Automatically selects iter=0
for lowess
if y
is binary, otherwise uses iter=3
.
stat_plsmo(
mapping = NULL,
data = NULL,
geom = "smooth",
position = "identity",
n = 80,
fullrange = FALSE,
span = 2/3,
fun = function(x) x,
na.rm = FALSE,
show.legend = NA,
inherit.aes = TRUE,
...
)
a data.frame with additional columns
predicted value
see ggplot2 documentation
number of points to evaluate smoother at
should the fit span the full range of the plot, or just the data
see f
argument to lowess
a function to transform smoothed y
If FALSE
(the default), removes missing values with
a warning. If TRUE
silently removes missing values.
other arguments are passed to smoothing function
lowess
for loess
smoother.
# \donttest{
require(ggplot2)
c <- ggplot(mtcars, aes(qsec, wt))
c + stat_plsmo()
c + stat_plsmo() + geom_point()
c + stat_plsmo(span = 0.1) + geom_point()
# Smoothers for subsets
c <- ggplot(mtcars, aes(y=wt, x=mpg)) + facet_grid(. ~ cyl)
c + stat_plsmo() + geom_point()
c + stat_plsmo(fullrange = TRUE) + geom_point()
# Geoms and stats are automatically split by aesthetics that are factors
c <- ggplot(mtcars, aes(y=wt, x=mpg, colour=factor(cyl)))
c + stat_plsmo() + geom_point()
c + stat_plsmo(aes(fill = factor(cyl))) + geom_point()
c + stat_plsmo(fullrange=TRUE) + geom_point()
# Example with logistic regression
data("kyphosis", package="rpart")
qplot(Age, as.numeric(Kyphosis) - 1, data = kyphosis) + stat_plsmo()
# }
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