predict.ngr {PFLR}

Predict Method for ngr Objects

Description

Predicted values based on "ngr" class objects.

Usage

## S3 method for class 'ngr'
predict(object, Xnew, ...)

Arguments

object

An object of class "ngr".

Xnew

New covariate matrix for prediction, should be dense, centred.

...

Not applicable

Value

Estimated Y hat value.

Examples

library(fda)
betaind = 1
snr  = 2
nsim = 1
n    = 50
p    = 21
Y = array(NA,c(n,nsim))
X = array(NA,c(n,p,nsim))
domain = c(0,1)

M = 20 
d = 3
norder   = d+1
nknots   = M+1
tobs = seq(domain[1],domain[2],length.out = p)
knots    = seq(domain[1],domain[2],length.out = nknots)
nbasis   = nknots + norder - 2
basis    = create.bspline.basis(knots,nbasis,norder)
basismat = eval.basis(tobs, basis) 
h = (domain[2]-domain[1])/M
cef = c(1, rep(c(4,2), (M-2)/2), 4, 1)

V = eval.penalty(basis,int2Lfd(2))
alphaPS = 10^(-(10:3))
kappa   = 10^(-(8:7))
tau     = exp(seq(-35,-28,len=20))
gamma   = 0.5


for(itersim in 1:nsim)
{
dat = ngr.data.generator.bsplines(n=n,nknots=64,norder=4,p=p,domain=domain,snr=snr,betaind=betaind)
Y[,itersim]  = dat$Y
X[,,itersim] = dat$X
}

ngrfit = ngr(Y=Y[1:n,1],X=(X[1:n,,1]),M,d,domain,extra= list(alphaPS=alphaPS, kappa=kappa, tau=tau))
predict(ngrfit,X[1:n,,1])