| Library: | smoother |
| See also: | regestp lpregest lregxestp |
| Macro: | lregestp | |
| Description: | estimates a multivariate regression function using local polynomial kernel regression. The computation uses WARPing. |
Binning for local polynomials, Fan/Marron (1994)
WARPing method, W. Haerdle, "Smoothing Techniques with applications in S"
| Usage: | mh = lregestp(x {,h {,K {,d}}}) | |
| Input: | ||
| x | n x (p+1), the data. In the first p columns the independent, in the last column the dependent variable. | |
| h | scalar or p x 1 vector, bandwidth. If not given, 20% of the volume of x[,1:p] is used. | |
| K | string, kernel function on [-1,1]^p. If not given, the product Quartic kernel "qua" is used. | |
| d | scalar, discretization binwidth. d[i] must be smaller than h[i]. If not given, the minimum of h/3 and (max(x)-min(x))'/r, with r=100 for p=1, and r=(1000^(1/p)) for p>1 is used. | |
| Output: | ||
| mh | m x (p+1) matrix, the first p columns constitute a grid and the last column contains the regression estimate on that grid. | |
library("smoother")
library("plot")
;
x = 4.*pi.*(uniform(400,2)-0.5)
m = sum(cos(x),2)
e = uniform(400)-0.5
x = x~(m+e)
;
mh = regestp(x,2)
mh = setmask(mh, "surface","blue")
m = setmask(x[,1:2]~m,"black","cross","small")
plot(mh,m)
setgopt(plotdisplay,1,1,"title","ROTATE!")
The Local Linear regession estimate (blue) using Quartic kernel and bandwidth h=2 and the true regression function (thin black crosses) are pictured.
| Library: | smoother |
| See also: | regestp lpregest lregxestp |