Details section of the help
Calculation is performed by the (currently undocumented) predictdf
generic function and its methods. For most methods the confidence
bounds are computed using the predict method - the exceptions are
loess which uses a t-based approximation, and for glm where the normal
confidence interval is constructed on the link scale, and then
back-transformed to the response scale.
So predictdf will generally call
stats::predict, which in turn will call the correct
predict method for the smoothing method. Other functions involving stat_smooth are also useful to consider.
Most model fitting functions will have
predict method associated with the
class of the model. These will usually take a
newdata object and an argument
se.fit that will denote whether the standard errors will be fitted. (see
?predict) for further details.
display confidence interval around smooth? (TRUE by default, see level to control
This is passed directy to the predict method to return the appropriate standard errors (method dependant)
should the fit span the full range of the plot, or just the data
This defines the
newdata values for
x at which the predictions will be evaluated
level of confidence interval to use (0.95 by default)
Passed directly to the predict method so that the confidence interval can define the appropriate critical value (eg
qt((1 - level)/2, df) for the standard errors to be multiplied by
number of points to evaluate smoother at
Used in conjunction with
fullrange to define the
x values in the
Within a call to
stat_smooth you can define
se which is what is partially matched to
se), and will define the
interval argument if necessary.
level will give level of the confidence interval (defaults 0.95).
newdata object is defined within the processing, depending on your setting of
fullrange to a sequence of length
n within the full range of the plot or the data.
In your case, using
rlm, this will use
predict.rlm, which is defined as
predict.rlm <- function (object, newdata = NULL, scale = NULL, ...)
## problems with using predict.lm are the scale and
## the QR decomp which has been done on down-weighted values.
object$qr <- qr(sqrt(object$weights) * object$x)
predict.lm(object, newdata = newdata, scale = object$s, ...)
So it is internally calling
predict.lm with an appropriate scaling of the
qr decomposition and