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# Load some things in
library('CollocInfer')
# The Chemostat equations represent a four-species Chemostat plus the resource
# of Nitrogen. There are two species of Algae with varying defenses against
# Rotifers. The Rotifers themselves are divided into two class -- breeding
# and senescent, although these two are very tightly coupled.
#
# A full description of these equations can be found in the user manual.
# The five state variables for the equations are
#
# N - nitrogen content in the Chemostat
# C1 - Algal type 1
# C2 - Algal type 2
# B - Breeding Rotifers
# S - Senescent Rotifers
#
# The system has 16 parameters. Notable features include that only the sums
# C1+C2 and B+S can be observed. Further, an unknown fraction of each is counted
# at each time. This requires us to set up a model for the observation process
# along with the ODE.
# First we load up some data
data(ChemoData)
# The first two of these parameters give the fractions of Algae and Rotifers
# that are counted. The remaining parameters are all positive and using
# their logged values is helpful.
logpars=c(ChemoPars[1:2],log(ChemoPars[3:16]))
# Parameters 'p1' and 'p2' represent relative palatability of the two algal
# clones, as such only one can be estimated and we fix p2 = 0.
active = c(1:2,5,7:16)
# We need some basis functions
rr = range(ChemoTime)
knots = seq(rr[1],rr[2],by=0.5)
bbasis = create.bspline.basis(rr,norder=4,breaks=knots)
# smooth obtained from generating some ODE solutions
y0 = log(c(2,0.1,0.4,0.2,0.1))
names(y0) = ChemoVarnames
odetraj = lsoda(y0,ChemoTime,func=chemo.ode,logpars)
DEfd = smooth.basis(ChemoTime,odetraj[,2:6],fdPar(bbasis,int2Lfd(2),1e-6))
coefs0 = DEfd$fd$coef
# We need a measurement model
ChemoMeas = function(t,x,p,more)
{
return( cbind( log(exp(x[,'C1'])+exp(x[,'C2'])) + log(p['a1']), log(exp(x[,'B'])+exp(x[,'S']))+log(p['a2']) ) )
}
# From here we can employ the usual setup functions
out = LS.setup(pars = logpars,coefs = coefs0,fn=chemo.fun,
basisvals=bbasis,lambda=c(1e3,2e2,2e2,1e3,1e3)/2,data=ChemoData,times=ChemoTime,
posproc=TRUE,names=ChemoVarnames,likfn=ChemoMeas)
lik = out$lik
proc = out$proc
# Now, with parameters fixed, we'll estiamte coefficients.
control.in = list()
control.in$trace = 2
control.in$maxit = 1000
control.in$reltol = 1e-6
res1 = inneropt(coefs=coefs0, pars=logpars, times=ChemoTime, data=log(ChemoData),
lik=lik, proc=proc, in.meth='optim', control.in=control.in)
# We'll plot agreement with the data
out1 = CollocInferPlots(res1$coefs,logpars,lik,proc,ChemoTime,log(ChemoData))
# And conduct the outer optimization
res2 = outeropt(coefs=coefs0, pars=logpars, times=ChemoTime, data=log(ChemoData),
lik=lik, proc=proc, in.meth='optim', control.in=control.in)
# along with diagnostic plots
out2 = CollocInferPlots(res2$coefs,res2$pars,lik,proc,ChemoTime,log(ChemoData))
###############################################################
# The following provides a manual setup that employs the builtin genin
# functions for measurement or process models that involve linear combinations
# of variables.
mids = c(min(knots),(knots[1:(length(knots)-1)] + 0.25),max(knots))
bvals.obs = eval.basis(ChemoTime,bbasis)
bvals.proc = list(bvals = eval.basis(mids,bbasis),
dbvals = eval.basis(mids,bbasis,1));
# We'll choose a fairly large value of lambda.
lambda = c(1e3,2e2,2e2,1e3,1e3)
# We can now set up the proc object. We will want to take a log transformation
# of the state here for numerical stability. In general it is better to do
# finite differencing AFTER the log transformation rather than before it.
proc = make.SSEproc() # Sum of squared errors
proc$bvals = bvals.proc # Basis values
proc$more = make.findif.ode() # Finite differencing
proc$more$qpts = mids # Quadrature points
proc$more$weights = rep(1,5)*lambda # Quadrature weights (including lambda)
proc$more$names = ChemoVarnames # Variable names
proc$more$parnames = ChemoParnames # Parameter names
proc$more$more = list(fn=make.logtrans()$fn,eps=1e-8) # Log transform
proc$more$more$more = list(fn=chemo.fun) # ODE function
# For the lik object we need to both represent the linear combination transform
# and we need to model the observation process.
# First to represent the observation process, we can use the genlin
# functions. These produce a linear combination of the the states
# (they can be used in proc objects for linear systems, too).
temp.lik = make.SSElik()
temp.lik$more = make.genlin()
# Genlin requires a more object with two elements. The 'mat' element
# gives a template for the matrix defining the linear combination. This is
# all zeros 2x5 in our case for the two observations from five states.
# The 'sub' element specifies which elements of the parameters should be
# substituted into the mat element. 'sub' should be a kx3 matrix, each
# row defines the row (1) and column (2) of 'mat' to use and the element
# of the parameter vector (3) to add to it.
temp.lik$more$more = list(mat=matrix(0,2,5,byrow=TRUE),
sub = matrix(c(1,2,1,1,3,1,2,4,2,2,5,2),4,3,byrow=TRUE))
#temp.lik$more$weights = matrix(c(100,1),length(ChemoTime),2)
temp.lik$more$weights = c(100,1)
# Finally, we tell CollocInfer that the trajectories are represented on
# the log scale and must be exponentiated before comparing them to the data.
lik = make.logstate.lik()
lik$more = temp.lik
lik$bvals = bvals.obs
# Now lets try running this
# Because we don't have direct observations of any state, we'll use a starting
# smooth obtained from generating some ODE solutions
y0 = log(c(2,0.1,0.4,0.2,0.1))
names(y0) = ChemoVarnames
odetraj = lsoda(y0,ChemoTime,func=chemo.ode,logpars)
DEfd = smooth.basis(ChemoTime,odetraj[,2:6],fdPar(bbasis,int2Lfd(2),1e-6))
coefs0 = DEfd$fd$coef
# Now, with parameters fixed, we'll estiamte coefficients.
control.in = list()
control.in$trace = 2
control.in$maxit = 1000
control.in$reltol = 1e-6
res = inneropt(coefs=coefs0, pars=logpars, times=ChemoTime, data=ChemoData,
lik=lik, proc=proc, in.meth='optim', control.in=control.in)
# We'll for the trajectory and also the appropriate sum of exponentiated
# states to compare to the data.
coefs1 = matrix(res$coefs,dim(coefs0))
traj = lik$bvals %*% coefs1
obstraj = lik$more$more$fn(ChemoTime,exp(traj),logpars,lik$more$more$more)
# Plot these against the data
X11()
par(mfrow=c(2,1))
plot(obstraj[,1],type='l',ylab='Chlamy',xlab='',cex.lab=1.5,cex.axis=1.5)
points(ChemoData[,1])
plot(obstraj[,2],type='l',ylab='Brachionus',xlab='days',cex.lab=1.5,cex.axis=1.5)
points(ChemoData[,2])
# Now we can continue with the outer optimization
res2 = outeropt(pars=logpars,times=ChemoTime,data=ChemoData,coef=coefs1,
lik=lik,proc=proc,active=active,in.meth='optim',out.meth='nlminb')
# We'll extract the resulting parameters and coefficients.
npars = res2$pars
C = as.matrix(res2$coefs,dim(C))
# And obtain an estimated trajectory and the exponentiated sum to comprare
# to the data.
traj = lik$bvals%*%C
ptraj = lik$more$more$fn(ChemoTime,exp(traj),npars,lik$more$more$more)
# Lets have a look at how much we changed our parameters on the original
# scale.
new.pars = npars
new.pars[3:16] = exp(new.pars[3:16])
print(ChemoPars)
print(new.pars)
print(new.pars/ChemoPars)
# Now we can produce a set of diagnostic plots.
# Firstly, a representation of the trajectory compared to the data.
X11()
par(mfrow=c(2,1))
plot(ChemoTime,ptraj[,1],type='l',ylab='Chlamy',xlab='',cex.lab=1.5)
points(ChemoTime,ChemoData[,1])
plot(ChemoTime,ptraj[,2],type='l',ylab='Brachionus',xlab='days',cex.lab=1.5)
points(ChemoTime,ChemoData[,2])
# Now we'll plot both the derivative of the trajectory and the value of the
# differential equation right hand side at each point. This represents the
# fit to the model.
traj2 = proc$bvals$bvals%*%C
dtraj2 = proc$bvals$dbvals%*%C
colnames(traj2) = ChemoVarnames
ftraj2 = proc$more$fn(proc2$more$qpts,traj2,npars,proc$more$more)
X11()
par(mfrow=c(5,1),mai=c(0.3,0.6,0.1,0.1))
for(i in 1:5){
plot(mids,dtraj2[,i],type='l',xlab='',ylab=ChemoVarnames[i],
cex.lab=1.5,ylim=c(-0.5,0.5))
lines(mids,ftraj2[,i],col=2,lty=2)
abline(h=0)
}
legend('topleft',legend=c('Smooth','Model'),lty=1:2,col=1:2,cex=1.2)
# Solving the differential equation from the estiamted initial condition
# of the trajectory allows us to compare the qualitative behavior of
# our estimate to that of the differential equation.
y0 = traj[1,]
names(y0) = ChemoVarnames
odetraj = lsoda(y0,ChemoTime,func=chemo.ode,parms=npars)
X11()
par(mfrow=c(2,1))
matplot(ChemoTime,traj,col=1,type='l',lwd=3,cex.lab=1.5,cex.axis=1.5,
ylab='',cex.main=1.5,main='Reconstructed Trajectories')
legend(x='topright',legend=ChemoVarnames,lwd=3,col=1,lty=1:5)
matplot(ChemoTime,odetraj[,2:6],col=1,type='l',lwd=3,cex.axis=1.5,cex.lab=1.5,
ylab='',cex.main=1.5,main='ODE Solution')
# We can also compare the pattern of observations predicted by the differential
# equation and that estimated by our methods.
otraj = lik$more$more$fn(ChemoTime,exp(odetraj[,2:6]),npars,lik$more$more$more)
X11()
par(mfrow=c(2,1))
matplot(ChemoTime,ptraj,type='l',lwd=2,xlab='days',cex.lab=1.5,ylab='',
cex.axis=1.5,cex.main=1.5,main='Predicted Observations -- Smooth')
matplot(ChemoTime,ChemoData,add=TRUE,pch = c(1,2))
legend('topright',legend=c('Algae','Rotifers'),pch=1:2,col=1:2)
matplot(ChemoTime,otraj,type='l',lwd=2,xlab='days',cex.lab=1.5,ylab='',
cex.axis=1.5,cex.main=1.5,main='Predicted Observations -- ODE')
legend('topright',legend=c('Algae','Rotifers'),lty=1:2,col=1:2,lwd=2)
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