geom_smooth {ggplot2} | R Documentation |
Add a smoothed condition mean.
geom_smooth(mapping=NULL, data=NULL, stat="smooth", position="identity", ...)
mapping |
mapping between variables and aesthetics generated by aes |
data |
dataset used in this layer, if not specified uses plot dataset |
stat |
statistic used by this layer |
position |
position adjustment used by this layer |
... |
ignored |
This page describes geom_smooth, see layer
and qplot
for how to create a complete plot from individual components.
A layer
The following aesthetics can be used with geom_smooth. Aesthetics are mapped to variables in the data with the aes
function: geom\_smooth(\code{aes}(x = var))
x
: x position (required)
y
: y position (required)
colour
: border colour
fill
: internal colour
size
: size
linetype
: line type
weight
: observation weight used in statistical transformation
alpha
: NULL
Hadley Wickham, http://had.co.nz/
## Not run: # See stat_smooth for examples of using built in model fitting # if you need some more flexible, this example shows you how to # plot the fits from any model of your choosing library(ggplot2) qplot(wt, mpg, data=mtcars, colour=factor(cyl)) model <- lm(mpg ~ wt + factor(cyl), data=mtcars) grid <- with(mtcars, expand.grid( wt = seq(min(wt), max(wt), length = 20), cyl = levels(factor(cyl)) )) grid$mpg <- stats::predict(model, newdata=grid) qplot(wt, mpg, data=mtcars, colour=factor(cyl)) + geom_line(data=grid) # or with standard errors err <- stats::predict(model, newdata=grid, se = TRUE) grid$ucl <- err$fit + 1.96 * err$se.fit grid$lcl <- err$fit - 1.96 * err$se.fit qplot(wt, mpg, data=mtcars, colour=factor(cyl)) + geom_smooth(aes(min=lcl, max=ucl), data=grid, stat="identity") ## End(Not run)