Monolpoly: Monotonized Local Regression

In sharpData: Data Sharpening

Description

Local constant and local linear regression are applied to bivariate data. The response is ‘sharpened’ or perturbed in a way to render a monotonically increasing curve estimate.

Usage

Monolpoly(x, y, h, d=1,  xgrid, numgrid = 401, ...)

Arguments

x	a vector of explanatory variable observations
y	binary vector of responses
h	bandwidth
d	degree, can be either 0 or 1
xgrid	gridpoints on x-axis where monotonicity constraint is enforced
numgrid	number of equally-spaced gridpoints (if xgrid not specified)
...	other arguments for locpoly

Details

Data are perturbed the smallest possible L2 distance subject to the constraint that the local linear estimate is monotonically increasing.

Value

x	locations of function estimate evaluations
y	function estimate evaluations (sharpened - monotonized)
ysharp	sharpened responses

Author(s)

W.J.Braun

References

Braun, W.J. and Hall, P., Data Sharpening for Nonparametric Estimation Subject to Constraints, Journal of Computational and Graphical Statistics, 2001

Examples

gridpts <- seq(1, 10, length=101)
x <- seq(1, 10, length=51)
p <- exp(-1 + .2*x)/(1 + exp(-1 + .2*x))
y <- rbinom(51, 1, p)
plot(x, y)
lines(Monolpoly(x, y, h=0.6, xgrid=gridpts))
##
plot(faithful)
with(faithful, 
lines(Monolpoly(eruptions, waiting, h=0.1, d=1, 
range=c(1.55,5.15))))

sharpData documentation built on March 30, 2021, 9:06 a.m.