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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, ...)
see ggplot
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.
see ggplot2
other arguments are passed to smoothing function
a data.frame with additional columns
predicted value
lowess
for loess
smoother, and histSpikeg
# NOT RUN {
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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