Nothing
FhNtimes = seq(0,1000,25)
FhNpars = c(0.7,0.8,0.008)
names(FhNpars) = c('a','b','c')
knots = seq(0,1000,2)
norder = 3
nbasis = length(knots) + norder - 2
bbasis = create.bspline.basis(range=range(FhNtimes),nbasis=nbasis,
norder=norder,breaks=knots)
coefs = matrix(0,bbasis$nbasis,2)
colnames(coefs) = c('V','R')
profile.obj = LS.setup(pars=FhNpars,coefs=coefs,fn=make.fhn.input()$fn,basisvals=bbasis,lambda=1e8,times=FhNtimes,
more = list(infn=function(t){return(0.2*(t>500))}))
lik = profile.obj$lik
proc= profile.obj$proc
# Let's start off with a smooth
FhNdata = DE2x(c(-1,1),FhNtimes,FhNpars,proc)
matplot(FhNtimes,FhNdata,type='b')
fd.obj = smooth.basis(FhNtimes,FhNdata,fdPar(bbasis,1,0.01))
plotfit.fd(FhNdata,FhNtimes,fd.obj$fd)
coefs = fd.obj$fd$coefs
# Now we can look at doing the inner optimization
resI = inneropt(FhNdata,FhNtimes,FhNpars,coefs,lik,proc,in.meth="SplineEst")
traj = proc$bvals$bvals%*%resI$coefs
matplot(proc$more$qpts,traj,type='l')
matplot(FhNtimes,FhNdata,add=TRUE)
# And the outer optimization
resO = outeropt(FhNdata,FhNtimes,FhNpars,coefs,lik,proc)
traj = proc$bvals$bvals%*%resO$coefs
matplot(proc$more$qpts,traj,type='l')
matplot(FhNtimes,FhNdata,add=TRUE)
dtraj = proc$bvals$dbvals%*%resO$coefs
ftraj = proc$more$fn(proc$more$qpts,traj,FhNpars,proc$more$more)
matplot(proc$more$qpts,dtraj,type='l',lty=1)
matplot(proc$more$qpts,ftraj,type='l',lty=2,add=TRUE)
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