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

 Usage: m = gintest(code,t,y,h,g,loc{,opt}) Input: code string, specifying the code function implemented codes: noid, bipro, bilo t n x p matrix, the continuous predictor variables. y n x q matrix , the observed response variables h p 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 optional, a list with optional input. The macro "gplmopt" can be used to set up this parameter. The order of the list elements is not important. opt.tg ng x pg vector, a grid for continuous part. If tg is given, the nonparametric function will also 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: m n(ng) x p(pg) x q matrix, containing the marginal integration estimators

Example:
```library("gam")
randomize(1235)
n     = 100
p     = 2
t     = uniform(n,p)*2-1
g1    = 2*t[,1]
g2    = t[,2]^2
g2    = g2 - mean(g2)
m     = g1 + g2
y     = cdfn(m) .> uniform(n)    ; probit model
h     = #(1.7, 1.5)
g     = #(1.7, 1.5)
tg    = grid(-0.8,0.1,19)
opt   = gamopt("tg",tg~tg,"shf",1)
loc   = 1
code  = "bipro"
m     = gintest(code,t,y,h,g,loc,opt)
d1    = tg[,1]~m[,1]
d2    = tg[,2]~m[,2]
bild  = createdisplay(1,2)
show(bild,1,1,d1,t[,1]~g1)
show(bild,1,2,d2,t[,2]~g2)

```
Result:
```the marginal integration estimator of the additive
functions, see Linton and Haerdle (1996)
```

Author: S. Sperlich, W. Stockmeyer, W. Haerdle, 19970711 license MD*Tech
(C) MD*TECH Method and Data Technologies, 05.02.2006