sharpData (version 1.4)

sharpiteration: 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.

See Also

locpoly

Examples

# NOT RUN {
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)
# }