nlr (version 0.1-3)

dfr.corrts: Derivative free Two Stage estimate

Description

Derivative free two stage estimate for nonlinear regreession model with autocorrelated error.

Usage

dfr.corrts(formula, data, start = getInitial(formula, data), 
control = nlr.control(tolerance = 0.001, minlanda = 1/2^10, 
maxiter = 25 * length(start)), correlation = 1, ...)

Arguments

formula

nl.form object of the nonlinear function model. See nl.form object.

data

list of data with the response and predictor as name of variable.

start

list of starting value parameter, name of parameters must be represented as names of variable in the list.

control

nlr.control object, include tolerance, maxiter,... see nlr.control.

correlation

correlation structure, at the moment parameter of AR(p) process.

…

any argument pass to formula

Value

fited

nl.fitt.gn object generated by nlsnm function.

tm

fitted time series model for residuals.

Details

In first stage nonlinear regression parameter estimate and in second stage autocorrelation structure estimate and finally the generalized least square estimates the function model parameters.

In this function all stages compute by derivative free methods, which minimization methods uses Nelder-Mead method.

References

Riazoshams, H., Midi, H., Sharipov, O. S.H, (2010). The Performance of Robust Two Stage Estimator in Nonlinear Regression with autocorrelated Error, Communications in Statistics - Simulation and Computation, 39: 1251-1268.

See Also

nl.robcorrts, nlsqr.gn, nl.fitt.gn, nlr.control, nlsnm

Examples

# NOT RUN {
# The direct call of nlr call dfr.corrts.
p1<- 8.06e+10
p2<- 1e11
p3<-1970
p4=6
chstart2 <- list(p1=p1,p2=p2,p3=p3,p4=p4)
irandt<-nlr::trade.ir
dfrir<- dfr.corrts (nlrobj5[[4]],data=list(xr=irandt[,1],yr=irandt[,2]),start=chstart2,
control=nlr.control(trace=TRUE),correlation = 2)
dfrir$fited$parameters
# }