inneropt {CollocInfer}

Inner Optimization Functions

Description

Estmates coefficients given parameters.

Usage

inneropt(data,times,pars,coefs,lik,proc,in.meth='nlminb',control.in=list())

Arguments

data

Matrix of observed data values.

times

Vector observation times for the data.

pars

Initial values of parameters to be estimated processes.

coefs

Vector giving the current estimate of the coefficients in the spline.

lik

lik object defining the observation process.

proc

proc object defining the state process.

in.meth

Inner optimization function currently one of 'nlminb', 'maxNR', 'optim', 'trust' or 'SplineEst'. The last calls SplineEst.NewtRaph. This is fast but has poor convergence.

control.in

Control object for inner optimization function.

Details

This minimizes the objective function defined by the addition of the lik and proc objectives with respect to the coefficients. A number of generic optimization routines can be used and some experimentation is recommended.

Value

A list with elements

coefs

A matrix giving he optimized coefficients.

res

The results of the inner optimization function.

See Also

outeropt, Smooth.LS,LS.setup, multinorm.setup, SplineCoefsErr

Examples

## Not run: 
# FitzHugh-Nagumo Equations

data(FhNdata)   # Some data

knots = seq(0,20,0.2)         # Create a basis
norder = 3
nbasis = length(knots) + norder - 2
range = c(0,20)

bbasis = create.bspline.basis(range=range(FhNtimes),nbasis=nbasis,
                                    norder=norder,breaks=knots)

lambda = 10000               # Penalty value

DEfd = smooth.basis(FhNtimes,FhNdata,fdPar(bbasis,1,0.5))   # Smooth to estimate
                                                            # coefficients first
coefs = DEfd$fd$coefs
colnames(coefs) = FhNvarnames

profile.obj = LS.setup(pars=FhNpars,coefs=coefs,fn=make.fhn(),
                        basisvals=bbasis,lambda=lambda,times=FhNtimes)

lik = profile.obj$lik
proc= profile.obj$proc

res = inneropt(FhNdata,times=FhNtimes,FhNpars,coefs,lik,proc,in.meth='nlminb')

plot(fd(res$coefs,bbasis))

## End(Not run)