CI.brackets = c("[", "]"),
rsquared.conf.level = 0.95,
coef.digits = 3,
coef.keep.zeros = TRUE,
rr.digits = 2,
f.digits = 3,
p.digits = 3,
label.x = "left",
label.y = "top",
label.x.npc = NULL,
label.y.npc = NULL,
hstep = 0,
vstep = NULL,
output.type = NULL,
na.rm = FALSE,
orientation = NA,
parse = NULL,
show.legend = FALSE,
inherit.aes = TRUE
)
mapping |
The aesthetic mapping, usually constructed with
|
data |
A layer specific dataset, only needed if you want to override the plot defaults. |
geom |
The geometric object to use display the data |
position |
The position adjustment to use for overlapping points on this layer |
... |
other arguments passed on to |
formula |
a formula object. Using aesthetic names |
method |
function or character If character, "lm", "rlm" or the name of
a model fit function are accepted, possibly followed by the fit function's
|
method.args |
named list with additional arguments. |
n.min |
integer Minimum number of distinct values in the explanatory variable (on the rhs of formula) for fitting to the attempted. |
eq.with.lhs |
If |
eq.x.rhs |
|
small.r, small.p |
logical Flags to switch use of lower case r and p for coefficient of determination and p-value. |
CI.brackets |
character vector of length 2. The opening and closing brackets used for the CI label. |
rsquared.conf.level |
numeric Confidence level for the returned confidence interval. Set to NA to skip CI computation. |
coef.digits, f.digits |
integer Number of significant digits to use for the fitted coefficients and F-value. |
coef.keep.zeros |
logical Keep or drop trailing zeros when formatting the fitted coefficients and F-value. |
rr.digits, p.digits |
integer Number of digits after the decimal point to
use for |
label.x, label.y |
|
label.x.npc, label.y.npc |
|
hstep, vstep |
numeric in npc units, the horizontal and vertical step used between labels for different groups. |
output.type |
character One of "expression", "LaTeX", "text", "markdown" or "numeric". |
na.rm |
a logical indicating whether NA values should be stripped before the computation proceeds. |
orientation |
character Either "x" or "y" controlling the default for
|
parse |
logical Passed to the geom. If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
This statistic can be used to automatically annotate a plot with
R^2
, adjusted R^2
or the fitted model equation. It supports
linear regression, robust linear regression and median regression fitted
with functions lm
, or rlm
. The R^2
and adjusted R^2
annotations can be used with any linear model
formula. The confidence interval for R^2
is computed with function
ci_rsquared
from package 'confintr'. The fitted
equation label is correctly generated for polynomials or quasi-polynomials
through the origin. Model formulas can use poly()
or be defined
algebraically with terms of powers of increasing magnitude with no missing
intermediate terms, except possibly for the intercept indicated by "- 1" or
"-1" or "+ 0"
in the formula. The validity of the formula
is
not checked in the current implementation, and for this reason the default
aesthetics sets R^2
as label for the annotation. This statistic
generates labels as R expressions by default but LaTeX (use TikZ device),
markdown (use package 'ggtext') and plain text are also supported, as well
as numeric values for user-generated text labels. The value of parse
is set automatically based on output-type
, but if you assemble
labels that need parsing from numeric
output, the default needs to
be overridden. This stat only generates annotation labels, the predicted
values/line need to be added to the plot as a separate layer using
stat_poly_line
(or stat_smooth
), if
the default formula is overriden with an argument, it is crucial to make
sure that the same model formula is used in all layers. In this case it is
best to save the formula as an object and supply this object as argument to
the different statistics.
A ggplot statistic receives as data
a data frame that is not the one
passed as argument by the user, but instead a data frame with the variables
mapped to aesthetics. stat_poly_eq()
mimics how stat_smooth()
works, except that only polynomials can be fitted. Similarly to these
statistics the model fits respect grouping, so the scales used for x
and y
should both be continuous scales rather than discrete.
With method "lm"
, singularity results in terms being dropped with a
message if more numerous than can be fitted with a singular (exact) fit.
In this case or if the model results in a perfect fit due to a low
number of observations, estimates for various parameters are NaN
or
NA
. When this is the case the corresponding labels are set to
character(0L)
and thus not visble in the plot.
With methods other than "lm"
, the model fit functions simply fail
in case of singularity, e.g., singular fits are not implemented in
"rlm"
.
In both cases the minimum number of observations with distinct values in
the explanatory variable can be set through parameter n.min
. The
default n.min = 2L
is the smallest suitable for method "lm"
but too small for method "rlm"
for which n.min = 3L
is
needed. Anyway, model fits with very few observations are of little
interest and using larger values of n.min
than the default is
usually wise.
A data frame, with a single row and columns as described under
Computed variables. In cases when the number of observations is
less than n.min
a data frame with no rows or columns is returned
rendered as an empty/invisible plot layer.
stat_poly_eq()
understands x
and y
,
to be referenced in the formula
and weight
passed as argument
to parameter weights
. All three must be mapped to numeric
variables. In addition, the aesthetics understood by the geom
("text"
is the default) are understood and grouping respected.
If the model formula includes a transformation of x
, a
matching argument should be passed to parameter eq.x.rhs
as its default value "x"
will not reflect the applied
transformation. In plots, transformation should never be applied to the
left hand side of the model formula, but instead in the mapping of the
variable within aes
, as otherwise plotted observations and fitted
curve will not match. In this case it may be necessary to also pass
a matching argument to parameter eq.with.lhs
.
If output.type different from "numeric"
the returned tibble contains
columns listed below. If the model fit function used does not return a value,
the label is set to character(0L)
.
x position
y position
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
Confidence interval for R^2
of the fitted model as a character string to be parsed
F value and degrees of freedom for the fitted model as a whole.
P-value for the F-value above.
AIC for the fitted model.
BIC for the fitted model.
Number of observations used in the fit.
Set according to mapping in aes
.
Set according method
used.
numeric values, from the model fit object
If output.type is "numeric"
the returned tibble contains columns
listed below. If the model fit function used does not return a value,
the variable is set to NA_real_
.
x position
y position
list containing the "coefficients" matrix from the summary of the fit object
numeric values, from the model fit object
Set according to mapping in aes
.
TRUE is polynomial is forced through the origin
One or columns with the coefficient estimates
To explore the computed values returned for a given input we suggest the use
of geom_debug
as shown in the last examples below.
stat_regline_equation()
in package 'ggpubr' is
a renamed but almost unchanged copy of stat_poly_eq()
taken from an
old version of this package (without acknowledgement of source and
authorship). stat_regline_equation()
lacks important functionality
and contains bugs that have been fixed in stat_poly_eq()
.
For backward compatibility a logical is accepted as argument for
eq.with.lhs
. If TRUE
, the default is used, either
"x"
or "y"
, depending on the argument passed to formula
.
However, "x"
or "y"
can be substituted by providing a
suitable replacement character string through eq.x.rhs
.
Parameter orientation
is redundant as it only affects the default
for formula
but is included for consistency with
ggplot2::stat_smooth()
.
R option OutDec
is obeyed based on its value at the time the plot
is rendered, i.e., displayed or printed. Set options(OutDec = ",")
for languages like Spanish or French.
Originally written as an answer to question 7549694 at Stackoverflow but enhanced based on suggestions from users and my own needs.
This statistics fits a model with function lm
,
function rlm
or a user supplied function returning an
object of class "lm"
. Consult the documentation of these functions
for the details and additional arguments that can be passed to them by name
through parameter method.args
.
This stat_poly_eq
statistic can return ready formatted labels
depending on the argument passed to output.type
. This is possible
because only polynomial and quasy-polynomial models are supported. For
quantile regression stat_quant_eq
should be used instead of
stat_poly_eq
while for model II or major axis regression
stat_ma_eq
should be used. For other types of models such as
non-linear models, statistics stat_fit_glance
and
stat_fit_tidy
should be used and the code for construction of
character strings from numeric values and their mapping to aesthetic
label
needs to be explicitly supplied by the user.
Other ggplot statistics for linear and polynomial regression:
stat_poly_line()
# generate artificial data
set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
y <- y / max(y)
my.data <- data.frame(x = x, y = y,
group = c("A", "B"),
y2 = y * c(1, 2) + c(0, 0.1),
w = sqrt(x))
# give a name to a formula
formula <- y ~ poly(x, 3, raw = TRUE)
# using defaults
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line() +
stat_poly_eq()
# no weights
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula)
# other labels
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(use_label("eq"), formula = formula)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(use_label(c("eq", "R2")), formula = formula)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(use_label(c("R2", "R2.CI", "P", "method")), formula = formula)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(use_label(c("R2", "F", "P", "n"), sep = "*\"; \"*"),
formula = formula)
# grouping
ggplot(my.data, aes(x, y2, color = group)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula)
# rotation
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, angle = 90)
# label location
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, label.y = "bottom", label.x = "right")
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, label.y = 0.1, label.x = 0.9)
# modifying the explanatory variable within the model formula
# modifying the response variable within aes()
formula.trans <- y ~ I(x^2)
ggplot(my.data, aes(x, y + 1)) +
geom_point() +
stat_poly_line(formula = formula.trans) +
stat_poly_eq(use_label("eq"),
formula = formula.trans,
eq.x.rhs = "~x^2",
eq.with.lhs = "y + 1~~`=`~~")
# using weights
ggplot(my.data, aes(x, y, weight = w)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula)
# no weights, 4 digits for R square
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, rr.digits = 4)
# manually assemble and map a specific label using paste() and aes()
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(aes(label = paste(after_stat(rr.label),
after_stat(n.label), sep = "*\", \"*")),
formula = formula)
# manually assemble and map a specific label using sprintf() and aes()
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(aes(label = sprintf("%s*\" with \"*%s*\" and \"*%s",
after_stat(rr.label),
after_stat(f.value.label),
after_stat(p.value.label))),
formula = formula)
# x on y regression
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula, orientation = "y") +
stat_poly_eq(use_label(c("eq", "adj.R2")),
formula = x ~ poly(y, 3, raw = TRUE))
# conditional user specified label
ggplot(my.data, aes(x, y2, color = group)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(aes(label = ifelse(after_stat(adj.r.squared) > 0.96,
paste(after_stat(adj.rr.label),
after_stat(eq.label),
sep = "*\", \"*"),
after_stat(adj.rr.label))),
rr.digits = 3,
formula = formula)
# geom = "text"
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(geom = "text", label.x = 100, label.y = 0, hjust = 1,
formula = formula)
# using numeric values
# Here we use columns b_0 ... b_3 for the coefficient estimates
my.format <-
"b[0]~`=`~%.3g*\", \"*b[1]~`=`~%.3g*\", \"*b[2]~`=`~%.3g*\", \"*b[3]~`=`~%.3g"
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula,
output.type = "numeric",
parse = TRUE,
mapping =
aes(label = sprintf(my.format,
after_stat(b_0), after_stat(b_1),
after_stat(b_2), after_stat(b_3))))
# Inspecting the returned data using geom_debug()
# This provides a quick way of finding out the names of the variables that
# are available for mapping to aesthetics with after_stat().
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
if (gginnards.installed)
library(gginnards)
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, geom = "debug")
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, geom = "debug", output.type = "numeric")
# names of the variables
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, geom = "debug",
summary.fun = colnames)
# only data$eq.label
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, geom = "debug",
output.type = "expression",
summary.fun = function(x) {x[["eq.label"]]})
# only data$eq.label
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(aes(label = after_stat(eq.label)),
formula = formula, geom = "debug",
output.type = "markdown",
summary.fun = function(x) {x[["eq.label"]]})
# only data$eq.label
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, geom = "debug",
output.type = "latex",
summary.fun = function(x) {x[["eq.label"]]})
# only data$eq.label
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, geom = "debug",
output.type = "text",
summary.fun = function(x) {x[["eq.label"]]})
# show the content of a list column
if (gginnards.installed)
ggplot(my.data, aes(x, y)) +
geom_point() +
stat_poly_line(formula = formula) +
stat_poly_eq(formula = formula, geom = "debug", output.type = "numeric",
summary.fun = function(x) {x[["coef.ls"]][[1]]})
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