| Library: | gam |
| See also: | intest pcad |
| Quantlet: | intest1 | |
| Description: | estimation of the univariate additive functions in a separable additive model using Nad.Wat. |
| Usage: | gest = intest1(x,y,xg,h,g) | |
| Input: | ||
| x | n x d matrix , the observed explanatory variable where the directions of interest have to be the first q columns | |
| y | n x p matrix , the observed response variables | |
| xg | m x q matrix , the grid with m points in each of the q directions of interest | |
| h | q x 1 or 1 x 1 matrix , chosen bandwidth for the directions of interest | |
| g | d x 1 or 1 x 1 matrix , chosen bandwidth for the directions not of interest | |
| Output: | ||
| gest | m x q x p matrix, containing the marginal integration estimators | |
library("gam")
n = 150
x = uniform(n,4)*4-2
g1 = 2*x[,1]
g2 = x[,2]^2 - 4/3
g3 = exp(x[,3])
g4 = sin(1.5*x[,4])
eps = normal(n,1) * sqrt(0.5)
y = g1 + g2 + g3 + g4 + eps
xg = grid(-1.8,0.2,19)
xg = xg~xg
h = #(1.0, 0.75) ; we are interested in
g = #(1.3, 1.0, 1.5, 1.5) ; the shape of g1, g2
gest = intest1(x,y,xg,h,g)
bild = createdisplay(1,2)
dat11 = x[,1]~g1
dat12 = xg[,1]~gest[,1]
dat21 = x[,2]~g2
dat22 = xg[,2]~gest[,2]
setmaskp(dat12,4,4,8)
setmaskp(dat22,4,4,8)
setmaskl(dat12,(1:rows(dat12))',4,1,1)
setmaskl(dat22,(1:rows(dat22))',4,1,1)
show(bild,1,1,dat11,dat12)
show(bild,1,2,dat21,dat22)
the marginal integration estimator of the additive functions, using a multidimensional Nadaraya Watson see Tjostheim and Auestad, "Nonparametric Identifi- cation of Nonlinear Time Series: Projections", JASA, (1994)