Nothing
# An example file fitting the FitzHugh-Nagumo equations to data in the
# new R Profiling Code. This will eventually be interfaced with the new R
# "Partially Observed Markov Process (pomp)" class of objects.
library(fda)
library(odesolve)
library(maxLik)
library(MASS)
library('Matrix')
library('SparseM')
source('../R/ProfileR.R')
source('../R/fhn.R')
source('../R/findif.ode.R')
source('../R/SSElik.R')
source('../R/SSEproc.R')
source('../R/makeid.R')
source('../R/makeexp.R')
source('../R/genlin.R')
source('../R/cvar.R')
source('../R/Multinorm.R')
source('../R/Cproc.R')
source('../R/exp.Cproc.R')
source('../R/multinorm.shortcut.R')
source('../R/sse.shortcut.R')
source('../R/inneropt.R')
###############################
#### Data Generation #######
###############################
t = seq(0,20,0.05)
pars = c(0.2,0.2,3)
names(pars) = c('a','b','c')
x0 = c(-1,1)
names(x0) = c('V','R')
fhn = make.fhn()
y = lsoda(x0,times=t,func=fhn$fn.ode,pars)
y = y[,2:3]
data = y + 0.05*array(rnorm(802),dim(y))
###############################
#### Basis Object #######
###############################
knots = seq(0,20,0.2)
norder = 3
nbasis = length(knots) + norder - 2
range = c(0,20)
bbasis = create.bspline.basis(range=range,nbasis=nbasis,
norder=norder,breaks=knots)
# Initial values for coefficients will be obtained by smoothing
fd.data = array(data,c(dim(data)[1],1,dim(data)[2]))
DEfd = data2fd(fd.data,t,bbasis,fdnames=list(NULL,NULL,c('V','R')) )
coefs = matrix(as.vector(DEfd$coefs),dim(DEfd$coefs)[1],dim(DEfd$coefs)[3])
colnames(coefs) = DEfd$fdnames[[3]]
###############################
#### Optimization Control ###
###############################
control=list() # Control parameters
control$trace = 0
control$maxit = 1000
control$maxtry = 10
control$reltol = 1e-6
control$meth = "BFGS"
control.in = control
control.in$reltol = 1e-12
control.out = control
control.out$trace = 2
control.in$print.level = 0
control.in$iterlim = 1000
#################################
### Initial Parameter Guesses ###
#################################
profile.obj = sse.setup(pars=pars,coefs=coefs,fn=make.fhn(),basisvals=bbasis,lambda=10000,times=t)
lik = profile.obj$lik
proc= profile.obj$proc
pres = ParsMatchOpt(pars,coefs,proc)
npars = pres$pars
#############################################################
### If We Only Observe One State, We Can Re-Smooth Others ###
#############################################################
tcoefs = coefs
tcoefs[,2] = 0
fres = FitMatchOpt(coefs=tcoefs,which=2,pars=pres$pars,proc)
ncoefs = fres$coefs
###############################
#### Parameter Optimization ###
###############################
spars = c(0.2,0.2,2) # Perturbed parameters
names(spars)=names(pars)
lambda = 10000
### SSE Shortcuts ####
Ires1 = Smooth.sse(make.fhn(),data,t,pars=spars,coefs,bbasis,lambda=lambda,in.meth='nlminb',control.in=control.in)
Ores1 = Profile.sse(make.fhn(),data=data,times=t,pars=spars,coefs=coefs,basisvals=bbasis,lambda=lambda,in.meth='nlminb',out.meth='nls',
control.in=control.in,control.out=control.out)
### SSE with ProfileErr ###
Ires2 = inneropt(data,times=t,pars,coefs,lik,proc,in.meth='nlminb',control.in)
Ores2 = outeropt(data=data,times=t,pars=pars,coefs=coefs,lik=lik,proc=proc,in.meth="nlminb",out.meth="nlminb",
control.in=control.in,control.out=control.out)
### Multinorm ####
var = c(1,0.01)
Ires3 = Smooth.multinorm(make.fhn(),data,t,pars=spars,coefs,bbasis,var=var,in.meth='nlminb',control.in=control.in)
Ores3 = Profile.multinorm(make.fhn(),data,t,pars=spars,coefs,bbasis,var=var,
out.meth='nlminb', in.meth='nlminb',control.in=control.in,control.out=control.out)
# Lets look at the result
DEfd = fd(Ores1$coefs,bbasis) # Data and reconstructed trajectory
par(mfrow=c(2,1))
plotfit.fd(data,t,DEfd)
traj = as.matrix(Ores1$proc$bvals$bvals%*%Ores1$coefs) # Look at how well the
colnames(traj) = Ores1$proc$more$names # derivative of the
dtraj = as.matrix(Ores1$proc$bvals$dbvals%*%Ores1$coefs) # trajectory fits the
ftraj = Ores1$proc$more$fn(t,traj,Ores1$pars) # right hand side.
matplot(dtraj,type='l',col=2)
matplot(ftraj,type='l',col=4,add=TRUE)
Profile.covariance(pars=Ores1$pars,times=t,data=data,coefs=Ores1$coefs,
lik=Ores1$lik,proc=Ores1$proc)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.