Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: smoother
See also: lpregest sker locpol

Macro: lpregxest
Description: estimates a univariate regression function using local polynomial kernel regression with Quartic kernel.

Reference(s):

Usage: y = lpregxest (x,h {,p {,v}})
Input:
x n x 2, the data. In the first column the independent, in the second column the dependent variable.
h scalar, bandwidth. If not given, the rule of thumb bandwidth computed by lpregrot is used.
p integer, order of polynomial. If not given, p=1 (local linear) is used. p=0 yields the Nadaraya-Watson estimator. p=2 (local quadratic) is the highest possible order.
v m x 1, values of the independent variable on which to compute the regression. If not given, x is used.
Output:
mh n x 2 or m x 2 matrix, the first column is the sorted first column of x or the sorted v, the second column contains the regression estimate on the values of the first column.

Note:

Example:
library("smoother") 
library("plot")
;
x = 4.*pi.*(uniform(200)-0.5)   ; independent variable
m = cos(x)                      ; true function
e = uniform(200)-0.5            ; error term             
x = x~(m+e)                             
;
mh = lpregxest(x,1)                ; estimate function
;
mh = setmask(mh, "line","blue")
m  = setmask(sort(x[,1]~m) , "line","black","thin")
plot(x,mh,m)                                         
Result:
The Nadaraya-Watson regession estimate (blue) using   
Quartic kernel and bandwidth h=1 and the true 
regression function (thin black) are pictured.

Library: smoother
See also: lpregest sker locpol

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Author: Marlene Mueller, 980413
(C) MD*TECH Method and Data Technologies, 17.8.2000