Adds linear model fits to plots. geom_lm()
and stat_lm()
are essentially
equivalent. Use geom_lm()
unless you want a non-standard geom.
stat_lm(
mapping = NULL,
data = NULL,
geom = "lm",
position = "identity",
interval = c("none", "prediction", "confidence"),
level = 0.95,
formula = y ~ x,
lm.args = list(),
backtrans = identity,
...,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
geom_lm(
mapping = NULL,
data = NULL,
stat = "lm",
position = "identity",
interval = c("none", "prediction", "confidence"),
level = 0.95,
formula = y ~ x,
lm.args = list(),
backtrans = identity,
...,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE
)
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.
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)
).
Use to override the default connection between
geom_lm
and stat_lm
.
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.
One of "none"
, "confidence"
or "prediction"
.
The level used for confidence or prediction intervals
a formula describing the model in terms of y
(response)
and x
(predictor).
A list of arguments supplied to lm()
when performing the fit.
a function that transforms the response back to
the original scale when the formula
includes a transformation on
y
.
Other arguments passed on to layer()
. These are
often aesthetics, used to set an aesthetic to a fixed value, like
colour = "red"
or size = 3
. They may also be parameters
to the paired geom/stat.
If FALSE
, the default, missing values are removed with
a warning. If TRUE
, missing values are silently removed.
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()
.
Stat calculation is performed by the (currently undocumented)
predictdf
. Pointwise confidence or prediction bands are
calculated using the predict()
method.
lm()
for details on linear model fitting.
ggplot(data = mosaicData::KidsFeet, aes(y = length, x = width, color = sex)) +
geom_lm() +
geom_point()
#> Warning: Using the `size` aesthetic with geom_line was deprecated in ggplot2 3.4.0.
#> ℹ Please use the `linewidth` aesthetic instead.
ggplot(data = mosaicData::KidsFeet, aes(y = length, x = width, color = sex)) +
geom_lm(interval = "prediction", color = "skyblue") +
geom_lm(interval = "confidence") +
geom_point() +
facet_wrap(~sex)
#> Warning: Using the `size` aesthetic with geom_ribbon was deprecated in ggplot2 3.4.0.
#> ℹ Please use the `linewidth` aesthetic instead.
# non-standard display
ggplot(data = mosaicData::KidsFeet, aes(y = length, x = width, color = sex)) +
stat_lm(aes(fill = sex),
color = NA, interval = "confidence", geom = "ribbon",
alpha = 0.2
) +
geom_point() +
facet_wrap(~sex)
ggplot(mpg, aes(displ, hwy)) +
geom_lm(
formula = log(y) ~ poly(x, 3), backtrans = exp,
interval = "prediction", fill = "skyblue"
) +
geom_lm(
formula = log(y) ~ poly(x, 3), backtrans = exp, interval = "confidence",
color = "red"
) +
geom_point()