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
knots = seq(domain[1],domain[2],length.out = nknots)
nbasis = nknots + norder - 2
basis = create.bspline.basis(knots,nbasis,norder)
V = eval.penalty(basis,int2Lfd(2))
extra=list(lambda=exp(seq(-18,-12, length.out = 10)),gamma=10^(-8:-6))
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
}
slosfit = SLoS(Y=Y[1:n,1],(X[1:n,,1]),M=M,d=d,domain=domain,extra=extra)
predict(slosfit,(X[1:n,,1]))
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