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: spatial
See also: SPKRsurfgls SPKRexpcov SPKRgaucov SPKRsphercov SPKRtrmat SPKRprmat SPKRsemat SPKRcorrelogram SPKRvariogram SPKRmultcontours

Quantlet: SPKRsurfls
Description: Fits a trend surface, i.e., a polynomial regression surface, by least squares.

Reference(s):

Link:
Usage: myres = SPKRsurfls (np, xmat)
Input:
np scalar - degree of the polynomial surface, an integer in the range 0..6
xmat n x 3 - matrix of locations (x_i, y_i) [columns 1 & 2] and observations z_i [column 3]
Output:
res list - consisting of components x, y, z, np, f, r, beta, wz, minx, maxx, type --
x n x 1 - same as xmat[,1]
y n x 1 - same as xmat[,2]
z n x 1 - same as xmat[,3]
np scalar - same as input value np
f matrix - internal use only
r matrix - internal use only
beta matrix - (np + 1)(np + 2) / 2 coefficients
wz matrix - internal use only
minx 1 x 3 - minimum of columns of xmat
maxx 1 x 3 - maximum of columns of xmat
type string - "trls"

Note:

Example:

; load the spatial statistics library

library ("spatial")

; read a spatial data set

topo = read("topo.dat")

; calculate a polynomial regression surface of order 2

myres = SPKRsurfls (2, topo)

Result:

A list consisting of input parameters, intermediate

results, and final results of a polynomial regression

surface. This list will be used in other spatial

statistics quantlets such as SPKRtrmat, SPKRcorrelogram,

or SPKRvariogram.


Library: spatial
See also: SPKRsurfgls SPKRexpcov SPKRgaucov SPKRsphercov SPKRtrmat SPKRprmat SPKRsemat SPKRcorrelogram SPKRvariogram SPKRmultcontours

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: Juergen Symanzik, 000725
(C) MD*TECH Method and Data Technologies, 21.9.2000