ParsMatch: Estimate of Parameters from Smooth

In CollocInfer: Collocation Inference for Dynamic Systems

Estimate of Parameters from Smooth

Description

Objective function and derivatives to estimate parameters with a fixed smooth.

Usage

ParsMatchOpt(pars,coefs,proc,active=1:length(pars),meth='nlminb',control=list())

ParsMatchErr(pars,coefs,proc,active=1:length(pars),allpars,sgn=1)

ParsMatchDP(pars,coefs,proc,active=1:length(pars),allpars,sgn=1)

ParsMatchList(pars,coefs,proc,active=1:length(pars),allpars,sgn=1)

Arguments

pars	Initial values of parameters to be estimated processes.
coefs	Vector giving the current estimate of the coefficients in the spline.
proc	proc object defining the state process.
active	Incides indicating which parameters of allpar should be estimated; defaults to all of them.
allpars	Vector of all parameters, the assignment allpar[active]=pars is made initially.
sgn	Is the minimizing (1) or maximizing (0)?
meth	Optimization function currently one of 'nlminb', 'MaxNR', 'optim' or 'trust'.
control	Control object for optimization function.

Details

These routines fix the estimated states at the value given by coefs and estimate pars to maximize agreement between the fixed state and the objective given by the proc object.

A number of optimization routines have been implemented in FitMatchOpt, some experimentation is advised.

Value

ParsMatchOpt	A list containing: pars The entire parameter vector after optimization. res The output of the optimization routine.
ParsMatchErr	The value of the process likelihood at the current estimated states.
ParsMatchDP	The derivative fo ParsMatchErr with respect to pars[active].
ParsMatchList	A list with entries value and gradient given by the output of ParsMatchErr and ParsMatchDP respectively.

See Also

FitMatchErr, SplineCoefsErr, inneropt

Examples

data(FhNdata)

###############################
####  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(FhNtimes),nbasis=nbasis,
	norder=norder,breaks=knots)


# Initial values for coefficients will be obtained by smoothing

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



#################################
### Initial Parameter Guesses ###
#################################

profile.obj = LS.setup(pars=FhNpars,coefs=coefs,fn=make.fhn(),basisvals=bbasis,
    lambda=1000,times=FhNtimes)
lik = profile.obj$lik
proc= profile.obj$proc

pres = ParsMatchOpt(FhNpars,coefs,proc)

npars = pres$pars

CollocInfer documentation built on April 4, 2025, 2:21 a.m.