loess.boot {simpleboot} | R Documentation |
Bootstrapping of loess fits produced by the loess
function in
the modreg
package. Bootstrapping can be done by resampling
rows from the original data frame or resampling residuals from the
original model fit.
loess.boot(lo.object, R, rows = TRUE, new.xpts = NULL, ngrid = 100, weights = NULL)
lo.object |
A loess fit, produced by loess . |
R |
The number of bootstrap replicates. |
rows |
Should we resample rows? Setting rows to
FALSE indicates resampling of residuals. |
new.xpts |
Locations where new predictions are to be made. If
new.xpts is NULL , then an evenly spaced grid spanning
the range of X (containing ngrid points) is used. In either
case |
ngrid |
Number of grid points to use if new.xpts is
NULL . |
weights |
Resampling weights; a vector with length equal to the number of observations. |
The user can specify locations for new predictions through
new.xpts
or an evenly spaced grid will be used. In either
case, fitted values at each new location will be stored from each
bootstrap sample. These fitted values can be retrieved using either
the fitted
method or the samples
function.
Note that the loess
function has many parameters for the user
to set that can be difficult to reproduce in the bootstrap setting.
Right now, the user can only specify the span
argument to
loess
in the original fit.
An object of class "loess.simpleboot"
(which is a list)
containing the elements:
method |
Which method of bootstrapping was used (rows or residuals). |
boot.list |
A list containing values from each of the bootstrap samples. Currently, only residual sum of squares and fitted values are stored. |
orig.loess |
The original loess fit. |
new.xpts |
The locations where predictions were made (specified
in the original call to loess.boot ). |
Roger D. Peng
set.seed(1234) x <- runif(100) ## Simple sine function simulation y <- sin(2*pi*x) + .2 * rnorm(100) plot(x, y) ## Sine function with noise lo <- loess(y ~ x, span = .4) ## Bootstrap with resampling of rows lo.b <- loess.boot(lo, R = 500) ## Plot original fit with +/- 2 std. errors plot(lo.b) ## Plot all loess bootstrap fits plot(lo.b, all.lines = TRUE) ## Bootstrap with resampling residuals lo.b2 <- loess.boot(lo, R = 500, rows = FALSE) plot(lo.b2)