nlr (version 0.1-3)

dfr.hetroLS: Derivative free CLSME.

Description

Derivative free Classic Least square based Multi Stage Estimate (CLSME) for heteroscedastic error case.

Usage

dfr.hetroLS(formula, data, start = getInitial(formula, data), control = nlr.control(
tolerance = 1e-04, minlanda = 1/2^10,maxiter = 25 * length(start)), varmodel, 
tau = getInitial(varmodel, vdata), ...)

Arguments

formula

nl.form object of the nonlinear function model.

data

list of data include responce and predictor.

start

list of parameter values of nonlinear model function (\(\theta\). in \(f(x,\theta)\)).

control

list of nlr.control for controling convergence criterions.

varmodel

nl.fomr object of variance function model for heteroscedastic variance.

tau

list of initial values for variance model function varmodel argument.

…

extra arguments to nonlinear regression model, heteroscedastic variance function, robust loss function or its tuning constants.

Value

generalized fitt object nl.fitt.gn. The hetro slot include parameter estimate and other information of fitt for heteroscedastic variance model.

(parameters

nonlinear regression parameter estimate of \(\theta\).

correlation

of fited model.

form

nl.form object of called nonlinear regression model.

response

computed response.

predictor

computed (right side of formula) at estimated parameter with gradient and hessian attributes.

curvature

list of curvatures, see curvature function.

history

matrix of convergence history, collumns include: convergence index, parameters, minimized objective function, convergence criterion values, or other values. These values will be used in plot function in ploting history.

method

fittmethod object of method used for fitt.

data

list of called data.

sourcefnc

Object of class "callorNULL" source function called for fitt.

Fault

Fault object of error, if no error Fault number = 0 will return back.

vm

covariance matrix, diagonal of variance model predicted values.

rm

cholesky decomposition of vm.

gresponse

transformed of response by rm, include gradinet and hessian attributes.

gpredictor

transformed of predictor by rm, include gradinet and hessian attributes.

hetro

nl.fitt object of fited variance odel:

• parametersestimate of variance parameter \(\tau\)

• formnl.form object of called varmodel.

• predictorvariance model computed at estimated parameter, \(H(x;\hat\tau)\)

• responsesample variance computed used as response variable.

• historymatrix of convergence history, collumns include: convergence index, parameters, minimized objective function, convergence criterion values, or other values.

• methodfittmethod object of method used for fitt.

• dataresponse (\(z_i\)) and predictor t variable values, used to computing the variance model.

• sourcefncObject of class "callorNULL" source function called for fitt.

• FaultFault object of error, if no error Fault number = 0 will return back.

Details

Least square based estimate for nonlinear regression with hetroscedastic error when variance is a general function of unkown parameters.

References

Riazoshams, H. (2012), Robustifying the Least Squares estimate of parameters of variance model function in nonlinear regression with heteroscedastic variance, Poster Presentation, Royal Statistical Society Conference (RSS) 2012, Telford, UK.

See Also

fittmethod, nl.form, nl.fitt, nl.fitt.gn, nl.hetroLS, nlr.control

Examples

# NOT RUN {
ntpstart=list(p1=.12,p2=6,p3=1,p4=33)
ntpstarttau=list(tau1=-.66,tau2=2,tau3=.04)
datalist=list(xr=ntp$dm.k,yr=ntp$cm.k)
htls<- dfr.hetroLS(formula=nlrobj1[[15]], data=datalist, start= ntpstart,tau=ntpstarttau,
varmodel=nlrobjvarmdls3[[2]],control=nlr.control(tolerance=1e-8))
htls$parameters
# }