inneropt.DDE: Inner optimization for estmating coefficients given...

In gpDDE: General Profiling Method for Delay Differential Equation

Description

Estmates spline coefficients given parameters for DDE models.

Usage

inneropt.DDE(data, times, pars, beta, coefs, lik, proc, in.meth = "nlminb",
  control.in = list(), basisvals, fdobj0)

Arguments

data	Matrix of observed data values.
times	Vector observation times for the data.
pars	Initial values of parameters to be estimated processes.
beta	Initial values of the contribution of lags for the delay.
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', 'optim', or 'trustOptim'.
control.in	Control object for inner optimization function.
basisvals	Values of the collocation basis to be used. This should be a basis object from the fda package.
fdobj0	A functional data object fitted with the history part of the data.

Details

This minimizes the objective function for DDE models 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.

Author(s)

Ziqian Zhou

gpDDE documentation built on May 2, 2019, 1:09 p.m.