| sharpiteration {sharpData} | R Documentation |
Iterated Data Sharpening for Local Polynomial Regression
Description
Calculation of sharpened responses for bias reduction in function and estimation, assuming a gaussian kernel is used in bivariate scatterplot smoothing.
Usage
sharpiteration(x, y, deg, h, nsteps, na.rm, ...)
Arguments
x |
a numeric vector containing the predictor variable values. |
y |
a numeric vector containing the response variable values. |
deg |
a numeric vector containing the local polynomial degree used. |
h |
a numeric vector containing the (scalar) bandwidth. |
nsteps |
a numeric vector containing the number of iteration steps. |
na.rm |
a logical value indicating whether to remove missing values from fitted vectors |
... |
additional arguments to locpoly |
Value
a list with elements containing the sharpened (i.e. perturbed) response values, ready for input into a local polynomial regression estimator. The ith list element corresponds to i steps of data sharpening.
Author(s)
W.J. Braun
See Also
locpoly
Examples
speed <- MPG[, 1]
mpg <- MPG[, 2]
h <- dpill(speed, mpg)
mpgSharp <- sharpiteration(speed, mpg, 1, h, 2)
mpg.lS <- locpoly(speed, mpgSharp[[2]], bandwidth=h, degree=1)
mpg.lX <- locpoly(speed, mpg, bandwidth=h, degree=1)
plot(mpg ~ speed)
lines(mpg.lX) # unsharpened function estimation
lines(mpg.lS, col=2, lty=2) # sharpened function estimation (2 steps)