stat_poly_line() fits a polynomial, by default with
stats::lm(), but alternatively using robust regression or generalized
least squares. Predicted values and a confidence band, if possible, are
computed and, by default, plotted.
Usage
stat_poly_line(
mapping = NULL,
data = NULL,
geom = "smooth",
position = "identity",
...,
method = "lm",
formula = NULL,
se = NULL,
fit.seed = NA,
fm.values = FALSE,
n = 80,
fullrange = FALSE,
level = 0.95,
method.args = list(),
n.min = 2L,
na.rm = FALSE,
orientation = NA,
show.legend = NA,
inherit.aes = TRUE
)Arguments
- mapping
The aesthetic mapping, usually constructed with
aes. Only needs to be set at the layer level if you are overriding the plot defaults.- 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
layer. This can include aesthetics whose values you want to set, not map. Seelayerfor more details.- method
function or character If character, "lm", "rlm", "lqs". "gls" "ma", "sma", or the name of a model fit function are accepted, possibly followed by the fit function's
methodargument separated by a colon (e.g."rlm:M"). If a function is different tolm(),rlm(),lqs(),gls(),ma,sma, it must have formal parameters namedformula,data,weights, andmethod. See Details.- formula
a formula object. Using aesthetic names
xandyinstead of original variable names.- se
Display confidence interval around smooth? (`TRUE` by default only for fits with
lm()andrlm(), see `level` to control.)- fit.seed
RNG seed argument passed to
set.seed(). Defaults toNA, indicating thatset.seed()should not be called.- fm.values
logical Add metadata and parameter estimates extracted from the fitted model object;
FALSEby default.- n
Number of points at which to predict with the fitted model.
- fullrange
Should the fit span the full range of the plot, or just the range of the data group used in each fit?
- level
Level of confidence interval to use (0.95 by default).
- method.args
named list with additional arguments. Not
dataorweightswhich are always passed through aesthetic mappings.- n.min
integer Minimum number of distinct values in the explanatory variable (on the rhs of formula) for fitting to the attempted.
- 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
formula. The letter indicates the aesthetic considered the explanatory variable in the model fit.- show.legend
logical. Should this layer be included in the legends?
NA, the default, includes if any aesthetics are mapped.FALSEnever includes, andTRUEalways includes.- 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.
Value
The value returned by the statistic is a data frame, with n
rows of predicted values and their confidence limits. Optionally it will
also include additional values related to the model fit. When a
predict() method is not available for the fitted model class, the
value returned by calling fitted() is returned instead, with a
message.
Details
This statistic is similar to stat_smooth but
has different defaults and supports additional model fit functions. It also
interprets the argument passed to formula differently than
stat_smooth(), accepting y as explanatory variable and setting
orientation automatically. The default for method is
"lm" and spline-based smoothers like loess are not supported.
Other defaults are consistent with those in stat_poly_eq(),
stat_quant_line(), stat_quant_band(), stat_quant_eq(),
stat_ma_line(), stat_ma_eq(). As some model fitting functions
can depend on the RNG, fit.seed if different to NA is used
as argument in a call to set.seed() immediately
ahead of model fitting.
geom_poly_line() treats the x and y aesthetics differently and can
thus have two orientations. The orientation can be deduced from the argument
passed to formula. Thus, stat_poly_line() will by default guess
which orientation the layer should have. If no argument is passed to
formula, the formula defaults to y ~ x. For consistency with
stat_smooth orientation can be also specified directly
passing an argument to the orientation parameter, which can be either
"x" or "y". The value of orientation gives the axis that
is taken as the explanatory variable or x in the model formula.
Package 'ggpmisc' does not define new geometries matching the new statistics
as they are not needed and conceptually transformations of data are
statistics in the grammar of graphics.
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 and if the model results in a perfect fit due to low
number of observation, estimates for various parameters are NaN or
NA.
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
wise.
Note
Currently confidence bands for the regression band are not plotted in some cases, and in the case of MA and SMA model, it only displays the uncertainty of the slope.
Model fit methods supported
Several model fit functions are
supported explicitly, and some of their differences smoothed out. The
compatibility is checked late, based on the class of the returned fitted
model object. This makes it possible to use wrapper functions that do model
selection or other adjustments to the fit procedure on a per panel or per
group basis. In the case of fitted model objects of classes not explicitly
supported an attempt is made to find the usual accessors, and if found,
either complete or partial support frequently just works. The argument to
parameter method can be either the name of a function or a character
string giving the name. This approach makes it possible to support model
fit functions that are not dependencies of 'ggpmisc'. Either attach the
package where the function is defined and pass it by name or as string, or
use double colon notation when passing the name of the function.
Computed variables
`stat_poly_line()` provides the following variables, some of which depend on the orientation:
- y or x
predicted value
- ymin or xmin
lower pointwise confidence interval around the mean
- ymax or xmax
upper pointwise confidence interval around the mean
- se
standard error
If fm.values = TRUE is passed then columns based on the summary of
the model fit are added, with the same value in each row within a group.
This is wasteful and disabled by default, but provides a simple and robust
approach to achieve effects like colouring or hiding of the model fit line
based on P-values, r-squared, adjusted r-squared or the number of
observations.
Aesthetics
stat_poly_line 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
("geom_smooth" is the default) are understood and grouping
respected.
See also
Other ggplot statistics for linear and polynomial regression:
stat_poly_eq()
Examples
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
stat_poly_line()
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
stat_poly_line(formula = x ~ y)
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
stat_poly_line(formula = y ~ poly(x, 3))
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
stat_poly_line(formula = x ~ poly(y, 3))
# Smooths are automatically fit to each group (defined by categorical
# aesthetics or the group aesthetic) and for each facet.
ggplot(mpg, aes(displ, hwy, colour = class)) +
geom_point() +
stat_poly_line(se = FALSE)
ggplot(mpg, aes(displ, hwy)) +
geom_point() +
stat_poly_line() +
facet_wrap(~drv)
# Inspecting the returned data using geom_debug()
gginnards.installed <- requireNamespace("gginnards", quietly = TRUE)
if (gginnards.installed)
library(gginnards)
if (gginnards.installed)
ggplot(mpg, aes(displ, hwy)) +
stat_poly_line(geom = "debug")
#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (head):"
#> x y ymin ymax flipped_aes PANEL group orientation
#> 1 1.600000 30.04871 29.17768 30.91974 FALSE 1 -1 x
#> 2 1.668354 29.80738 28.95779 30.65696 FALSE 1 -1 x
#> 3 1.736709 29.56605 28.73763 30.39446 FALSE 1 -1 x
#> 4 1.805063 29.32471 28.51718 30.13225 FALSE 1 -1 x
#> 5 1.873418 29.08338 28.29640 29.87036 FALSE 1 -1 x
#> 6 1.941772 28.84205 28.07529 29.60882 FALSE 1 -1 x
if (gginnards.installed)
ggplot(mpg, aes(displ, hwy)) +
stat_poly_line(geom = "debug", fm.values = TRUE)
#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (head):"
#> x y ymin ymax p.value r.squared adj.r.squared n
#> 1 1.600000 30.04871 29.17768 30.91974 2.038974e-46 0.5867867 0.5850056 234
#> 2 1.668354 29.80738 28.95779 30.65696 2.038974e-46 0.5867867 0.5850056 234
#> 3 1.736709 29.56605 28.73763 30.39446 2.038974e-46 0.5867867 0.5850056 234
#> 4 1.805063 29.32471 28.51718 30.13225 2.038974e-46 0.5867867 0.5850056 234
#> 5 1.873418 29.08338 28.29640 29.87036 2.038974e-46 0.5867867 0.5850056 234
#> 6 1.941772 28.84205 28.07529 29.60882 2.038974e-46 0.5867867 0.5850056 234
#> fm.class fm.method fm.formula fm.formula.chr flipped_aes PANEL group
#> 1 lm lm:qr y ~ x y ~ x FALSE 1 -1
#> 2 lm lm:qr y ~ x y ~ x FALSE 1 -1
#> 3 lm lm:qr y ~ x y ~ x FALSE 1 -1
#> 4 lm lm:qr y ~ x y ~ x FALSE 1 -1
#> 5 lm lm:qr y ~ x y ~ x FALSE 1 -1
#> 6 lm lm:qr y ~ x y ~ x FALSE 1 -1
#> orientation
#> 1 x
#> 2 x
#> 3 x
#> 4 x
#> 5 x
#> 6 x
if (gginnards.installed)
ggplot(mpg, aes(displ, hwy)) +
stat_poly_line(geom = "debug", method = lm, fm.values = TRUE)
#> [1] "PANEL 1; group(s) -1; 'draw_function()' input 'data' (head):"
#> x y ymin ymax p.value r.squared adj.r.squared n
#> 1 1.600000 30.04871 29.17768 30.91974 2.038974e-46 0.5867867 0.5850056 234
#> 2 1.668354 29.80738 28.95779 30.65696 2.038974e-46 0.5867867 0.5850056 234
#> 3 1.736709 29.56605 28.73763 30.39446 2.038974e-46 0.5867867 0.5850056 234
#> 4 1.805063 29.32471 28.51718 30.13225 2.038974e-46 0.5867867 0.5850056 234
#> 5 1.873418 29.08338 28.29640 29.87036 2.038974e-46 0.5867867 0.5850056 234
#> 6 1.941772 28.84205 28.07529 29.60882 2.038974e-46 0.5867867 0.5850056 234
#> fm.class fm.method fm.formula fm.formula.chr flipped_aes PANEL group
#> 1 lm lm y ~ x y ~ x FALSE 1 -1
#> 2 lm lm y ~ x y ~ x FALSE 1 -1
#> 3 lm lm y ~ x y ~ x FALSE 1 -1
#> 4 lm lm y ~ x y ~ x FALSE 1 -1
#> 5 lm lm y ~ x y ~ x FALSE 1 -1
#> 6 lm lm y ~ x y ~ x FALSE 1 -1
#> orientation
#> 1 x
#> 2 x
#> 3 x
#> 4 x
#> 5 x
#> 6 x