stat_lm  R Documentation 
Adds linear model fits to plots. geom_lm()
and stat_lm()
are essentially
equivalent. Use geom_lm()
unless you want a nonstandard 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
)
mapping 
Set of aesthetic mappings created by 
data 
The data to be displayed in this layer. There are three options: If A A 
geom, stat 
Use to override the default connection between

position 
Position adjustment, either as a string naming the adjustment
(e.g. 
interval 
One of 
level 
The level used for confidence or prediction intervals 
formula 
a formula describing the model in terms of 
lm.args 
A list of arguments supplied to 
backtrans 
a function that transforms the response back to
the original scale when the 
... 
Other arguments passed on to 
na.rm 
If 
show.legend 
logical. Should this layer be included in the legends?

inherit.aes 
If 
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()
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)
# nonstandard 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()
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