Library: | gam |
See also: | intest intestpl gintestpl gamfit pcad |
Quantlet: | gintest | |
Description: | estimation of the univariate additive functions in a separable generalized additive model using Nad.Watson, local linear or local quadratic |
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 |
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] setmaskp(d1,4,4,8) setmaskp(d2,4,4,8) bild = createdisplay(1,2) show(bild,1,1,d1,t[,1]~g1) show(bild,1,2,d2,t[,2]~g2)
the marginal integration estimator of the additive functions, see Linton and Haerdle (1996)