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: gam
See also: intestpl gintest gintestpl pcad gamfit

Macro: intest
Description: estimation of the univariate additive functions in a separable additive model using Nad.Watson, local linear or local quadratic

Usage: gest = intest(t,y,h,g,loc{,opt})
Input:
t n x p matrix , the observed explanatory variable where the directions of interest have to be the first p columns
y n x q matrix , the observed response variables
h p(pg) x 1 or 1 x 1 matrix , chosen bandwidth for the directions of interest
g p x 1 or 1 x 1 matrix , chosen bandwidth for the directions not of interest
loc dummy, for loc=0 local constant (Nad. Wats.), for loc=1 local linear and for loc=2 local quadratic estimator will be used
opt.tg ng x pg vector, a grid for continuous part. If tg is given, the nonparametric function will be computed on this grid.
opt.shf integer, (show-how-far) if exists and =1, an output is produced which indicates how the iteration is going on (additive function / point of estimation / number of iteration).
Output:
gest n(ng) x pp x q matrix, containing the marginal integration estimates of the additive components in the first p columns, the derivatives in the followings, so pp = pg*(loc+1)

Example:
library("gam")
randomize(1234)
t     = uniform(50,2)*2-1
g1    = 2*t[,1]
g2    = t[,2]^2 
g2    = g2 - mean(g2)
y     = g1 + g2  + normal(50,1) * sqrt(0.25)
h     = #(1.2, 1.0)          
g     = #(1.4, 1.2)     
loc   = 1
gest  = intest(t,y,h,g,loc)
gest
bild  = createdisplay(1,2)
dat11 = t[,1]~g1
dat12 = t[,1]~gest[,1]
dat21 = t[,2]~g2
dat22 = t[,2]~gest[,2]
setmaskp(dat12,4,4,8)
setmaskp(dat22,4,4,8)
show(bild,1,1,dat11,dat12)
show(bild,1,2,dat21,dat22)
Result:
the marginal integration estimates of the additive 
functions and its derivatives, using local polynomials,
see Severance-Lossin & Sperlich (1995)

Library: gam
See also: intestpl gintest gintestpl pcad gamfit

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: Sperlich & Stockmeyer 970509
(C) MD*TECH Method and Data Technologies, 28.6.1999