Add a lowess smoother without counfidence bands.
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, ... )
mapping, data, geom, position, show.legend, inherit.aes
: see ggplot2 documentationn
: number of points to evaluate smoother atfullrange
: should the fit span the full range of the plot, or just the dataspan
: see f
argument to lowess
fun
: a function to transform smoothed y
na.rm
: If FALSE
(the default), removes missing values with a warning. If TRUE
silently removes missing values....
: other arguments are passed to smoothing functiona data.frame with additional columns - y: predicted value
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()
lowess
for loess
smoother.